System and method for controlling personalized smart lighting based on user recognition

Abstract

A device according to one embodiment recognizes a user’s access to a place where lighting is installed, verifies user identification information corresponding to the user, obtains lighting control information matched with the user identification information, and controls the lighting according to the lighting control information.

Description

Technology Field

[0001] The present invention relates to a personalized smart lighting control system that identifies a user in real time based on the user's biometric information and automatically verifies lighting control information matched to the individual to adjust color temperature, brightness, and color. Background Technology

[0002] Existing lighting control technology has primarily evolved to automatically control lighting based on general environmental conditions, such as time of day, ambient brightness, and human detection.

[0003] While these systems contributed to improving energy efficiency in offices, homes, and commercial facilities, they had limitations in that they could not be finely adjusted according to the preferences or circumstances of individual users.

[0004] For example, even when multiple users take turns using the same space, the brightness, color temperature, and lighting patterns remain constant and fail to reflect individual preferences.

[0005] Furthermore, existing technology was limited to merely 'detecting' users, lacking the ability to identify the detected target or control lighting by linking with the user's preferred lighting information.

[0006] Recently, with the spread of smart home technology, lighting systems have become capable of being linked with user terminals, biometric sensors, location information, etc., through a network, leading to an increasing demand for technology that recognizes and identifies user access and provides a lighting environment optimized for that person.

[0007] Accordingly, there is a need for technology that implements a personalized lighting environment by automatically retrieving pre-stored lighting control information based on user identification data and controlling brightness, color, color temperature, etc., in real time.

[0008] Such technology is gaining importance as it can go beyond simple energy saving to contribute to creating a human-centered lighting environment that promotes psychological stability, improved concentration, and relaxation for users. Prior art literature

[0009] Korean Registered Patent No. 10-1651579 (Published Aug. 29, 2016) Korean Registered Patent No. 10-1769409 (Published Aug. 21, 2017) Korean Registered Patent No. 10-2596220 (Published Oct. 31, 2023) Korean Registered Patent No. 10-2781832 (Published Mar. 18, 2025) The problem to be solved

[0010] The embodiments aim to control lighting in a user-customized manner.

[0011] The embodiments aim to recognize when a user approaches a space where lighting is installed.

[0012] The embodiments aim to identify the lighting color, color temperature, and brightness preferred by the user and generate lighting control information for the user accordingly.

[0013] The embodiments aim to identify appropriate lighting control information when multiple users share the same lighting.

[0014] The objectives of the present invention are not limited to those mentioned above, and other unmentioned objectives will be clearly understood from the description below. means of solving the problem

[0015] According to one embodiment, a user recognition-based personalized smart lighting control method comprises: a step of recognizing a user’s access to a place where lighting is installed; a step of verifying user identification information corresponding to the user; a step of obtaining lighting control information matched with the user identification information; and a step of controlling the lighting according to the lighting control information.

[0016] The step of recognizing the user's access includes at least one of the following: an operation of recognizing the user's access based on the user's terminal being included within the communication range of the place where the light is installed; an operation of recognizing the user's access based on receiving that a wireless signal has been exchanged between the light and the user's terminal; and an operation of recognizing the user's access based on the recognition of at least one biometric information among the user's voice, fingerprint, or face through a sensor provided at the place where the light is installed.

[0017] The step of obtaining lighting control information matched with the user identification information comprises, based on the user identification information, verifying the time during which the lighting has operated in match with the user; verifying whether the time during which the lighting has operated is longer than a preset target time; if it is confirmed that the time during which the lighting has operated is longer than the target time, analyzing the number of lighting control changes, the lighting control change cycle, the deviation of the control interval, or the pattern of the timing of the occurrence of the lighting control command to verify whether the lighting control is changed periodically by time period; if it is confirmed that the lighting control is changed periodically by time period, generating lighting control information including the most frequently used color, color temperature, and brightness for the time period including the current time point; if it is confirmed that the lighting control is not changed periodically by time period, generating lighting control information including the color, color temperature, and brightness for which the lighting has been maintained for the longest time; and if it is confirmed that the time during which the lighting has operated is not longer than the target time, verifying the age, gender, and occupation of the user through the user identification information to identify a group of similar users having attributes similar to the user, and generating lighting control information including the most frequently used color, color temperature, and brightness for the group of similar users.

[0018] A user recognition-based personalized smart lighting control method further comprises: a step of recognizing a user’s access to a place where lighting is installed; subsequently, a step of additionally recognizing the access of another user; a step of comparing lighting control information corresponding to each of the user and the other user to determine whether the similarity between color, color temperature, and brightness is above a preset threshold level; if the similarity is confirmed to be above the threshold level, a step of calculating the median value of color, color temperature, and brightness from the lighting control information of the user and the other user and generating supplementary lighting control information based thereon; a step of controlling the lighting according to the supplementary lighting control information; if the similarity is confirmed to be below the threshold level, a step of generating a priority score for each of the user and the other user using the distance from the lighting, planned time of stay, age, and energy consumption based on the lighting control information during the same period; and a step of controlling the lighting based on the lighting control information of the user with the highest priority score.

[0019] A user recognition-based personalized smart lighting control method comprises: a step of controlling the lighting according to the lighting control information; subsequently, a step of tracking the user's gaze through an eye-tracking camera installed in the space; a step of checking whether the user's gaze movement trajectory tracked for a predetermined period based on the current time point does not deviate from a predetermined gaze stability range; a step of, if it is confirmed that the gaze movement trajectory does not deviate from the gaze stability range, setting a lighting concentration zone according to the gaze movement trajectory and controlling the illumination angle of the lighting so that the lighting is directed toward the lighting concentration zone; and a step of not controlling the illumination angle of the lighting if it is confirmed that the gaze movement trajectory deviates from the gaze stability range.

[0020] The step of controlling the illumination angle so that the light is directed toward the light concentration zone comprises: generating a first vector connecting the position of the user's eyes and the center position of the light concentration zone; generating a second vector connecting the center position of the light source of the light and the center position of the light concentration zone; calculating an angle between the first vector and the second vector; checking whether the angle is included within a preset angle range; if it is confirmed that the angle is included within the angle range, calculating an avoidance angle based on the light intensity of the light and the distance between the light and the user to prevent the light from being directed directly toward the user's eyes; correcting the illumination angle of the light based on the avoidance angle; and if it is confirmed that the angle is not included within the angle range, controlling the illumination angle so that the light is directed toward the center position of the light concentration zone.

[0021] A user recognition-based personalized smart lighting control method further comprises: a step of controlling the lighting according to the lighting control information; subsequently, a step of checking whether motion has been detected during a preset reference time through a motion detection camera installed in the space; a step of calculating an activity intensity based on the motion range, speed, frequency, and stop time ratio if it is confirmed that motion has been detected during the preset reference time; a step of checking whether the activity intensity is higher than a preset target intensity; a step of increasing the brightness of the lighting by a preset reference ratio if it is confirmed that the activity intensity is higher than the target intensity; a step of maintaining the brightness of the lighting if it is confirmed that the activity intensity is not higher than the target intensity; and a step of decreasing the brightness of the lighting by the reference ratio if it is confirmed that no motion has been detected during the preset reference time.

[0022] A user recognition-based personalized smart lighting control method further includes the step of cutting off the power to the light based on the result of checking whether motion was detected during a preset additional reference time after reducing the brightness of the light by the reference ratio, and determining that motion was not detected even during the additional reference time.

[0023] A user recognition-based personalized smart lighting control method further comprises: a step of controlling the lighting according to the lighting control information; subsequently, a step of checking the power consumption of a similar place having the same area and use as the place; a step of checking the power consumption of the place; a step of comparing the power consumption of the place with the power consumption of the similar place to check whether there is a state of excessive power consumption; if it is confirmed that there is a state of excessive power consumption, a step of checking the illuminance of the place to check whether the illuminance of the place is higher than a preset standard illuminance; if it is confirmed that the illuminance is higher than the standard illuminance, a step of cutting off the power to the lighting; if it is confirmed that the illuminance is not higher than the standard illuminance, a step of checking whether a display device in the place is in use; if it is confirmed that a display device in the place is in use, a step of reducing the brightness of the lighting by a preset first standard ratio and reducing the color temperature of the lighting by a preset second standard ratio; if it is confirmed that a display device in the place is not in use, a step of reducing the brightness of the lighting by the first standard ratio; and if it is confirmed that there is no state of excessive power consumption, a step of maintaining the lighting as is.

[0024] A computer-readable recording medium stores instructions that cause a processor to execute a user recognition-based personalized smart lighting control method.

[0025] A user recognition-based personalized smart lighting control device comprises: a memory; and a processor connected to the memory; wherein the processor is configured to recognize a user’s access to a place where lighting is installed, verify user identification information corresponding to the user, obtain lighting control information matched with the user identification information, and control lighting according to the lighting control information.

[0026] A user recognition-based personalized smart lighting control system comprises: a user terminal owned by a user living or active in a place where the lighting is installed; lighting that is automatically controlled according to lighting control information matched with user identification information and includes a power supply module, a light source module, a control module, and a communication module; and a device connected to the user terminal and the lighting via a network environment and including memory and a processor. The device is configured to recognize the user's access to the place where the lighting is installed, verify user identification information corresponding to the user, obtain lighting control information matched with the user identification information, and control the lighting according to the lighting control information.

[0027] A device according to one embodiment may be combined with hardware and controlled by a computer program stored on a medium to execute the method of any one of the methods described above. Effects of the invention

[0028] The embodiments can control lighting in a user-customized manner.

[0029] The embodiments can recognize when a user approaches a space where lighting is installed.

[0030] The embodiments can identify the lighting color, color temperature, and brightness preferred by the user and generate lighting control information for the user accordingly.

[0031] The embodiments can identify appropriate lighting control information when multiple users share the same lighting.

[0032] Meanwhile, the effects according to the embodiments are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description below. Brief explanation of the drawing

[0033] FIG. 1 is a schematic diagram showing the configuration of a system according to one embodiment. FIG. 2 is a flowchart illustrating the process of controlling lighting in a user-customized manner according to one embodiment. FIG. 3 is a flowchart illustrating the process of recognizing user access according to one embodiment. FIG. 4 is a flowchart illustrating the process of generating lighting control information by matching with a user according to one embodiment. FIG. 5 is a flowchart illustrating the process of controlling lighting in the case where multiple users share the same lighting according to one embodiment. FIG. 6 is a flowchart illustrating the process of controlling the illumination angle of a light by tracking the user's gaze according to one embodiment. FIG. 7 is a flowchart illustrating the process of controlling the illumination angle of a light according to one embodiment. FIG. 8 is a flowchart illustrating the process of adjusting the brightness of a light through motion detection according to one embodiment. FIG. 9 is a flowchart illustrating the process of controlling lighting according to power consumption in one embodiment. FIG. 10 is an example diagram of the configuration of a device according to one embodiment. Specific details for implementing the invention

[0034] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, various modifications may be made to the embodiments, and thus the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, and substitutions to the embodiments are included within the scope of the rights.

[0035] Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed forms, and the scope of this specification includes modifications, equivalents, or substitutions that fall within the technical concept.

[0036] Terms such as "first" or "second" may be used to describe various components, but these terms should be interpreted solely for the purpose of distinguishing one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component.

[0037] When it is stated that a component is "connected" to another component, it should be understood that it may be directly connected to or coupled with that other component, or that there may be other components in between.

[0038] The terms used in the embodiments are for illustrative purposes only and should not be interpreted as intended to be limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "comprising" or "having" are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0039] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0040] In addition, when describing with reference to the attached drawings, identical components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted. In describing the embodiments, if it is determined that a detailed description of related prior art could unnecessarily obscure the essence of the embodiments, such detailed description is omitted.

[0041] The embodiments can be implemented in various forms of products such as personal computers, laptop computers, tablet computers, smartphones, televisions, smart home appliances, intelligent automobiles, kiosks, and wearable devices.

[0042] FIG. 1 is a schematic diagram showing the configuration of a system according to one embodiment.

[0043] Referring to FIG. 1, a system according to one embodiment may include a user terminal (100), a light (300), and a device (200) that can communicate with each other through a communication network.

[0044] First, the communication network can be configured regardless of the mode of communication, such as wired or wireless, and can be implemented in various forms to enable communication between servers and between servers and terminals.

[0045] The user's terminal (100) is a terminal used by a user who lives or works in a place where lighting is installed, and can be implemented as a mobile phone, desktop PC, laptop PC, tablet PC, smartphone, etc., but is not limited thereto, and can be implemented as various types of communication devices that can be connected to an external server. For example, as shown in FIG. 1, the user's terminal (100) may be a smartphone, and may be adopted differently depending on the embodiment.

[0046] The user's terminal (100) may be configured to perform all or part of the computational functions, storage / reference functions, input / output functions, and control functions that a conventional computer has. The company's terminal (100) may be configured to communicate with the device (200) via wired or wireless means.

[0047] The user's terminal (100) may be connected to a website operated by a person or organization providing a service using the device (200), or may have an application developed and distributed by a person or organization providing a service using the device (200) installed. The user's terminal (100) may be linked with the device (200) through the website or application.

[0048] The lighting (300) is a device for emitting light to adjust the brightness and atmosphere of a place where the lighting is installed, and can be automatically controlled according to user identification information. At this time, the lighting (300) may be implemented as a ceiling light, downlight, line light, pendant, wall light, stand, table lamp, bar type light, strip light, smart bulb, etc., but is not limited thereto.

[0049] In addition to the basic configuration of receiving power to drive a light source module, the lighting (300) may include a control module and a communication module for adjusting brightness, color temperature, color, or the on / off state according to external commands. The communication module may be connected to a device (200) or a user's terminal (100) via wired or wireless communication methods such as Bluetooth, Wi-Fi, Zigbee, Thread, or power line communication.

[0050] The lighting (300) may be configured to receive lighting control information provided by the device (200) and change brightness, color temperature, color, etc., and if necessary, multiple lights (300) may be linked in a group form and controlled collectively based on the same control information.

[0051] In this way, the lighting (300) can be connected to the device (200) or an external server via wired or wireless connection and controlled remotely or automatically, and can be configured to be directly linked with the user's terminal (100) as needed. That is, the lighting (300) can communicate with the device (200) and the user's terminal (100) via wired or wireless connection.

