Knx protocol-based building control system using ai facial recognition

Abstract

A building control system based on the KNX protocol using AI facial recognition is disclosed. The system comprises: a camera device that captures the face of a user within each space of a building; a building sensor that detects the indoor environmental conditions of each space within the building in real time; a user terminal that sets building load values ​​for each space within the building; and a cloud-based building control server that monitors the environmental conditions detected in real time by the building sensor, remotely controls the building load within the user's space according to the user load usage pattern captured by the camera device, and remotely controls the building load according to the building load value set by the user terminal. According to the above-described building control system based on the KNX protocol using AI facial recognition, the system is configured to recognize the user through facial recognition of the user in each space of the building and to create a user-customized indoor environment by learning the user's existing building load usage pattern, thereby having the effect of automatically creating an indoor environment for the user in the user's space. In particular, by configuring the system to integrate control various building loads from different manufacturers using the KNX protocol on a cloud server, it has the effect of automatically adjusting building load values ​​even for building loads using different manufacturers and different protocols.

Description

Technology Field

[0001] The present invention relates to a KNX protocol-based building control system, and more specifically, to a KNX protocol-based building control system using AI facial recognition. Background Technology

[0003] Recently, there is a trend of smart buildings becoming increasingly intelligent as AI algorithms are applied.

[0004] In many buildings, cooling, heating, and dehumidification systems are all integrated and operated as a single unit. However, because these devices are manufactured by different companies, there are significant challenges in integrating their control using smart building AI algorithms.

[0005] Meanwhile, each user has different preferences regarding temperature, humidity, and other factors for the indoor environment within a building. However, the reality is that existing smart buildings create indoor environments without taking these user preferences into account.

[0006] There is a growing demand for means to create user-based indoor environments for the numerous users within buildings. Prior art literature

[0008] Published Patent Application 10-2023-0053161 Registered Patent Application 10-1875488 The problem to be solved

[0009] The objective of the present invention is to provide a KNX protocol-based building control system using AI facial recognition. means of solving the problem

[0011] A KNX protocol-based building control system using AI facial recognition according to the purpose of the present invention described above may be configured to include a building sensor that detects the indoor environmental conditions of each space within the building in real time; and a user terminal that sets a building load value for each space within the building.

[0012] Here, it may be configured to further include a cloud-based