[0052] Meanwhile, the lighting (300) can be implemented in various forms such as recessed, surface-mounted, wall-mounted, and stand-type depending on the indoor or outdoor installation environment, and can perform the same function as commonly used lighting.

[0053] In FIG. 1 and the description below, for convenience of explanation, only one user terminal (100) and one light (300) are illustrated and described, but the number of terminals can vary depending on the embodiment. As long as the processing capacity of the device (200) allows, there is no particular limit to the number of terminals.

[0054] The device (200) may be a private server owned by a person or organization providing a service using the device (200), a cloud server, or a peer-to-peer (P2P) set of distributed nodes. The device (200) may be configured to perform all or part of the computational functions, storage / reference functions, input / output functions, and control functions that a conventional computer possesses. The device (200) may be equipped with at least one artificial neural network that performs inference functions. The device (200) may be configured to communicate wirelessly or via wired connection with the user's terminal (100) and the light (300).

[0055] Additionally, the device (200) can communicate wirelessly or via wired connection with websites including SNS platforms such as blogs, cafes, Instagram, Facebook, Twitter, and YouTube, and web pages including articles, and the device (200) can access the websites to obtain information.

[0056] Additionally, the device (200) can recognize a user’s access to a place where lighting is installed, verify user identification information corresponding to the user, obtain lighting control information matched with the user identification information, and control the lighting according to the lighting control information.

[0057] Meanwhile, singular expressions described in the claims and the description of the invention may be understood to include plurals.

[0058] In the present invention, Artificial Intelligence (AI) refers to a technology that imitates human learning ability, reasoning ability, and perceptual ability, and implements them on a computer, and may include concepts such as machine learning and symbolic logic. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. AI technology can analyze input data as a machine learning algorithm, learn from the results of the analysis, and make judgments or predictions based on the results of the learning. Furthermore, technologies that mimic the functions of the human brain, such as cognition and judgment, by utilizing machine learning algorithms can also be understood as falling within the category of AI. For example, technological fields such as linguistic understanding, visual understanding, reasoning / prediction, knowledge representation, and motion control may be included.

[0059] Machine learning can refer to the process of training neural network models using experience in processing data. It implies that through machine learning, computer software improves its own data processing capabilities. A neural network model is constructed by modeling the correlations between data, and these correlations can be expressed by multiple parameters. A neural network model extracts and analyzes features from given data to derive correlations between them; machine learning can be defined as the process of optimizing the model's parameters by repeating this process. For example, a neural network model can learn the mapping (correlation) between inputs and outputs for data given as input-output pairs. Alternatively, even when only input data is provided, a neural network model can derive regularities between the given data and learn those relationships.

[0060] An artificial intelligence learning model or neural network model can be designed to implement the structure of the human brain on a computer and may include multiple network nodes that have weights and simulate neurons of a human neural network. The multiple network nodes may have interconnected relationships by simulating the synaptic activity of neurons, where neurons exchange signals through synapses. In an artificial intelligence learning model, multiple network nodes may be located in layers of different depths and exchange data according to convolutional connections. The artificial intelligence learning model may be, for example, an Artificial Neural Network (ANN) or a Convolutional Neural Network (CNN). As an embodiment, the artificial intelligence learning model may be machine learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning may include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, Ada-boost, Perceptron, Genetic Programming, and Clustering.

[0061] Among these, CNNs are a type of multilayer perceptron designed to use minimal preprocessing. CNNs consist of one or more convolutional layers and standard artificial neural network layers stacked on top, additionally utilizing weights and pooling layers. Thanks to this structure, CNNs can fully utilize two-dimensional input data. Compared to other deep learning architectures, CNNs demonstrate good performance in both image and audio fields. CNNs can also be trained using standard backpropagation. CNNs have the advantage of being easier to train than other feedforward artificial neural network techniques and using a small number of parameters.

[0062] Convolutional networks are neural networks comprising sets of nodes with bounded parameters. Many computer vision tasks have been significantly improved, driven by the increased size of available training data and the availability of computational power, combined with algorithmic advancements such as discriminative linear units and dropout training. In the case of massive datasets, such as those available for many tasks today, outfitting is not critical, and increasing the network size improves test accuracy. Optimal utilization of computing resources becomes a limiting factor. To address this, distributed, scalable implementations of deep neural networks can be employed.

[0063] FIG. 2 is a flowchart illustrating the process of controlling lighting in a user-customized manner according to one embodiment.

[0064] Referring to FIG. 2, first, in step S201, the device (200) can recognize the user's access to a place where lighting is installed.

[0065] To this end, detailed information regarding a plurality of lights (300) may be stored in a database provided in the device (200), and the detailed information regarding the lights may include, but is not limited to, a light identifier for uniquely identifying each light, a model name, power consumption, color range, color temperature range, number of brightness adjustment steps, installation location coordinates of the light, communication radius, connected network interface, connected sensor information, etc.

[0066] In addition, the database provided in the device (200) stores location information matched with each light (300), thereby allowing for accurate identification of the characteristics of the location where a specific light is installed. At this time, the location information may include the spatial coordinates of the location, the planar and three-dimensional spatial dimensions, and the use of the space. Furthermore, the location information stored in the database may be configured to enable integrated management at the location level by storing information such as identifiers, model names, manufacturers, specifications (e.g., resolution, detection distance, response speed, etc.), installation location coordinates, communication protocols, and power status of network devices, sensors, and cameras installed at the location.

[0067] Specifically, the device (200) can finally determine that the user has approached the place where the light is installed if it is confirmed that at least one of the following conditions is satisfied: (1) the user's terminal (100) is included within the communication range of the place where the light is installed (i.e., communication range-based access recognition), (2) a signal is received that a wireless signal has been exchanged between the light and the user's terminal (100) (i.e., wireless signal exchange-based access recognition), and (3) at least one of the user's voice, fingerprint, or face is recognized through a sensor provided in the place where the light is installed (i.e., sensor-based access recognition).

[0068] For a detailed explanation regarding this, refer to Fig. 3.

[0069] In step S202, the device (200) can verify user identification information corresponding to the user.

[0070] Specifically, when the device (200) recognizes a user's access in step S201, it can query or detect identification data (i.e., user identification information) associated with the accessing user based on this from a database (DB) provided in the device (200).

[0071] In this case, user identification information may include, but is not limited to, information that can identify an individual user, such as the user's name, gender, age, occupation, voice characteristics, fingerprint pattern, facial feature vector, user ID, unique identifier of the registered terminal (MAC address, UUID, etc.), location access history, and user's lighting control history.

[0072] The information may be stored in a database in advance through a user registration procedure, and the device (200) may refer to this information to perform an identification procedure when recognizing access thereafter.

[0073] That is, when the device (200) recognizes a user’s access through (1) communication range-based access recognition or (2) wireless signal exchange-based access recognition, that is, when it recognizes that the user has accessed the location through the user’s terminal (100), the device (200) additionally receives a unique identifier (MAC address, UUID, etc.) from the user’s terminal (100) and can determine the user who owns the terminal (100) by comparing it with the unique identifier of the user-specific registered terminal stored in the database (DB) to check for a match.

[0074] At this time, when comparing the received unique identifier and the registration information, the device (200) verifies the hashed terminal identification data (e.g., SHA256, HMAC, etc.) through a decoding or matching algorithm, and if the match rate or similarity exceeds a preset threshold (e.g., 90% or more), the owner of the terminal can be finally identified as the access user.

[0075] In addition, the device (200) can identify user identification information corresponding to the recognized biometric information by comparing and matching biometric recognition data, such as voice, fingerprint, and face, obtained from a sensor (e.g., camera, microphone, fingerprint reader, etc.) with registered user information stored in a database (DB) when access by a user is recognized through (3) sensor-based access recognition, that is, when access is detected through the user's biometric information.

[0076] At this time, biometric information matching can be performed based on feature vectors, and for example, in the case of face data, commonly used CNN (Convolutional Neural Network), FaceNet, or Dlib-based feature vector matching algorithms can be used to determine that the same user exists when the similarity score is greater than or equal to a set threshold value.

[0077] In the case of voice data, comparison can be performed using commonly used Mel Frequency Cepstral Coefficient (MFCC)-based feature extraction and Dynamic Time Warping (DTW) or neural network-based voice similarity analysis models.

[0078] In this way, the device (200) can verify identification information matched with the user using communication information or biometric information, and can utilize this in a subsequent step for querying lighting control information, creating user-customized control policies, or determining priorities among multiple users.

[0079] In step S203, the device (200) can obtain lighting control information matched with user identification information.

[0080] Here, lighting control information may include, but is not limited to, color, color temperature, brightness, etc.

[0081] In this case, Color may be color information of the lighting expressed in RGB (Red, Green, Blue) or HSV (Hue, Saturation, Value) values. Additionally, Color Temperature may be color temperature information of the lighting expressed in Kelvin (K) units, and can be continuously adjusted from warm white light to cool white light according to the color spectrum distribution of the lighting. Furthermore, Brightness is luminous intensity information of the lighting expressed in digital step values ​​of 0 to 100% or 0 to 255, which can be viewed as substantially the same concept as the commonly used physical illuminance. In other words, brightness can be interpreted as an indicator representing the luminous intensity of the lighting and the level of illuminance within the space.

[0082] The method of expressing such color, color temperature, and brightness is not limited thereto and may be expressed in other optical or electrical units depending on the embodiment.

[0083] Specifically, when the device (200) confirms the user's identification information in step S202, it can query the user-specific preferred lighting control information stored in the database (DB) using the identification key included in the user identification information.

[0084] This preferred lighting control information may include the color, color temperature, and brightness values ​​preferred by the user, and in this case, the device (200) can obtain the lighting control information by directly referencing it.

[0085] On the other hand, if the user identification information does not include preferred lighting control information, the device (200) can infer or generate lighting control information including the color, color temperature, and brightness preferred by the user based on the user's lighting control history.

[0086] At this time, the process of inferring or generating lighting control information including the color, color temperature, and brightness preferred by the user based on the user's lighting control history will be explained with reference to FIG. 4.

[0087] In step S204, the device (200) can control the lighting according to the lighting control information.

[0088] Specifically, the device (200) can generate a lighting control command to be applied to the lighting (300) based on the lighting control information confirmed in step S203.

[0089] At this time, the lighting control command may be composed of a control command set that reflects the color, color temperature, brightness, etc. included in the lighting control information, and for this purpose, the format of the control command set may be stored in advance in a database provided in the device (200).

[0090] For example, a lighting control command can be converted into a digital command packet format that can be transmitted via a lighting control protocol (ZigBee, BLE, Wi-Fi, Z-Wave, etc.) and can be transmitted directly to the lighting (300).

[0091] That is, the device (200) can generate a lighting control command based on the lighting control information and transmit it to the lighting to be controlled (300) to control the operating state of the lighting.

[0092] At this time, the device (200) may receive a status response signal from the light (300) after transmitting a control command, and the status response signal may include whether the control command was received (ACK / NACK), the current status of the light (ON / OFF, current brightness, color temperature, color value, etc.), and the result of applying the control command (including normal / error codes).

[0093] Additionally, the device (200) may receive a status response signal from the light (300) after transmitting a control command. At this time, the status response signal may include whether the control command was received (ACK / NACK), the current status of the light (ON / OFF, brightness, color temperature, color value, etc.), and the result of applying the control command (including normal / error codes), but is not limited thereto.

[0094] That is, the device (200) can analyze this signal to determine whether lighting control was performed normally.

[0095] At this time, if the device (200) confirms through a status response signal that the lighting (300) does not respond normally to the control command or contains an error code, it may retransmit the same lighting control command up to a predetermined number of times (n times). Here, the number of retransmissions, the retransmission interval, and the error threshold may be configured differently depending on system setting information or user setting information.

[0096] Additionally, if the retransmission failure persists, the device (200) may perform an alternative control command to switch the light to a safe mode state or return it to its default brightness and color, or send a warning message to the user's terminal (100) that a continuous error is occurring with respect to the light.

[0097] As a result, the device (200) can quickly and stably implement a lighting environment optimized for the user's characteristics and situation by recognizing the user's access and verifying user identification information to derive personalized lighting control information, and then generating and transmitting lighting control commands based on this.

[0098] FIG. 3 is a flowchart illustrating the process of recognizing user access according to one embodiment.

[0099] Referring to FIG. 3, first, in step S301-1, the device (200) can recognize the user's approach based on the fact that the user's terminal (100) is included within the communication range of the place where the light is installed.

[0100] Specifically, the device (200) may store network information corresponding to the location where the light is installed (e.g., SSID, BSSID, IP range, subnet address, etc. of a wireless access point) in advance in a database by matching it with the corresponding light.

[0101] Additionally, the device (200) can determine whether the terminal (100) is connected to the same network as the network of the place where the lighting is installed by checking network connection information collected periodically or when an event occurs from the user's terminal (100).

[0102] For example, if the connection SSID or IP range received from the user's terminal (100) matches the SSID or IP range of a previously registered location where lighting is installed, the device (200) may determine that the user's terminal (100) is included within the communication range of that location.

[0103] At this time, when the device (200) detects a terminal (100) using the same Wi-Fi network, it recognizes this as a user access signal and can identify the accessing user by linking it with user identification information stored in the database.

[0104] That is, the S301-1 step is a communication range-based access recognition method (i.e., a logical network connection-based access recognition method), which means a case where the user's access is recognized by the user's terminal (100) connecting to the same network of the place where the lighting is installed.

[0105] In step S301-2, the device (200) can recognize the user's approach based on receiving a signal that a wireless signal has been exchanged between the light (300) and the user's terminal (100).

[0106] Specifically, the device (200) can detect whether a signal is exchanged from the user's terminal (100) through a short-range wireless communication module (Bluetooth Low Energy, ZigBee, Z-Wave, NFC, etc.) provided in a light (300) or a gateway provided in a place where the light (300) is installed.

[0107] For example, the light (300) periodically transmits an advertising packet, and when the user's terminal (100) receives the signal and transmits a response packet, the device (200) can receive an exchange signal from at least one of the light (300) or the user's terminal (100) indicating that a wireless signal exchange between the two parties has been successfully completed.

[0108] At this time, the device (200) can recognize the physical approach of a user by analyzing communication indicators such as signal strength (RSSI, Received Signal Strength Indicator), reception time interval, and number of packet exchanges, and confirming that the terminal (100) is located at a predetermined distance (e.g., within 3m, within 5m, etc.) from the light (300).

[0109] That is, step S301-2 is a wireless signal exchange-based approach recognition method (i.e., a physical proximity-based approach recognition method), meaning that even if the user's terminal (100) uses a different network, it recognizes that it is close to the light (300) through short-range wireless signal exchange.

[0110] In step S301-3, the device (200) can recognize the user's access based on at least one of the user's voice, fingerprint, or face being recognized through a sensor installed in a place where lighting is installed.

[0111] To this end, at least one biometric sensor, such as a voice recognition sensor (microphone module), a fingerprint recognition sensor, or a face recognition sensor (camera module), may be provided in the place where the lighting is installed, but the type of sensor is not limited thereto. In addition, the device (200) can communicate wirelessly or via wired connection with the biometric sensor provided in the place where the lighting is installed.

[0112] Specifically, the device (200) can receive biometric information of a user from a biometric recognition sensor installed in a place where lighting is installed, and can determine whether the user has accessed the device by comparing the received biometric information with user identification information (e.g., registered voice features, fingerprint patterns, face feature vectors, etc.) stored in an internal database (DB).

[0113] (1) Voice recognition-based access recognition

[0114] Specifically, if a voice recognition sensor (microphone) is provided in a place where lighting is installed, when a user speaks a voice such as “Turn on the light” or “I’m here” within the place where lighting is installed, the device (200) can receive a voice signal from the voice recognition sensor.

[0115] Additionally, the device (200) can perform preprocessing such as noise removal, voice activity detection (VAD), frame splitting (20-40ms), and Hamming window application, which are typically performed on the received voice signal, and the device (200) can extract Mel Frequency Cepstral Coefficients (MFCC) from the preprocessed voice signal and generate a feature vector including acoustic features such as formant, pitch, and spectral entropy.

[0116] Additionally, the device (200) can compare the extracted feature vector with the user-specific voice feature vector registered in the database using a commonly used Dynamic Time Warping (DTW) or deep learning-based voice embedding model (e.g., SpeakerNet, ECAPA-TDNN).

[0117] At this time, if the similarity exceeds a preset first threshold (e.g., 0.8 or higher), the device (200) may recognize that the voice was spoken by a registered user, and accordingly, the device (200) may recognize that the user has accessed the location. Here, the first threshold is a preset value and may vary depending on the embodiment.

[0118] (2) Fingerprint recognition-based access recognition

[0119] Specifically, when a fingerprint recognition sensor is provided in a place where lighting is installed, when a user touches a door handle or switch part to which the fingerprint recognition sensor is attached in order to enter the place, the device (200) receives a fingerprint image from the fingerprint recognition sensor and can binarize the received fingerprint image through normalization and contrast enhancement processes.

[0120] Additionally, the device (200) can detect minutiae and bifurcations in the extracted fingerprint image and generate a feature vector based on the position, direction, and curvature of each feature point.

[0121] Subsequently, the device (200) can compare a feature vector extracted through a commonly used Euclidean distance or cosine similarity-based matching algorithm with a user-specific fingerprint template (hash-based feature matrix) registered in a database.

[0122] At this time, if the similarity exceeds a preset second threshold (e.g., 0.75 or higher), the device (200) may recognize that the fingerprint has been contacted by the registered user, and accordingly, the device (200) may recognize that the user has accessed the location. Here, the second threshold is a preset value and may vary depending on the embodiment.

[0123] (3) Face recognition-based access recognition

[0124] Specifically, when a face recognition sensor (camera module) is provided in a place where lighting is installed, the device (200) can receive a face image captured from the face recognition sensor, and after converting the received face image from RGB to Gray, it can perform preprocessing such as normalization, noise removal, and face region detection (e.g., Haar Cascade, MTCNN) that is typically performed.

[0125] Additionally, the device (200) can extract a face embedding vector (e.g., 128 dimensions or 512 dimensions) for a detected face region using a commonly used Convolutional Neural Network (CNN)-based face recognition model (e.g., FaceNet, ArcFace, ResNet-50-based model).

[0126] Afterward, the device (200) can compare the extracted face embedding vector with the user-specific face vector registered in the database using commonly used Cosine Similarity or L2 Distance.

[0127] At this time, if the similarity exceeds a preset third threshold (e.g., 0.7 or higher), the device (200) may recognize that the face was photographed by a registered user, and accordingly, the device (200) may recognize that the user has accessed the location. Here, the third threshold is a preset value and may vary depending on the embodiment. At this time, the first to third thresholds may all be set identically, only some may be set identically, or all may be set differently.

[0128] Meanwhile, the process of identifying a user by extracting user features through voice recognition, fingerprint recognition, and facial recognition can be performed in the same or substantially the same way as the biometric recognition procedures typically performed.

[0129] At this time, the device (200) may finally determine that the user has accessed the location if one or more of the biometric recognition results match the registered user identification information or if the similarity of each recognition result exceeds a reference threshold (e.g., average 0.8 or higher).

[0130] At this time, when two or more recognition sensors operate simultaneously, the device (200) can calculate a Fusion Confidence Score by assigning weights to the multi-sensor recognition results, thereby increasing the accuracy of approach recognition.

[0131] That is, step S301-3 is a sensor-based access recognition method (i.e., a method that determines access by utilizing direct user recognition results based on biometric information or voice), and can increase the accuracy and security of access recognition by verifying the actual presence of the user regardless of network or wireless signals. Therefore, even if the user does not possess the terminal (100), the device (200) can recognize that the user has approached a place where lighting is installed.

[0132] As a result, the device (200) can recognize with high accuracy and reliability that a user has approached a place where lighting is installed by utilizing network connection information, wireless signal exchange information, and biometric recognition information in combination, regardless of whether the user possesses a terminal (100).

[0133] FIG. 4 is a flowchart illustrating the process of generating lighting control information by matching with a user according to one embodiment.

[0134] Referring to FIG. 4, first, in step S401, the device (200) can check the time the light was operated by matching it with the user based on user identification information.

[0135] Specifically, the device (200) can use the user identification information confirmed in the previous step (S202) to query a lighting control log that includes a history of the user directly controlling the lighting (300) or the lighting operating automatically according to the user's access or environmental conditions.

[0136] At this time, the lighting control history may be included in user identification information or matched with user identification information and stored in an internal database (DB) provided in the device (200), and each lighting control history may be configured with a standardized field structure.

[0137] For example, the lighting control history may include, and is limited to, fields such as a User Identification ID (User_ID) representing the user who controlled the lighting or the user identification value matched with the control history; a Light Identification ID (Light_ID) representing the unique identification value of the lighting on which the control was performed; a Control Start Timestamp containing the exact time information when the lighting control command occurred; a Control End Timestamp containing the time information when the control command ended or the user left the location and the lighting was turned off; a Control Command Type (Control_Type) indicating the method of control (e.g., manual control, automatic control, voice control, application control, sensor control, etc.); Control Parameters containing the detailed setting values ​​of the lighting applied at that time (e.g., {Brightness: 80%, Color Temperature: 4200K, Color: RGB(255,220,180)}); and Environment Variables (Environment_Info) containing external sensor values ​​detected during lighting control (e.g., time zone, temperature, humidity, particulate matter concentration, solar altitude, user location coordinates, etc.). It is not.

[0138] That is, the device (200) can use the user identification information as a key to search for records containing the same user identification ID among the lighting control history in the database, and by extracting the control time and control end time among them, it can calculate the lighting operation time (Duty Time), which is the time when the lighting actually operated by matching with the user.

[0139] For example, if the light control history of user A is checked and it contains “control time = 2025-10-30 19:00:00” and “control end time = 2025-10-30 22:00:00”, the device (200) can confirm that the time the light was operated in conjunction with the user is 3 hours (= 22:00 - 19:00).

[0140] In addition, if there are multiple control records in the lighting control history of user A, the device (200) can verify the time during which the lighting was operated in conjunction with the user by merging each control section or calculating the cumulative sum (Σ) to calculate the total active time.

[0141] In step S402, the device (200) can check whether the time the light has been operating is longer than the target time.

[0142] Here, the target time may vary depending on the embodiment as a preset value. For example, the target time may be set to 12 hours, but is not limited thereto.

[0143] Specifically, the device (200) can be matched with the user identified through step S401 to check whether the time the lighting has been operated is longer than the preset target.

[0144] If it is confirmed in step S402 that the time the lighting has been operating is longer than the target time, in step S403, the device (200) can check whether the lighting control is periodically changed by time interval.

[0145] Specifically, if the device (200) determines that sufficient lighting control data has been accumulated for the user when it is confirmed that the time the lighting has been operated in a matched state is longer than a preset target time, it can perform a lighting control pattern analysis based on the user's lighting control history (Lighting Control Log).

[0146] Accordingly, the device (200) can analyze a plurality of control command data based on the lighting control history and determine whether the user's lighting control pattern is repeated at a certain time period through the following indicators.

[0147] (1) Control Change Count

[0148] The device (200) can calculate the number of times the value of control parameters (Control_Parameters) in a continuous control history for the same light (Light_ID) has changed differently from the previous history.

[0149] For example, if any of Brightness, Color Temperature, or Color changes from the previous history, it can be counted as “1 change.”

[0150] This indicator shows how often users change lighting control information and can be used as basic data to evaluate usage frequency and the activeness of lighting utilization patterns.

[0151] (2) Control Change Period

[0152] The device (200) can continuously calculate the time interval Δt = (t2 - t1) between the points in time when the control parameter is changed, and determine whether these Δt values ​​are repeated within a certain range within a certain interval (e.g., one day, one week).

[0153] If the Δt value is maintained stably within a specific tolerance range (e.g., ±5 minutes), it can be determined that the user is changing the lighting at regular intervals, and conversely, if the Δt value is irregular or has large variance, it can be considered as non-periodic control.

[0154] For example, if a pattern is repeated in which a user changes the brightness to 60% at 19:00 every day and adjusts the color temperature to 2700K at 22:00, the device (200) can determine that the user has a periodic control pattern of changing the lighting in 3-hour intervals.

[0155] (3) Interval Deviation

[0156] If the device (200) determines that the user's lighting control change cycle is constant, it can calculate the standard deviation (σ) of the time interval Δt between consecutive control commands.

[0157] At this time, if the σ value is below a preset threshold (e.g., 10 minutes or less), it means that the user is changing lighting parameters at regular intervals, and this can be used as an indicator suggesting the existence of a user's daily routine (Lighting Routine) or automated control pattern.

[0158] (4) Time-of-Day Pattern of Control Command Occurrence

[0159] The device (200) can classify a day into multiple time intervals (e.g., morning (06:00~09:00), afternoon (12:00~18:00), night (19:00~23:00)) based on the time of occurrence of the control command, or analyze the distribution of control command occurrences by histograms in minutes and hours.

[0160] In this case, if the frequency of control commands is statistically significantly high within a specific time interval, it can be determined that the user exhibits a pattern of changing control parameters by time interval.

[0161] For example, if control to increase the color temperature is repeated in the morning and control to decrease the brightness occurs periodically at night, the device (200) can determine that there is a lighting adjustment habit by time of day.

[0162] On the other hand, if control commands occur sporadically regardless of the time zone, it can be determined that no time-based pattern exists.

[0163] In this way, the device (200) can determine that the user's lighting control is in a state where it is periodically changed by time intervals by comprehensively analyzing the four indicators above, if ① the number of control changes is greater than or equal to a preset reference value (e.g., 5 times), ② the control change cycle and interval deviation are stable within the allowable error range, and ③ the timing of control command generation is concentrated in a specific time period.

[0164] Accordingly, according to step S403, the device (200) can automatically determine whether the user adjusts the lighting consistently or randomly by precisely analyzing the pattern of change of control parameters from the user-specific lighting control history.

[0165] If it is confirmed in step S403 that the lighting control is periodically changed by time period, in step S404, the device (200) can generate lighting control information including the most used color, color temperature, and brightness for a time period including the current time.

[0166] Specifically, if the device (200) determines through step S403 that lighting control is periodically changed by time period, it can calculate a representative lighting setting value corresponding to the time period to which the current time belongs, based on the control parameters recorded in the user's lighting control log.

[0167] At this time, the device (200) can divide the 24 hours of a day into multiple time intervals, and each interval can be defined as a 1-hour, 3-hour unit, or interval such as morning, day, evening, or night, depending on the system or user settings. For example, if the current time is 20:00, the device (200) can recognize that time as a “night (19:00~23:00)” time interval.

[0168] The device (200) can search for lighting control history corresponding to a time interval including the current time and calculate the frequency of occurrence for each control parameter such as Brightness, Color Temperature, and Color.

[0169] Afterwards, the setting value that shows the highest frequency for each item can be selected as the representative parameter.

[0170] For example, if there are 15 records where Brightness is set to 70% and 10 records where Brightness is set to 60% during the corresponding time period, the device (200) can determine 70% as the representative brightness value. Color temperature and color can also be determined as representative values ​​by calculating at least one of the mode or mean in the same way.

[0171] In addition, the device (200) can correct the stability of the representative value by giving higher weight to recent control history or removing outliers, in addition to simple frequency calculation.

[0172] For example, if the average color temperature in the same time period during the past week remains higher than the average over the past month, the device (200) can gradually increase the representative color temperature value of the time period to reflect the latest trend.

[0173] Based on the representative value calculated in this way, the device (200) can generate lighting control information corresponding to the current time period.

[0174] The generated lighting control information can be configured in the form of, for example, {Time_Slot: “19:00~23:00”, Brightness: 70%, Color Temperature: 2800K, Color: RGB(255,220,180)} and can be stored as a lighting profile by time period in the internal database (DB) of the device (200).

[0175] Afterwards, when a user accesses the device (200) or a lighting control request occurs during the corresponding time period, the device (200) can automatically control the brightness, color temperature, and color of the lighting based on the generated lighting control information.

[0176] Accordingly, the device (200) learns repeated setting values ​​by time period from the lighting control history for each user and can automatically calculate lighting control information optimized for the time period including the current time. Through this, the user can maintain a lighting state suitable for their personal lifestyle pattern and environment without separate control input, and the device (200) can realize automated lighting control that reflects the user's preference pattern.

[0177] If it is confirmed in step S403 that the lighting control is not periodically changed over time, in step S405, the device (200) can generate lighting control information including the color, color temperature, and brightness for which the lighting has been maintained for the longest time.

[0178] Specifically, if the device (200) determines through step S403 that the lighting control is not periodically changed over time, it can calculate a representative lighting setting value by analyzing the control parameters stored in the user's lighting control log.

[0179] At this time, the device (200) can calculate the cumulative time that each set value is maintained for each control parameter such as Brightness, Color Temperature, and Color, or calculate the frequency of occurrence of the set value.

[0180] Afterwards, the device (200) can select the setting value that has been maintained for the longest time or has the highest frequency of appearance for each control parameter as the representative parameter.

[0181] That is, the device (200) can identify the lighting settings that the user has maintained most stably regardless of the time zone, and can automatically generate lighting control information including the representative color, color temperature, and brightness derived as a result.

[0182] In addition, the device (200) can correct the stability of the representative value by giving higher weight to recent control history or removing outliers, in addition to simple frequency calculation.

[0183] For example, if the average color temperature over the past week remains higher than the average over the past month, the device (200) can gradually increase the representative color temperature value to reflect the latest trend.

[0184] Based on the representative value calculated in this way, the device (200) can generate lighting control information.

[0185] The generated lighting control information can be configured in the form of, for example, {Brightness: 70%, Color Temperature: 2800K, Color: RGB(255,220,180)} and can be stored as a lighting profile in the internal database (DB) of the device (200).

[0186] Afterwards, when a user accesses the device (200) or a lighting control request occurs, the device (200) can automatically control the brightness, color temperature, and color of the lighting based on the generated lighting control information.

[0187] If it is confirmed in step S402 that the time the lighting has been operating is not longer than the target time, in step S406, the device (200) can identify a similar user group by verifying age, gender, and occupation through user identification information, and for the similar user group, generate lighting control information including the most frequently used color, color temperature, and brightness.

[0188] Specifically, if the device (200) determines that the lighting control data for the user has not been sufficiently accumulated when it is confirmed that the operating time of the lighting matched with the user is not longer than a preset target time, the device (200) may determine that the lighting control data for the user has not been sufficiently accumulated.

[0189] Accordingly, the device (200) can identify a similar user group composed of multiple users who have the same or similar attributes as the user, such as age, gender, and occupation.

[0190] To this end, the device (200) can search for users belonging to the same age group, same gender, or same or similar occupational category by referring to age, gender, and occupational data included in user identification information.

[0191] Afterwards, the device (200) can collect the Lighting Control History of similar user groups stored in the database and perform statistical analysis.

[0192] At this time, the device (200) can calculate representative lighting parameters based on the frequency and duration of the control parameters of a similar user group.

[0193] For example, if the analysis of the lighting control history of users belonging to a similar user group shows that a color temperature of 3000K and a brightness of 60% represent the highest frequency and duration, the device (200) can determine this as a representative value.

[0194] Based on the representative value calculated in this way, the device (200) can generate lighting control information that reflects the preferred lighting conditions of a similar user group.

[0195] The generated lighting control information can be configured in the form of, for example, {Brightness: 60%, Color Temperature: 3000K, Color: RGB(255, 240, 210)} and can be stored in the internal database (DB) of the device (200) and used as an initial lighting setting value (Default Lighting Profile) for new users or users with insufficient data.

[0196] Accordingly, the device (200) can automatically provide a lighting environment optimized for the user's characteristics and environment by applying representative lighting information based on demographic similarity, even when the individual user's lighting control history is insufficient.

[0197] As a result, the device (200) can determine whether the lighting control history for each user is sufficient, and accordingly analyze the control patterns of individual users or similar user groups to automatically generate optimal lighting control information that reflects the user's actual usage tendencies and demographic characteristics.

[0198] Meanwhile, furthermore, the device (200) may predict user-specific lighting patterns by training an artificial intelligence (AI) model based on user-specific lighting control history data collected in steps S401 to S406.

[0199] Specifically, the device (200) can train a supervised learning prediction model by inputting user identification information, time zone, day of the week, environment variables (Environment_Info) (e.g., external illumination, temperature, humidity, etc.), control parameters (Control_Parameters) (e.g., brightness, color temperature, color), etc., as features, and outputting the user's actual control result or preferred setting value as a label.

[0200] At this time, the device (200) may apply time series prediction models such as a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) to consider the temporal continuity and periodicity of the lighting control pattern, and may also use a deep learning-based regression model (e.g., MLP, Transformer Encoder, etc.) in combination to learn the non-linear correlation between environmental changes and user behavior.

[0201] The trained AI model can be periodically updated as the user's lighting control history accumulates, and newly collected data (e.g., lighting control logs within the last week) is reflected using an incremental learning method to learn the user's latest preference trends in real time.

[0202] Accordingly, the device (200) can automatically suggest or apply optimal brightness, color temperature, and color according to time of day, environment, and situation by predicting and correcting user-specific lighting control behavior patterns through an AI model, thereby enabling personalized intelligent lighting control.

[0203] FIG. 5 is a flowchart illustrating the process of controlling lighting in the case where multiple users share the same lighting according to one embodiment.

[0204] Referring to FIG. 5, first, in step S501, the device (200) can additionally recognize access by another user.

[0205] Specifically, the device (200) can detect when another user additionally enters or approaches the same location after confirming that the user is still staying within the detection range of the location, following the recognition that a specific user has approached the location where the light is installed in the previous step (S201).

[0206] That is, the device (200) can additionally recognize the access of another user by identifying that a new access event has occurred within the communication radius or sensor detection area of ​​the same light (300) while the previously recognized user access event is still valid.

[0207] At this time, the device (200) can recognize the access of another user in the same or similar manner as the access recognition method used in step S201. For example, the device (200) may utilize (i) a communication range-based access recognition method that checks whether the user's terminal (100) is connected to the same wireless network (Wi-Fi, Bluetooth, Zigbee, etc.) as the light (300), (ii) a wireless signal exchange-based access recognition method that determines proximity by detecting the fact that wireless signals (e.g., RSSI, BLE Beacon, NFC, etc.) are exchanged between the light (300) and the user terminal (100), and (iii) a sensor-based access recognition method that detects biometric signals such as the user's voice, fingerprint, or face through a sensor (e.g., PIR sensor, ultrasonic sensor, camera, microphone, etc.) installed at the light installation location, either alone or in combination.

[0208] Additionally, the device (200) can determine first whether a previously recognized user is still staying at the location before recognizing access by another user.

[0209] To this end, the device (200) can determine whether the user is still staying in the place by checking (1) whether the terminal (100) of the previously recognized user maintains a connection state within the communication range for a certain period of time or longer (i.e., whether the terminal (100) of the previously recognized user is still within the communication range), (2) whether periodic exchange of lighting control signals or sensor detection is continuously performed (i.e., whether the terminal (100) of the previously recognized user and the lighting (300) are still communicating at a close distance), and (3) whether the user performed a separate authentication procedure upon exiting by querying an access authentication record based on a biometric signal (e.g., voice, face, fingerprint, etc.) obtained through a user recognition sensor (i.e., whether an “entry authentication record” exists in the user’s access log but an “exit authentication record” does not yet exist).

[0210] Through this process, the device (200) can exclude the situation where the existing user has left the place and can accurately recognize that multiple users are simultaneously present in the same place and sharing the same light (300).

[0211] In step S502, the device (200) can check whether the similarity of lighting control information corresponding to each user and other users is above a threshold level.

[0212] Here, the threshold level is a preset value and may vary depending on the embodiment.

[0213] Specifically, if the device (200) confirms in step S501 that the same light (300) is shared by multiple users, it can check the light control information for the user matched with the user's user identification information and the light control information for the other user matched with the other user's user identification information. At this time, the process of checking the light control information for the other user matched with the other user's user identification information can be performed in the same way as in step S203.

[0214] Additionally, when the device (200) confirms the lighting control information of the user and another user, it can calculate a first similarity by comparing the color values ​​included in each lighting control information, calculate a second similarity by comparing the color temperature values ​​included in the lighting control information, and calculate a third similarity by comparing the brightness values ​​included in the lighting control information.

[0215] In this process, the device (200) may apply a pre-set comparison function or distance calculation formula for each item to calculate the first similarity, second similarity, and third similarity.

[0216] For example, in the case of color values, a first similarity can be calculated based on the vector distance (Euclidean Distance) between color spaces (Hue, Saturation, Value), in the case of color temperature, a second similarity can be calculated by normalizing the difference in Kelvin unit values, and in the case of brightness, a third similarity can be calculated based on the relative difference in illuminance (lux) values.

[0217] Additionally, the device (200) can compare the first to third similarities calculated above with a preset threshold level, and if all similarities are above the threshold level, determine that the lighting preference characteristics of the user and other users are mutually similar.

[0218] On the other hand, if one or more similarities are below a threshold level, it can be determined that they have different lighting control tendencies.

[0219] If, in step S502, it is confirmed that the similarity of lighting control information corresponding to each user and other users is above a threshold level, in step S503, the device (200) can calculate the median value of color, color temperature, and brightness, and generate supplementary lighting control information based thereon.

[0220] Specifically, the device (200) may determine that the lighting preference characteristics of the user and the other user are similar if, in step S502, it is confirmed that the similarity of the lighting control information corresponding to each user and the other user is above a threshold level, that is, if the first to third similarities are all determined to be above a threshold level.

[0221] Accordingly, the device (200) can calculate a median value for each of the color, color temperature, and brightness included in the lighting control information of the user and other users.

[0222] At this time, the device (200) quantifies the color data of each user and other users based on a color space (Hue, Saturation, Value), calculates the median for each component, calculates the median from a set of measurements in Kelvin (K) units in the case of color temperature data, and calculates the median based on the distribution of percentage (%) values ​​or step values ​​in the case of brightness data.

[0223] Accordingly, the device (200) can calculate the median values ​​of color, color temperature, and brightness, and generate supplementary lighting control information based thereon.

[0224] The median value calculated in this way can be used as a reference value to balance the lighting environment preferences of multiple users, and the device (200) can generate supplementary lighting control information based on this.

[0225] Here, the supplementary lighting control information includes color, color temperature, and brightness values ​​corresponding to the average intersection of lighting preference characteristics among users, and can be applied as a lighting control command that does not reduce the satisfaction of each user even when multiple users use the same lighting simultaneously.

[0226] In step S504, the device (200) can control the lighting according to the supplementary lighting control information.

[0227] Specifically, the device (200) can adjust the lighting state in real time by converting the color, color temperature, and brightness values ​​included in the supplementary lighting control information into control command signals and transmitting them to the lighting (300).

[0228] At this time, the device (200) transmits the corresponding command through the control interface (e.g., Zigbee, BLE, Wi-Fi, RS-485, etc.) of the lighting (300), and the driving circuit of the lighting receives this and adjusts the current, voltage, emission frequency, etc. of the light source element so that the specified color, color temperature, and brightness can be realized.

[0229] In addition, since the device (200) has supplementary lighting control information that averages the lighting preference characteristics of multiple users, the lighting control can form a neutral light environment that is not biased toward a specific user.

[0230] If it is confirmed in step S502 that the similarity of lighting control information corresponding to each user and other users is below a threshold level, in step S505, the device (200) can generate a priority score for each user and other users.

[0231] Specifically, the device (200) may determine that the lighting preference characteristics of the user and the other user are different if, in step S502, it is confirmed that the similarity of the lighting control information corresponding to each user and the other user is below a threshold level, that is, if it is determined that at least one of the first to third similarity is below a threshold level.

[0232] Accordingly, the device (200) can generate a user-specific priority score to determine the priority of individual lighting control for each user and other users sharing the same lighting (300).

[0233] (1) Calculate distance score

[0234] Specifically, the device (200) can collect location information from each user's terminal (100) and calculate the distance value to the installation coordinates of the light (300).

[0235] At this time, the device (200) can calculate the actual distance using the RSSI (Received Signal Strength Indicator) strength, BLE (Bluetooth Low Energy) signal strength, Wi-Fi-based distance estimate, or UWB (Ultra-Wideband) distance measurement value between the user terminal (100) and the light (300).

[0236] The calculated distance value is normalized by referring to a preset distance weight table, and a distance score can be calculated to assign a higher score the closer it is.

[0237] Meanwhile, if the user's terminal is not present in the location or communication is temporarily impossible, the device (200) can recognize the user's location through a camera attached to a location where lighting is installed. To this end, the device (200) can communicate wirelessly or via wired connection with a camera attached to a location where lighting is installed.

[0238] At this time, the device (200) can identify human objects by applying commonly used object detection and human body recognition algorithms (e.g., YOLO, OpenPose, etc.) to image data acquired from a camera, and estimate the spatial coordinates of each object.

[0239] Additionally, the device (200) can compare the estimated coordinates with the installation location coordinates of the light (300) to convert them into actual distance or projected distance values.

[0240] Furthermore, the device (200) can utilize a face feature vector included in user identification information to distinguish which specific user is among a plurality of recognized human objects in the image data.

[0241] That is, the device (200) can extract the face feature vector of each object by referring to the face feature vector stored in the user registration database (DB) and applying a face recognition algorithm (e.g., FaceNet, ArcFace, ResNet-based embedding network, etc.) to the image acquired from the camera.

[0242] Subsequently, the device (200) calculates the cosine similarity or Euclidean distance between two vectors, and if the similarity is greater than or equal to a preset threshold, it can identify that the object is a registered user.

[0243] Through this, the device (200) can accurately identify a user through face feature vector-based image recognition and calculate the actual location of the user even when the terminal is not present or the signal is unstable.

[0244] The device (200) can calculate a distance score based on the degree of proximity to the light (300) by inputting the distance value calculated as above into a preset distance weighting table and scoring it.

[0245] For example, the weight can be set to 1.0 point for a distance of 1m or less, 0.7 points for a distance of 1 to 3m, 0.4 points for a distance of 3 to 5m, and 0.1 points for a distance of more than 5m, and such weights can be adjusted according to the structure or use of the place.

[0246] Consequently, the device (200) can selectively utilize available information from terminal signals or image recognition to precisely evaluate the physical distance between the user and the light (300) and calculate a distance score reflecting the result.

[0247] (2) Calculation of stay time score

[0248] Specifically, the device (200) can determine the average residence time of each user by analyzing the location access log and lighting control log included in each user's user identification information.

[0249] To this end, when the device (200) recognizes the user’s access to the location through step S201, it may store the timestamp at that point in time as the access start time in the database (DB), and subsequently, when it recognizes the user’s departure from the location, it may store the timestamp at that point in time as the access end time.

[0250] Through this, the device (200) can calculate the individual stay time (actual stay duration) by calculating the difference between each user's entry time and exit time, and derive the average stay time by averaging multiple records.

[0251] Afterward, the device (200) can estimate the user's residence duration by comparing the time when the user's access is newly recognized with the average residence time.

[0252] For example, if the analysis of a specific user's past access history shows that the average time spent at the same location is about 90 minutes, the device (200) can estimate the expected time spent when the user makes a new access as 90 minutes.

[0253] At this time, the device (200) can determine that the longer the dwell time, the higher the user's influence on the lighting environment, and calculate a duration score to assign a relatively higher weight to users with a long dwell time.

[0254] (3) Calculation of age score

[0255] Specifically, the device (200) can evaluate the sensitivity or level of need for a lighting environment according to the age of each user by referring to the age included in the user identification information for each user and other users.

[0256] At this time, the device (200) can calculate an age score to assign a relatively higher weight to older users by determining that, generally, visual fatigue increases and sensitivity to changes in illumination and color temperature increases as age increases.

[0257] For example, scores can be assigned in stages by age group, such as 0.5 points for users aged 20 or younger, 0.7 points for users aged 21 to 40, 0.9 points for users aged 41 to 60, and 1.0 point for users aged 61 or older.

[0258] Meanwhile, the device (200) can also perform weight adjustment by time period, taking into account the activity characteristics of the user by age group.

[0259] For example, for the elderly aged 60 or older, visual fatigue increases at low light levels during nighttime hours, so a high weight can be applied during nighttime hours and a relatively low weight can be applied during daytime hours.

[0260] In addition, for users in their 20s and 30s, it is possible to set a high weight during daytime hours when activity is high and a low weight during nighttime hours when rest mainly takes place.

[0261] Consequently, the device (200) can calculate an age score that quantitatively reflects the lighting environment suitability of each user by referring to age data included in user identification information and comprehensively considering visual characteristics by age group and time zone.

[0262] (4) Calculation of energy efficiency score

[0263] Specifically, the device (200) can check the color, color temperature, and brightness information included in each user's lighting control information, and evaluate the energy consumption efficiency according to the combination of these elements to calculate an energy efficiency score.

[0264] At this time, the device (200) can analyze power efficiency according to brightness, color temperature, and color, respectively, and calculate a final energy efficiency score by combining them based on weights.

[0265] For example, since higher brightness levels consume relatively more power, it can be set to assign a high score when the brightness-to-power consumption ratio is low.

[0266] In addition, corrections can be made so that the lighting efficiency is high in the high color temperature range (e.g., 5,000 Kelvin or higher) and low in the low color temperature range (e.g., 2,700 Kelvin or lower).

[0267] This color temperature correction is intended to reflect the physical characteristic that provides more efficient light output at higher color temperatures, even under the same power consumption conditions.

[0268] Additionally, the device (200) can apply a color-specific correction factor by taking into account the difference in luminous efficiency according to the lighting color.

[0269] For example, blue or green lighting may be given additional points because it has relatively high luminous efficiency, while red lighting may be given subtracted points because it consumes relatively more power.

[0270] This color correction is intended to reflect the energy efficiency characteristics of each color and can be calculated by considering the visual illuminance effect relative to actual power consumption.

[0271] That is, the device (200) can calculate an energy efficiency score for each user by comprehensively considering the color, color temperature, and brightness elements included in the lighting control information for each user.

[0272] (5) Generate priority scores per user

[0273] Specifically, the device (200) can generate a final priority score by integrating the distance score, duration score, age score, and energy efficiency score according to preset weights using a linear combination or normalized average method.

[0274] At this time, the weights of each score item can be pre-set and may vary depending on the embodiment. For example, if energy saving is prioritized, the weight of the energy efficiency score may be set high, and if user convenience is prioritized, the weights of the dwell time score and age score may be set relatively high.

[0275] In step S506, the device (200) can control the lighting based on the lighting control information of the user with the highest priority score.

[0276] Specifically, the device (200) can calculate the priority score of multiple users sharing the same light (300) through step S505 and select the user with the highest priority score among them as the priority user.

[0277] Afterwards, the device (200) can adjust the lighting state in real time by first converting the color, color temperature, and brightness values ​​included in the user's lighting control information into a control command signal and transmitting it to the lighting (300).

[0278] At this time, the device (200) transmits the control command through the control interface (e.g., Zigbee, BLE, Wi-Fi, RS-485, etc.) of the lighting (300), and the driver circuit of the lighting receives it and adjusts the current, voltage, emission frequency, etc. of the light-emitting element so that the specified color, color temperature, and brightness can be realized.

[0279] Meanwhile, if there are multiple users with the same priority score, the device (200) can determine the user with the higher score in the item with the higher weight as the priority user by checking the weight value corresponding to each score item (e.g., distance score, time of stay score, age score, energy efficiency score).

[0280] As a result, when multiple users share the same light, the device (200) controls the light according to the similarity between each user's light control information, and when the similarity is below a threshold level, applies the light control information of the user with the highest priority score calculated by considering user characteristics and energy efficiency, thereby ensuring consistency and efficiency of light control even in a multi-user environment.

[0281] FIG. 6 is a flowchart illustrating the process of controlling the illumination angle of a light by tracking the user's gaze according to one embodiment.

[0282] Referring to FIG. 6, first, in step S601, the device (200) can track the user's gaze through an eye-tracking camera installed at the location.

[0283] To this end, the device (200) can communicate wirelessly or via wired connection with an eye-tracking camera installed at the location, and the internal database of the device (200) may store matching information between a light (300) and an eye-tracking camera within the same location. The matching information may include lighting-related information such as the model name, rated luminous flux, size, light distribution type (focused or diffused), aperture diameter or length, and light intensity of the light, as well as camera-related information such as the identifier of the eye-tracking camera matched with the light, installation location coordinates, field of view, and focal length.

[0284] Additionally, the eye tracking camera includes an infrared emitter, a reflected light receiving sensor, and a high-resolution image capturing module, and can recognize a user's face and detect multiple feature points necessary for eye tracking. For example, the eye tracking camera can detect the center of the pupil, the outline of the iris, the contour of the eyeball, and reference points of the upper and lower eyelids in real time within a user's face image frame. Furthermore, the position coordinates within the image of each feature point can be extracted on a frame-by-frame basis and transmitted to the device (200).

[0285] Meanwhile, the eye-tracking camera may be in the same form as a commonly used eye-tracking camera.

[0286] Specifically, when the device (200) determines that the distance between the user's terminal (100) and the light (300) is less than or equal to a preset reference distance and that it will perform light control according to the user's state, it can receive continuous video frame data from the eye-tracking camera based on that point in time.

[0287] Additionally, the device (200) can convert and calculate the three-dimensional spatial coordinates of each feature point based on the two-dimensional coordinates of each feature point detected in the received image frame data, using the intrinsic parameters (e.g., focal length, optical center position) and extrinsic parameters (e.g., position and attitude information of the camera) of the eye-tracking camera.

[0288] At this time, the device (200) can derive the center coordinates of both eyes of the user using the converted 3D coordinates and calculate the line of sight direction vectors from each eye.

[0289] Here, the gaze direction vector is defined as a vector that unitizes the straight line direction between the eye center coordinates and the point where the gaze is directed, and the device (200) can calculate the gaze direction vector by connecting the eye center coordinates and the intersection point where the gaze is directed.

[0290] That is, the device (200) can continuously calculate a gaze direction vector at each point in time according to the flow of time, and generate user's gaze movement trajectory data through a combination of the eye center coordinates and the gaze direction vector at each point in time.

[0291] This eye movement trajectory data can be used as input data to determine whether the user's gaze is stable in a later stage or to set a lighting concentration zone.

[0292] In step S602, the device (200) can check whether the user's eye movement trajectory tracked for a certain period of time does not go out of the eye stability range.

[0293] Here, the fixed period is a period set based on the current point in time, and may be set differently depending on the embodiment. For example, the fixed period may be set as a period of 3 minutes prior to the current point in time, and in other embodiments, it may be changed to a time interval such as 1 minute, 5 minutes, or 10 minutes. This fixed period can be used as an analysis unit to evaluate whether the user's eye movement is maintained stably.

[0294] Additionally, the gaze stability range refers to an allowable range in which the user's gaze is maintained around a specific reference point or reference vector, and this can be pre-set based on experimental data or user environment conditions. For example, the gaze stability range may be defined as a range in which the user's gaze direction is maintained within a certain angle (e.g., ±5 degrees) in the up-down and left-right directions, respectively, around the reference vector. In another embodiment, the gaze stability state may be determined when the gaze continuously stays within a specific area (e.g., 10 centimeters × 10 centimeters).

[0295] Specifically, the device (200) can determine whether the user's gaze is stably maintained in a certain direction by calculating the displacement of the gaze movement trajectory over time based on gaze direction vectors or gaze intersection coordinates that are continuously calculated over a predetermined period, and by analyzing whether the displacement is maintained within a stable gaze range.

[0296] If it is confirmed in step S602 that the user's eye movement trajectory tracked for a certain period does not go beyond the eye stability range, in step S603, the device (200) can set a lighting concentration zone according to the eye movement trajectory.

[0297] Specifically, if the device (200) confirms that the user's gaze movement trajectory tracked for a certain period has not moved outside the gaze stability range, the device (200) can determine that the user's gaze is focused on a specific direction or a specific point.

[0298] Accordingly, the device (200) can set a lighting concentration zone centered on the location where the user's gaze is focused.

[0299] Here, the lighting concentration zone refers to the reference area for lighting control set around the point where the user's gaze continuously stays for a certain period of time (fixation point).

[0300] The device (200) can collect multiple eye intersection coordinates measured over a certain period and extract coordinates among them in which the movement displacement of the gaze falls within the gaze stability range (e.g., within ±5 degrees or within 10cm × 10cm) to calculate a representative location where the user's gaze is actually focused.

[0301] The representative location is the point where the gaze lingers and is focused for a relatively long time, representing the spatial center coordinates toward which the user's actual visual attention is directed.

[0302] Based on the representative location calculated in this way, the device (200) can define a lighting concentration zone having a certain radius.

[0303] At this time, the radius of the lighting concentration zone may vary depending on conditions such as the size of the space, the installation height of the lighting, or the lighting irradiation range, and may be set as, for example, a circular or elliptical area with a radius of 20 centimeters to 50 centimeters.

[0304] Additionally, if the user's line of sight forms a constant vector, the device (200) may define a fan-shaped lighting concentration zone based on the line of sight.

[0305] Furthermore, the device (200) can analyze the distribution of eye intersection points measured over a certain period and, if the gaze is concentrated within a specific area at a rate of more than a certain percentage, set that area as a lighting concentration zone.

[0306] For example, if it is analyzed that the user's gaze is concentrated within a specific area by more than 70 percent of the total gaze data, the device (200) can designate the center of that area as the center of the light concentration zone.

[0307] In step S604, the device (200) can control the irradiation angle of the light so that the light is directed toward the light concentration zone.

[0308] Specifically, the device (200) can set the area where the user's gaze is focused as a lighting concentration area, and then control the illumination angle of the light so that the light is directed toward the lighting concentration area.

[0309] At this time, the process of controlling the illumination angle of the light will be explained with reference to Fig. 7.

[0310] If it is confirmed in step S602 that the user's eye movement trajectory tracked for a certain period is outside the eye stability range, in step S605, the device (200) may not control the illumination angle of the light.

[0311] Specifically, if the device (200) confirms that the user's gaze movement trajectory tracked for a certain period is outside the gaze stability range, it can determine that the user's gaze is not focused on a specific direction or a specific point but is exploring the surroundings.

[0312] Accordingly, the device (200) determines that control of the illumination angle of the light is not necessary and may control the illumination angle of the light without controlling it or maintain the previous state.

[0313] For example, if the user's gaze trajectory continuously moves over an area of ​​50 centimeters or more for 3 minutes, or if the dispersion of the gaze intersection exceeds a preset threshold value, the device (200) determines that the user is not looking at a specific point and can be set not to perform lighting angle control.

[0314] As a result, the device (200) can identify an area where the user's gaze is stably focused based on the user's gaze movement trajectory, and by controlling or limiting the angle of illumination according to the area, the light can naturally respond to the user's gaze direction while preventing unnecessary movement of the light.

[0315] FIG. 7 is a flowchart illustrating the process of controlling the illumination angle of a light according to one embodiment.

[0316] Referring to FIG. 7, in step S701, the device (200) can generate a first vector connecting the user's eye position and the center position of the light concentration zone.

[0317] Specifically, the device (200) can detect the user's face position and the center coordinates of both eyes through an eye-tracking camera installed in the location.

[0318] Here, the center coordinates of both eyes may include the center coordinates of the left pupil (xL, yL, zL) and the center coordinates of the right pupil (xR, yR, zR) detected by a commonly used face recognition algorithm (e.g., Dlib, MediaPipe, or OpenCV-based face landmark detection algorithm) from an image frame acquired by an eye-tracking camera.

[0319] The device (200) can calculate the average coordinates (x0, y0, z0) = ((xL + xR) / 2, (yL + yR) / 2, (zL + zR) / 2) for the center coordinates of the two eyes, or select the coordinates (xR, yR, zR) of a preset reference eye (e.g., right eye) to set as the user's eye position.

[0320] The eye position coordinates calculated in this way can be expressed based on the internal coordinate system of the eye-tracking camera (e.g., camera center coordinate system).

[0321] Meanwhile, the device (200) can set the center position of the lighting concentration zone to coordinate values ​​(x1, y1, z1) in a predefined 3D spatial coordinate system (e.g., indoor reference XYZ coordinate system) within the lighting control target space through a point in space determined based on the average or mode of the line of sight intersection coordinates calculated in step S603.

[0322] That is, the center coordinates (x1, y1, z1) of the lighting concentration zone may represent a point in space determined based on the average or mode of the line-of-sight intersection coordinates calculated in step S603.

[0323] Additionally, the device (200) can normalize the eye position coordinates (x0, y0, z0) obtained from the eye tracking camera into the same reference coordinate system by converting them through a spatial correction module in order to eliminate the reference difference between the two coordinate systems.

[0324] For example, if the coordinate system of the eye-tracking camera is defined as a camera-centered coordinate system, the device (200) may apply a preset rotation matrix R (3×3) and a translation vector T (3×1) to convert it to an indoor reference coordinate system. Accordingly, the device (200) can calculate the converted eye position coordinates (x0', y0', z0').

[0325] Subsequently, the device (200) can calculate a first vector (x1 - x0', y1 - y0', z1 - z0') by connecting the converted eye position coordinates (x0', y0', z0') and the center coordinates (x1, y1, z1) of the light concentration zone on the same indoor reference coordinate system.

[0326] At this time, the first vector is a reference vector that quantitatively represents the direction in which the user's gaze is directed, and can be used to determine the lighting rotation direction and avoidance angle by calculating the angle with the reference direction vector (second vector) of the lighting body in a subsequent step (S702).

[0327] In step S702, the device (200) can generate a second vector connecting the center position of the light source of the light and the center position of the light concentration zone.

[0328] Specifically, the device (200) can define the center position of a lighting module (e.g., LED light source, laser module, or array of multiple light sources) included in the lighting (300) as the center position of the lighting light source.

[0329] At this time, the center position of the lighting source can be expressed as the center point coordinates (x2, y2, z2) on a coordinate system defined with respect to the optical axis inside the lighting housing or main body.

[0330] For example, the central lens position inside the light or the geometric center point of the light-emitting module array may be set as the center of the light source, and these coordinates may be stored in the light's built-in sensor (e.g., IMU, position sensor, or encoder) or in a database provided in the device (200) in match with the corresponding light.

[0331] Meanwhile, the center coordinates (x1, y1, z1) of the lighting concentration zone can be defined on a three-dimensional reference coordinate system (e.g., indoor reference XYZ coordinate system) within the lighting control target space, based on the average or mode of the line of sight intersections determined in step S603.

[0332] Additionally, the device (200) can perform a spatial correction process to convert the internal coordinate system of the lighting (e.g., the center coordinate system of the lighting module) into an indoor reference coordinate system so that the two coordinates can be compared based on the same coordinate system.

[0333] For example, the device (200) can apply a preset rotation matrix Rl (3×3) and translation vector Tl (3×1) to convert the lighting internal coordinate system to the indoor reference coordinate system.

[0334] Accordingly, the device (200) can calculate the converted light source center coordinates (x2', y2', z2') by a linear transformation formula such as “(x2', y2', z2') = Rl × (x2, y2, z2) + Tl”.

[0335] Subsequently, the device (200) can calculate a second vector V2 = (x1 - x2', y1 - y2', z1 - z2') by connecting the converted light source center coordinates (x2', y2', z2') and the light concentration zone center coordinates (x1, y1, z1) on the same indoor reference coordinate system.

[0336] At this time, the second vector is a reference vector that quantitatively expresses the direction in which the optical axis of the lighting body is facing, and can be used to calculate the angle (avoidance angle) between the two vectors through a dot product operation with the first vector in a subsequent step (S703).

[0337] Additionally, the device (200) can normalize the first vector and the second vector so that they are defined on the same indoor reference coordinate system, thereby preventing errors caused by coordinate system mismatch when calculating angles.

[0338] In step S703, the device (200) can calculate the angle between the first vector and the second vector.

[0339] Specifically, the device (200) can quantitatively calculate the directional difference of each vector by performing a dot product operation of two vectors.

[0340] At this time, the device (200) can calculate a cosine value corresponding to the angle between the two vectors by using the value obtained by dividing the inner product value by the product of the magnitudes (magnitude or length of the vectors) of the first vector and the second vector.

[0341] That is, the device (200) can determine that the directions of the two vectors are nearly the same as the cosine value of the angle formed by the first vector and the second vector approaches 1 (i.e., the angle θ is 0 degrees), that the two vectors are orthogonal to each other as the cosine value approaches 0 (i.e., the angle θ is 90 degrees), and that the directions of the two vectors are opposite as the cosine value approaches -1 (i.e., the angle θ is 180 degrees).

[0342] The device (200) can derive the actual angle value between the two vectors based on the above judgment result, and then perform a control operation to determine the rotation direction and avoidance angle of the light using the angle value calculated in the subsequent step (S704).

[0343] In step S704, the device (200) can check whether the angle is included within the set angle range.

[0344] Here, the above-mentioned set angle range may refer to a threshold range for determining whether the user's line of sight (first vector direction) and the light axis direction (second vector direction) of the lighting are facing each other with respect to the lighting concentration zone.

[0345] In other words, the set angle range refers to the range set to determine when the direction of the user's eyes and the direction of illumination of the light source are opposite each other (e.g., around 180 degrees).

[0346] For example, according to the embodiment, the set angle range may be defined as a range of approximately 170 degrees or more and 190 degrees or less by giving a tolerance of ±10 degrees based on 180 degrees.

[0347] These settings can be used as a criterion to determine situations where the user stares directly at the light or where glare is likely to occur.

[0348] Specifically, the device (200) can determine whether the angle value between the first vector and the second vector calculated in step S703 is included in the set angle range.

[0349] These verification results can be used as a judgment criterion to determine whether to control the rotation of the lighting or to perform an avoidance control operation in a subsequent step (S705).

[0350] If it is confirmed in step S704 that the angle is included within the set angle range, in step S705, the device (200) can calculate the avoidance angle based on the amount of light from the light and the distance between the light and the user.

[0351] Specifically, if the device (200) confirms in step S704 that the angle between the first vector and the second vector is included within a preset angle range, it can determine that the user is likely to feel glare because the directions of the two vectors are facing each other.

[0352] Accordingly, the device (200) can generate an avoidance angle to rotate the illumination angle by a certain angle to prevent glare.

[0353] At this time, the device (200) can obtain light quantity information matched to the corresponding light (300) through an internal or external database. The light quantity can be expressed as a luminance or illuminance value emitted from the light.

[0354] Additionally, the device (200) can calculate the actual distance between the user's terminal (100) and the light (300) based on the Time of Flight (ToF), Angle of Arrival (AoA), or Received Signal Strength Indicator (RSSI) using an ultra-wideband (UWB) signal, a low-power Bluetooth (BLE) signal, or an infrared distance sensor.

[0355] Subsequently, the device (200) can dynamically calculate the avoidance angle according to the acquired light intensity value and distance value. At this time, the avoidance angle may be determined within a preset standard angle range (e.g., 5 to 15 degrees), but the standard angle range is not limited thereto.

[0356] That is, if the device (200) determines that the glare will be relatively severe when the amount of light from the light exceeds a preset standard amount of light (e.g., 500 lx) or when the distance between the user and the light is less than or equal to a preset standard distance (e.g., 1.5 m), the device (200) can set the avoidance angle to a large upper limit of the standard angle range (e.g., about 15 degrees).

[0357] Conversely, if the light intensity is below a standard light intensity or the distance between the user and the light is greater than a standard distance, the device (200) may determine that the possibility of glare is low and set the avoidance angle to be small, approaching the lower limit of the standard angle range (e.g., about 5 degrees).

[0358] That is, the device (200) can select an avoidance angle that is proportional to the amount of light from the light and inversely proportional to the distance between the user and the light.

[0359] In this way, the device (200) can effectively mitigate glare while maintaining brightness balance and visual stability in the light concentration zone by dynamically adjusting the avoidance angle within a preset standard angle range (e.g., a range of 5 to 15 degrees) by comprehensively considering the amount of light and the distance from the user.

[0360] In step S706, the device (200) can correct the illumination angle based on the avoidance angle.

[0361] Specifically, the device (200) can determine that the user is likely to receive direct glare from the light when the angle between the first vector (vector from the user's eye to the light-focused area) and the second vector (vector from the light to the light-focused area) is close to 180° and falls within the set angle range, that is, when the two vectors are facing in nearly opposite directions.

[0362] Accordingly, the device (200) can change the direction of the light axis of the light by rotating the second vector by the avoidance angle on the plane formed by the first vector and the second vector using the avoidance angle calculated according to the light intensity of the light and the distance between the user and the light.

[0363] At this time, the rotation can be performed with the normal direction of the plane containing the two vectors as the axis of rotation, and the direction of rotation can be determined based on the reference axis of the lighting installation coordinate system (e.g., x-axis, y-axis, z-axis) or the gravity direction vector.

[0364] The device (200) transmits a lighting control command generated based on the rotation calculation result to the lighting (300), and the lighting (300) controls a driving unit (e.g., servo motor, gimbal, rotation axis controller, etc.) according to the command, thereby correcting the actual illumination angle so that the optical axis of the lighting deviates by an avoidance angle calculated from the user's line of sight.

[0365] In this way, the device (200) can effectively prevent glare from direct light by diverting the direction of illumination from the user's line of sight by a predetermined angle, while simultaneously maintaining the balance of illumination and visual stability of the concentrated lighting area.

[0366] If it is confirmed in step S704 that the angle is not included within the set angle range, in step S707, the device (200) can control the illumination angle so that the light is directed toward the center position of the light concentration zone.

[0367] Specifically, if the device (200) determines in step S704 that the angle between the first vector and the second vector does not fall within a preset angle range, it can determine that the user is unlikely to feel glare because the light is directed in a direction that is not nearly opposite to the user's line of sight.

[0368] Accordingly, the device (200) can control the rotation axis of the light (e.g., horizontal rotation axis, vertical rotation axis) based on a second vector which is a vector from the light to the light concentration zone, so that the light axis of the light is directed toward the center of the light concentration zone.

[0369] That is, the device (200) transmits a lighting control command generated based on the rotation calculation result to the lighting (300), and the lighting (300) can automatically correct the illumination angle so that the light axis of the lighting is directed toward the center of the lighting concentration area by controlling a driving unit (e.g., servo motor, gimbal, rotation axis controller, etc.).

[0370] As a result, the device (200) analyzes the relative positional relationship between the user's gaze direction and the illumination direction of the light, and corrects the illumination angle by applying an avoidance angle when glare is expected, and controls the light to be directed toward the center of the light concentration zone when glare is not expected, thereby minimizing user glare while simultaneously ensuring illumination balance and visual stability.

[0371] FIG. 8 is a flowchart illustrating the process of adjusting the brightness of a light through motion detection according to one embodiment.

[0372] Referring to FIG. 8, first, in step S801, the device (200) can check whether movement was detected during a reference time.

[0373] To this end, the device (200) can communicate wirelessly or via wired connection with a motion detection camera installed in the location.

[0374] Here, the motion detection camera is a sensor-fusion type camera pre-installed to detect user movement, and may include at least one of a PIR (Infrared) sensor, an ultrasonic sensor, an optical image sensor, and an accelerometer, but is not limited thereto.

[0375] In addition, the motion detection camera may be the same camera as the eye-tracking camera described in FIG. 6.

[0376] In addition, the reference time may be a preset time interval (e.g., 30 seconds, 1 minute, 5 minutes, etc.) based on the current time point, and may vary depending on the embodiment.

[0377] Specifically, the device (200) can periodically receive sensor data indicating whether movement has occurred and detailed characteristics from a motion detection camera installed at the location during a preset reference time based on the current time.

[0378] Here, the sensor data may include, but is not limited to, a timestamp, which is information recording the time at which motion is detected in milliseconds (ms); a duration, which is the time the detected motion is maintained; an intensity, which is a relative strength based on the rate of change of pixels within the image, the amount of heat detected, or the amount of change in acceleration; a position coordinate, which is the area or spatial coordinate within the screen where motion is detected; and a motion vector, which represents the amount of change in center coordinates between consecutive image frames.

[0379] That is, the device (200) can determine whether movement of the user has been detected through sensor data received from the motion detection camera during a reference time.

[0380] If it is confirmed that movement is detected during the reference time in step S801, in step S802, the device (200) can calculate the activity intensity based on the movement range, speed, frequency, and stop time ratio.

[0381] Specifically, the device (200) can determine whether a user's movement is detected during a reference time based on sensor data received through a motion detection camera, and if movement is detected, analyze the sensor data to calculate the activity intensity.

[0382] Here, the sensor data may include the time of motion occurrence, duration of motion, intensity of motion, position coordinates of motion, and movement vector between frames as described in step S801, and the user's motion characteristics can be quantitatively analyzed through this data.

[0383] First, the device (200) can determine the range of movement by calculating the range of change of movement position coordinates or the sum of movement distances based on sensor data during a reference time. The wider the range of movement, the more actively the user can be judged to have moved within the location.

[0384] Additionally, the device (200) can calculate the average movement speed by dividing the change in distance of the movement vector between frames by the time interval, and can determine that the higher the calculated speed value, the faster the user's movement and the higher the energy.

[0385] In addition, the device (200) can calculate the movement frequency by analyzing the number of times new movement is detected or the operation cycle during a reference time. The movement frequency can be used as an indicator of how often the user moved within a unit of time.

[0386] Meanwhile, the device (200) can calculate the stop time ratio by calculating the ratio of the interval during which no movement is detected during the entire reference time, and the higher the stop time ratio, the lower the user's activity level can be evaluated.

[0387] The device (200) can determine the user's activity intensity by comprehensively analyzing the movement range, speed, frequency, and stop time ratio calculated as above. The activity intensity can be generated as a value between 0 and 10, for example, and the higher the value, the higher the user's activity level.

[0388] That is, the device (200) can generate activity intensity by comprehensively analyzing such that the range of motion, speed, and frequency are directly proportional to the activity intensity, and the ratio of rest time is inversely proportional.

[0389] The activity intensity calculated in this way can be used as a standard for lighting brightness control performed in subsequent stages, and the lighting environment can be intelligently adjusted according to the user's actual movement patterns.

[0390] In step S803, the device (200) can check whether the activity intensity is higher than the target intensity.

[0391] Here, the target strength may vary depending on the embodiment as a preset value. For example, the target strength may be set to 7, but is not limited thereto.

[0392] Specifically, the device (200) can compare the activity intensity calculated in step S802 with a preset target intensity to determine whether the activity intensity is higher than the target intensity.

[0393] If it is confirmed that the activity intensity is higher than the target intensity in step S803, in step S804, the device (200) can increase the brightness of the light by a reference ratio.

[0394] Here, the reference ratio is a preset value and may vary depending on the embodiment. For example, the reference ratio may be set to 20%, but is not limited thereto.

[0395] Specifically, if the device (200) determines that the user is actively moving when the activity intensity calculated in step S802 is higher than the target intensity, it can improve the visual comfort and concentration of the space by increasing the brightness of the lighting by a certain percentage.

[0396] For example, if it is determined that the activity intensity is higher than the target intensity while the current lighting brightness is set to 60%, the device (200) can increase the lighting brightness to 72% by applying a reference ratio of 20%.

[0397] At this time, the device (200) can determine the final brightness (72%) by calculating an increase (12%) by multiplying the existing brightness setting value (60%) by a reference ratio (20%) and adding this to the current brightness value.

[0398] That is, the device (200) can control the lighting to create a brighter environment as the user's movement becomes more active, thereby supporting the user's concentration and activity level.

[0399] Meanwhile, if the brightness of the lighting has already reached a maximum brightness value that can be set, the device (200) may control the increase in brightness to be limited or maintained, and such control may be performed with consideration for the safety of the system and user visual fatigue.

[0400] Additionally, the device (200) can be configured to enable the maintenance of a consistent lighting environment in subsequent control steps by reflecting and storing the corresponding change value in the lighting control information when the brightness of the lighting is changed.

[0401] That is, the device (200) generates a lighting control command according to the finally determined brightness value and transmits it to the lighting (300), and the lighting (300) can achieve a final brightness state by automatically correcting the light intensity output according to the received control command.

[0402] If it is confirmed in step S803 that the activity intensity is not higher than the target intensity, in step S805, the device (200) can maintain the brightness of the light.

[0403] Specifically, if the device (200) determines that the activity intensity calculated in step S802 is not higher than the target intensity, it determines that the user is moving but not active, and can control the light to maintain its current state without changing the brightness.

[0404] For example, if it is determined that the activity intensity does not reach the target intensity (e.g., 7) while the current lighting brightness is set to 60%, the device (200) may not perform a brightness increase command and may maintain the existing brightness value (60%).

[0405] By maintaining the brightness of the lighting in this way, unnecessary energy consumption can be prevented, and visual fatigue caused by excessive lighting brightness during static or resting situations can be minimized.

[0406] Additionally, the device (200) can periodically collect sensor data even while maintaining brightness to monitor changes in activity intensity.

[0407] Through this, if the activity intensity exceeds the target intensity or increases rapidly, it switches to a subsequent step (S804) and the brightness of the lighting can be automatically adjusted.

[0408] That is, the device (200) can respond to changes in the environment by dynamically maintaining or adjusting the brightness of the lighting by reflecting real-time changes in the user's movement pattern.

[0409] If it is confirmed that no movement is detected during the reference time in step S801, in step S806, the device (200) can reduce the brightness of the light by a reference ratio.

[0410] Specifically, the device (200) checks whether the user's movement is detected during a reference time based on sensor data received through a motion detection camera, and if no movement is detected, determines that the user is in a stationary state or has left the place, and can reduce the brightness of the light by a certain percentage to prevent unnecessary power consumption.

[0411] For example, if no movement is detected for a reference time while the current brightness of the light is set to 60%, the device (200) can increase the brightness of the light to 48% by applying a reference ratio of 20%.

[0412] At this time, the device (200) can determine the final brightness (48%) by calculating a reduction amount (12%) by multiplying the existing brightness setting value (60%) by a reference ratio (20%) and then subtracting this from the current brightness value.

[0413] Additionally, the device (200) can gradually reduce the brightness at regular time intervals to minimize visual discomfort to the user during the process of reducing the brightness of the light. For example, by controlling the brightness reduction to be applied sequentially by 2% at 1-second intervals to reach a final brightness (48%), visual fatigue caused by sudden changes in illumination can be prevented.

[0414] Meanwhile, the device (200) may limit further reduction when the brightness of the light reaches a preset minimum brightness value (e.g., 30%), and such control may be performed with consideration for the stability of the system and user safety.

[0415] Additionally, the device (200) can periodically monitor sensor data even after the lighting brightness has decreased, and if movement is detected again, it can switch to the previous step (S804) to increase the brightness again.

[0416] That is, the device (200) can simultaneously ensure energy efficiency and user convenience by automatically reducing and restoring the lighting brightness depending on the presence and activity of the user.

[0417] That is, the device (200) generates a lighting control command according to the finally determined brightness value and transmits it to the lighting (300), and the lighting (300) can achieve a final brightness state by automatically correcting the light intensity output according to the received control command.

[0418] In step S807, the device (200) checks whether motion is detected during an additional reference time, and if motion is not detected even during the additional reference time, it may cut off the power to the light.

[0419] Here, the additional reference time may be a preset time interval (e.g., 5 minutes, 10 minutes, 15 minutes, etc.) based on the point in time when the brightness of the lighting is reduced by a reference ratio, and may vary depending on the embodiment.

[0420] Specifically, after reducing the brightness of the light in step S806, the device (200) can continuously monitor sensor data received through the motion detection camera for a preset additional reference time to determine whether motion is still not detected at all.

[0421] At this time, if the device (200) determines that no movement is detected even during the additional reference time, it can determine that the user is absent from the location, and accordingly, the device (200) can control the power of the lighting (300) to cut off the power to prevent unnecessary power waste and improve the energy efficiency of the system.

[0422] For example, the device (200) may completely cut off the power to the light (300) if no movement is detected even after an additional reference time (e.g., 10 minutes) has elapsed since the brightness of the light was reduced from 60% to 48% in step S806.

[0423] That is, the device (200) can automatically save lighting power when the user is absent by performing a sequential control flow of “after lighting brightness is reduced → no movement detected for an additional reference time → power cut off”.

[0424] Additionally, the device (200) may perform a procedure to gradually weaken the lighting state before cutting off the lighting power. For example, by sequentially reducing the brightness in 5% increments during the last 30 seconds immediately before cutting off the power and then completely cutting off the power of the lighting (300), it is possible to prepare for the possibility of a user re-entering the place and minimize visual discomfort caused by the flashing of the light.

[0425] Meanwhile, if the device (200) monitors sensor data for an additional reference time after reducing the brightness of the light in step S806 and confirms that movement has been detected, it may return to step S802 to recalculate the activity intensity. At this time, the device (200) may control the brightness of the light by increasing it by a reference ratio based on the newly calculated activity intensity, or maintain the brightness of the light if it is determined to be below the target intensity.

[0426] Accordingly, the device (200) can simultaneously ensure energy efficiency and user convenience by detecting both the user absence state and the re-entry state and automatically adjusting the lighting brightness and power state.

[0427] As a result, the device (200) can automatically control the brightness and power status of the lighting according to the user's movement state and activity intensity, thereby improving energy efficiency and simultaneously ensuring visual comfort and space utilization convenience for the user.

[0428] FIG. 9 is a flowchart illustrating the process of controlling lighting according to power consumption in one embodiment.

[0429] Referring to FIG. 9, first, in step S901, the device (200) can check the power usage of a similar place having the same area and the same use as the place.

[0430] To this end, the database provided in the device (200) may include a location database in which information about each lighting installation location is stored, and the location database may store the area, use, power equipment capacity, power consumption history, average power usage by time of day, day, week, and month, and maximum and minimum consumption patterns, etc., mapped for each location.

[0431] Specifically, the device (200) can identify the area (e.g., in m² units) and use (e.g., office, commercial facility, residential space, etc.) of the place where the lighting (300) to be controlled is installed, and based on this, classify places with the same use within the place database that have an area error range of ±10% as similar places.

[0432] In addition, if necessary, auxiliary conditions such as the age of the building, occupancy, number of floors, and the presence of heating and cooling facilities may be additionally considered to improve the accuracy of classifying similar places.

[0433] After that, the device (200) can calculate, for a plurality of classified similar places, the average daily power consumption over the past 7 days, the average weekly power consumption over the past 4 weeks, and the average power consumption by time period corresponding to the current time period (e.g., 8 to 10 a.m., 1 to 3 p.m., etc.).

[0434] At this time, the average power consumption by time of day can be calculated based on historical data corresponding to the same time of day on the same day of the week (e.g., the average value of Monday morning from 8 to 10 a.m. over the past two weeks), thereby correcting for deviations caused by differences in season, work patterns, and operating time zones.

[0435] The above power consumption history and average usage data can be collected from a smart meter, an energy management server (EMS), or a cloud-based energy data platform at intervals of 10 minutes to 1 hour, and the device (200) can receive this data through API communication, MQTT protocol, or an HTTPS-based data linkage module, store it in an internal database, and calculate the average value and standard deviation using a statistics module.

[0436] Accordingly, the device (200) can use the average power consumption of similar locations by time period derived by the above process as a reference value to perform a comparative evaluation with the power consumption of the current location in real time or at a regular interval (e.g., every 30 minutes), and as a result, if the power consumption of the current location exceeds the reference value by more than a threshold ratio (e.g., +20%), it can determine that the energy is in an excessive consumption state.

[0437] In step S902, the device (200) can check the power usage of the location.

[0438] To this end, the device (200) can receive real-time power data from a smart meter or power measurement module installed at the location, and the power data may include items such as power consumption (W), current (A), voltage (V), power factor (PF), and energy consumption (kWh).

[0439] Specifically, the device (200) aggregates and stores power data collected at regular intervals (e.g., 10 minutes, 30 minutes, or 1 hour) on an hourly, daily, and weekly basis, and can check both real-time power usage within the last 24 hours and average usage over the last 7 days.

[0440] In addition, the device (200) can collect not only the power consumption of the lighting itself but also the power consumption of other electronic devices (e.g., ventilation fans, air purifiers, etc.) connected to the same circuit or the same distribution board through an energy monitoring module linked to the lighting (300).

[0441] At this time, the device (200) stores the power data along with time information in a power usage history table within a database and can correct instantaneous spikes or measurement noise by applying a moving average or exponential smoothing of the power consumption for each time period.

[0442] The corrected data can be compared 1:1 with the average power consumption of similar locations derived in step S901 for the same time interval (e.g., same day of the week, same time period).

[0443] In step S903, the device (200) can check whether it is in an overpowered state.

[0444] Specifically, the device (200) can compare the average power consumption by time of a similar location calculated in step S901 with the real-time or recent cumulative power consumption of the current location identified in step S902 by aligning them in the same time interval unit (e.g., recent 30 minutes, 1 hour, or based on the same time interval of the same day).

[0445] That is, the device (200) can calculate the difference in power consumption through {(average power consumption of a place according to the same time interval unit) - (average power consumption of a similar place according to the same time interval unit)} ÷ (average power consumption of a similar place according to the same time interval unit) x 100%, and the device (200) can determine whether the place is in a state of excessive power consumption by determining whether the calculated difference in power consumption exceeds a preset ratio (e.g., 10%, 20%, 30%).

[0446] In addition, the device (200) can apply weight correction in addition to simple absolute value comparison.

[0447] For example, for workspaces, you can set it so that a margin of error of ±10% is allowed for the standard usage during peak hours (e.g., 9 a.m. to 6 p.m.), and a margin of error of ±5% is applied during inactive hours (e.g., 10 p.m. to 6 a.m.).

[0448] Through this, the device (200) can prevent excessive false positives by reflecting the energy usage characteristics by time period.

[0449] In addition, the device (200) can also perform a comparison with the past weekly average power usage (P_hist) for the same location to additionally determine whether the current usage has increased compared to the past average.

[0450] For example, if power consumption increases by more than 25% compared to the average power consumption on the same day of the week over the past four weeks, it can be determined as an abnormal consumption pattern rather than a simple peak timing.

[0451] If it is confirmed in step S903 that there is an excessive power usage state, in step S904, the device (200) can check whether the illuminance of the place is higher than the standard illuminance.

[0452] Specifically, if the device (200) confirms that there is an excessive power usage state as a result of comparing the power usage of a place with the power usage of a similar place, the device (200) can receive real-time illuminance data from an illuminance sensor or a composite environment sensor module installed at the place, and the illuminance data can be expressed in units of lux.

[0453] At this time, the device (200) can collect illuminance data at intervals of 1 to 10 seconds to calculate an average value and perform signal smoothing processing based on a moving average or a low pass filter (LPF) to eliminate variations caused by instantaneous light interference (e.g., opening a window, temporary reflection, etc.).

[0454] In addition, the standard illuminance table provided in the device (200) may store standard illuminance values ​​according to the use of the place.

[0455] For example, standard illuminance may be defined according to KS A 3011 (standards for indoor lighting), IES (Institute of Illuminating Engineers) recommended standards, or preset illuminance values ​​based on the user environment, and may be pre-registered by the use of the place, such as office: 400~500 lux, commercial space: 300~400 lux, and residential space: 200~300 lux. Meanwhile, it is not limited to these.

[0456] That is, the device (200) can determine whether the illuminance of the location is higher than the standard illuminance by comparing the current illuminance confirmed through an illuminance sensor installed at the location with a preset standard illuminance.

[0457] If it is confirmed in step S904 that the illuminance of the place is higher than the standard illuminance, in step S905, the device (200) can cut off the power to the light.

[0458] Specifically, if the device (200) determines that the location is already sufficiently bright when it is confirmed that the illuminance of the location is higher than the standard illuminance, maintaining such an unnecessary lighting state may result in wasted power.

[0459] Accordingly, the device (200) can control the power of the light (300) to automatically cut off power in order to save power and improve the energy efficiency of the system.

[0460] If it is confirmed in step S904 that the illuminance of the place is not higher than the standard illuminance, in step S906, the device (200) can check whether a display device in the place is being used.

[0461] Specifically, if the device (200) determines that the illuminance of the location is not higher than the standard illuminance, the device (200) can check the device identification information (Device ID) and power status information (Power Status) for the display device (e.g., television, monitor, projector, electronic display board, other lighting, etc.) installed at the location.

[0462] To this end, the database provided in the device (200) may store device identification information (Device ID) and power status information (Power Status) for a display device installed at a location, matched with each location.

[0463] That is, the device (200) can determine whether each display device is in use by checking whether it is connected to the same network (e.g., Wi-Fi, Bluetooth, Zigbee, or local Ethernet network) or by receiving the real-time power consumption value (P_display) of the display device installed at the location from a smart plug, power monitoring sensor, or digital power metering module.

[0464] For example, the device (200) can determine that the display device (e.g., television) is currently in use if the power consumption value of the display device exceeds a preset power operation threshold (e.g., 10W or more) and, conversely, can recognize that the display device is turned off if the power consumption value is below the threshold or if the network connection is not maintained for a certain period of time.

[0465] In addition, the device (200) may also directly detect whether the display screen emits light through an image output sensor (e.g., a camera-based light intensity detection module or an RGB image analysis module).

[0466] In this case, the device (200) can analyze the average brightness, color change rate, frame refresh rate, etc. within the screen area of ​​the display device to additionally determine whether the device is actually outputting a video signal.

[0467] Accordingly, the device (200) can precisely determine whether a display device in a location is currently in use through the above procedure, and the result of this determination can be used as an input value for the lighting brightness and color temperature adjustment determination logic performed in a subsequent step (S907).

[0468] If it is confirmed in step S906 that a display device in the location is used, in step S907, the device (200) can reduce the brightness of the lighting by a first standard ratio and reduce the color temperature of the lighting by a second standard ratio.

[0469] Here, the first standard ratio and the second standard ratio are each preset values ​​and may vary depending on the embodiment.

[0470] Specifically, the device (200) may determine that there is no need to maintain excessive lighting in a place when a display device (e.g., television, electronic display, monitor, etc.) is outputting a video signal while the illuminance of the place is low.

[0471] Accordingly, the device (200) can automatically lower the brightness of the light by a preset first standard ratio (e.g., 10 to 30%) in consideration of the amount of light emitted from the display device, that is, to reduce unnecessary light redundancy.

[0472] Additionally, the device (200) can lower the color temperature of the lighting by a preset second standard ratio (e.g., 5 to 20%) to minimize visual fatigue and improve visibility in the viewing environment.

[0473] At this time, the first standard ratio and the second standard ratio may vary depending on the use of the place, the type of lighting, or the user's preferred environment setting value, and may also be automatically corrected by environment setting information or training data within the system's database.

[0474] Through such a lighting brightness and color temperature adjustment procedure, the device (200) can reduce unnecessary power consumption when using the display device and simultaneously improve visual comfort and energy efficiency in an environment for viewing images or displaying information.

[0475] If it is confirmed in step S906 that the display device in the location is not being used, in step S908, the device (200) can reduce the brightness of the light by a first standard ratio.

[0476] Specifically, the device (200) may recognize that the place corresponds to a relatively low-activity or inactive state when it is determined that a display device (e.g., television, electronic display, monitor, etc.) is not being used while the illuminance of the place is lower than the standard illuminance.

[0477] Accordingly, the device (200) can control the brightness of the light (300) to decrease in steps by a preset first standard ratio (e.g., 10 to 30%) to prevent unnecessary power waste.

[0478] Additionally, the device (200) can control the illumination so as not to lower the illumination level below a minimum illumination limit value (L_min) for ensuring safety or securing movement paths by considering the minimum illumination standard of the surrounding environment when adjusting the lighting brightness.

[0479] Through this, the device (200) can achieve efficient energy savings without completely turning off the lighting environment.

[0480] Accordingly, the device (200) can maintain a comfortable and safe indoor lighting environment while reducing unnecessary power consumption by comprehensively determining the usage status and lighting conditions of the location even when the display device is not in use.

[0481] Meanwhile, if it is confirmed in step S903 that there is no excessive power usage, in step S909, the device (200) can maintain the lighting.

[0482] Specifically, if the device (200) compares the power usage of a location with the power usage of a similar location and confirms that it is not in a state of excessive power usage, the device (200) determines that the location is in a normal energy usage state and can maintain the existing control state without changing the brightness, color temperature, or power state of the lighting.

[0483] In addition, the device (200) can prevent unnecessary control repetition or lighting flicker by periodically checking whether power usage is continuously maintained within a normal range for a certain period (e.g., one hour, one day, or one week).

[0484] Through this procedure, the device (200) can simultaneously achieve energy efficiency and user convenience while minimizing unnecessary lighting control operations.

[0485] As a result, the device (200) can prevent unnecessary power waste and simultaneously improve energy efficiency and user visual comfort by comprehensively determining the power consumption, illuminance, and whether the display device is in use of the location and controlling the brightness and color temperature of the lighting according to the situation.

[0486] FIG. 10 is an example diagram of the configuration of a device according to one embodiment.

[0487] A device (200) according to one embodiment includes a processor (210) and a memory (220). The processor (210) may include at least one device described with reference to FIGS. 1 through 9 or may perform at least one method described with reference to FIGS. 1 through 9. A person or organization using the device (200) may provide services related to some or all of the methods described with reference to FIGS. 1 through 9.

[0488] The memory (220) may store information related to the methods described above or store a program in which the methods described below are implemented. The memory (220) may be volatile memory or non-volatile memory.

[0489] The processor (210) can execute a program and control the device (200). The code of the program executed by the processor (210) can be stored in memory (220). The device (200) can be connected to an external device (e.g., a personal computer or a network) through an input / output device (not shown in the drawing) and can exchange data via wired or wireless communication. The processor (210) can be operatively connected to the components of the device (200). The processor (210) can load commands or data received from other components of the device (200) into memory (220), process the commands or data stored in memory (220), and store the resulting data.

[0490] Additionally, the device (200) may further include a communication circuit. The communication circuit may establish a communication channel with an external device (e.g., a user's terminal (100)) and transmit and receive various data with the external device. According to various embodiments, the communication circuit may include a cellular communication module and be configured to be connected to a cellular network (e.g., 3G, LTE, 5G, Wibro, or Wimax). According to various embodiments, the communication circuit may include a short-range communication module and transmit and receive data with the external device using short-range communication (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy (BLE), UWB), but is not limited thereto.

[0491] That is, unlike conventional automatic lighting technology based on simple illuminance detection or motion detection, the user recognition-based personalized smart lighting control method according to the present invention adopts a stepwise structure of 'Sensing → Identification → Pattern Analysis → Control', thereby improving user recognition accuracy to approximately 98% compared to a simple environment-responsive lighting control system and shortening the response speed to 0.5 seconds or less, thereby significantly improving the responsiveness perceived by the user.

[0492] In addition, the present invention can prevent unnecessary excessive illumination and reduce the occurrence of glare by more than 40% by automatically adjusting lighting brightness, color temperature, direction, and illumination angle.

[0493] In particular, by combining users' biometric information and lighting control history to analyze long-term patterns, it is possible to precisely reflect individual lighting preferences and response cycles that were difficult to implement with existing short-term detection methods.

[0494] Accordingly, even if multiple users exist within the same space, differentiated lighting control can be performed based on each user's location and status.

[0495] In addition, the present invention can simultaneously realize context awareness and personalized light response by fusing real-time detection information and accumulated user data, thereby reducing visual fatigue of the user and improving concentration and psychological stability.

[0496] Furthermore, since this invention is applicable not only to private spaces but also to various spatial environments where multiple users live or work, such as conference rooms, hospitals, schools, and public facilities, it has very high potential for industrial application as a next-generation smart lighting control technology capable of simultaneously improving user satisfaction and maximizing energy efficiency.

[0497] The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0498] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0499] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0500] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0501] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

Claim 1 A user recognition-based personalized smart lighting control method performed by a device, comprising: a step of recognizing a user’s access to a place where lighting is installed; a step of verifying user identification information corresponding to said user; a step of obtaining lighting control information matched with said user identification information; and a step of controlling said lighting according to said lighting control information.The step of recognizing the user's access includes at least one of the following: an operation of recognizing the user's access based on the user's terminal being included within the communication range of the place where the lighting is installed; an operation of recognizing the user's access based on receiving that a wireless signal has been exchanged between the lighting and the user's terminal; and an operation of recognizing the user's access based on the recognition of at least one biometric information among the user's voice, fingerprint, or face through a sensor provided at the place where the lighting is installed. The step of obtaining lighting control information matched with the user identification information includes, based on the user identification information, an operation of confirming the time the lighting has operated in match with the user; an operation of confirming whether the time the lighting has operated is longer than a preset target time; if it is confirmed that the time the lighting has operated is longer than the target time, an operation of analyzing the number of lighting control changes, the lighting control change cycle, the deviation of the control interval, or the pattern of the timing of the lighting control command occurrence to confirm whether the lighting control is changed periodically by time period; if it is confirmed that the lighting control is changed periodically by time period, an operation of generating lighting control information including the most frequently used color, color temperature, and brightness for the time period including the current time point; and the lighting A user recognition-based personalized smart lighting control method comprising: generating lighting control information including the color, color temperature, and brightness that the lighting has maintained for the longest time when it is confirmed that the control is not periodically changed over time; and, when it is confirmed that the time the lighting has operated is not longer than the target time, identifying a group of similar users having attributes similar to the user by verifying the age, gender, and occupation of the user through the user identification information, and generating lighting control information including the color, color temperature, and brightness that have been most frequently used for the group of similar users. Claim 2 delete Claim 3 In claim 1, the method further comprises: a step of recognizing a user’s access to a place where lighting is installed; subsequently, a step of additionally recognizing the access of another user; a step of comparing lighting control information corresponding to each of the user and the other user to determine whether the similarity between color, color temperature, and brightness is above a preset threshold level; if the similarity is confirmed to be above the threshold level, a step of calculating the median value of color, color temperature, and brightness from the lighting control information of the user and the other user and generating supplementary lighting control information based thereon; a step of controlling the lighting according to the supplementary lighting control information; if the similarity is confirmed to be below the threshold level, a step of generating a priority score for each of the user and the other user using the distance from the lighting, planned time of stay, age, and energy consumption according to the lighting control information during the same period; and a step of controlling the lighting based on the lighting control information of the user with the highest priority score, and a step of controlling the lighting according to the lighting control information. Subsequently, a step of tracking the user's gaze through an eye-tracking camera installed at the above location; a step of checking whether the user's gaze movement trajectory tracked for a predetermined period based on the current time does not deviate from a predetermined gaze stability range; and a step of, if it is confirmed that the gaze movement trajectory does not deviate from the gaze stability range, setting a lighting concentration zone according to the gaze movement trajectory and controlling the illumination angle so that the lighting is directed toward the lighting concentration zone.A user recognition-based personalized smart lighting control method comprising: a step of not controlling the illumination angle of the lighting when it is confirmed that the gaze movement trajectory deviates from the gaze stability range; and a step of controlling the illumination angle of the lighting so that the lighting is directed toward the lighting concentration zone, wherein the step comprises: generating a first vector connecting the user's eye position and the center position of the lighting concentration zone; generating a second vector connecting the center position of the light source of the lighting and the center position of the lighting concentration zone; calculating an angle between the first vector and the second vector; checking whether the angle is included within a preset angle range; if it is confirmed that the angle is included within the angle range, calculating an avoidance angle based on the light intensity of the lighting and the distance between the lighting and the user to prevent the lighting from being directed directly toward the user's eyes; correcting the illumination angle of the lighting based on the avoidance angle; and if it is confirmed that the angle is not included within the angle range, controlling the illumination angle of the lighting so that the lighting is directed toward the center position of the lighting concentration zone. Claim 4 A computer-readable recording medium storing instructions that cause a processor to execute a user recognition-based personalized smart lighting control method according to either Paragraph 1 or Paragraph 3. Claim 5 Memory; and a processor connected to the memory; wherein the processor is configured to recognize user access to a place where lighting is installed, verify user identification information corresponding to the user, obtain lighting control information matched with the user identification information, and control lighting according to the lighting control information; wherein the processor performs at least one of the following operations to recognize user access to a place where lighting is installed: an operation of recognizing user access based on the user's terminal being included within the communication range of the place where lighting is installed; an operation of recognizing user access based on receiving that a wireless signal has been exchanged between the lighting and the user's terminal; and an operation of recognizing user access based on the recognition of at least one biometric information among the user's voice, fingerprint, or face through a sensor provided at the place where lighting is installed; and wherein, to obtain lighting control information matched with the user identification information, the processor checks the time the lighting has operated in accordance with the user based on the user identification information, checks whether the time the lighting has operated is longer than a preset target time, and if it is confirmed that the time the lighting has operated is longer than the target time, the number of lighting control changes, the lighting control change cycle, and the control interval By analyzing the deviation or the pattern of the timing of the lighting control command generation, it is determined whether the lighting control changes periodically over time; if it is confirmed that the lighting control changes periodically over time, lighting control information is generated for the time period including the current time, including the most frequently used color, color temperature, and brightness; if it is confirmed that the lighting control does not change periodically over time, lighting control information is generated, including the color, color temperature, and brightness for which the lighting was maintained for the longest time; and if it is confirmed that the time the lighting operated is not longer than the target time, the user's age, gender,A user recognition-based personalized smart lighting control device that verifies an occupation to identify a group of similar users having attributes similar to the user, and generates lighting control information including the most frequently used color, color temperature, and brightness for the group of similar users. Claim 6 A user's terminal owned by a user living or active in a place where lighting is installed; lighting that is automatically controlled according to lighting control information matched to user identification information, and includes a power supply module, a light source module, a control module, and a communication module; and a device including memory and a processor, which is connected to the user's terminal and the lighting through a network environment; wherein the device is configured to recognize the user's access to a place where the lighting is installed, verify user identification information corresponding to the user, obtain lighting control information matched with the user identification information, and control the lighting according to the lighting control information; wherein, to recognize the user's access to a place where the lighting is installed, the device performs at least one of the following operations: recognizing the user's access based on the user's terminal being included within the communication range of the place where the lighting is installed; recognizing the user's access based on receiving that a wireless signal has been exchanged between the lighting and the user's terminal; and recognizing the user's access based on the recognition of at least one biometric information among the user's voice, fingerprint, or face through a sensor provided at the place where the lighting is installed; wherein, to obtain lighting control information matched with the user identification information, the device checks the time the lighting has operated in match with the user based on the user identification information, checks whether the time the lighting has operated is longer than a preset target time, and if it is confirmed that the time the lighting has operated is longer than the target time, changes the lighting control. By analyzing the pattern of the number of times, the lighting control change cycle, the deviation of the control interval, or the timing of the occurrence of lighting control commands, it is determined whether the lighting control changes periodically over time, and if it is confirmed that the lighting control changes periodically over time, lighting control information is generated including the most frequently used color, color temperature, and brightness for the time period including the current time point.A user recognition-based personalized smart lighting control system that, if it is confirmed that the lighting control is not periodically changed by time interval, generates lighting control information including the color, color temperature, and brightness for which the lighting has been maintained for the longest time, and if it is confirmed that the time the lighting has operated is not longer than the target time, identifies a group of similar users having attributes similar to the user by verifying the age, gender, and occupation of the user through the user identification information, and generates lighting control information including the color, color temperature, and brightness most frequently used for the group of similar users.

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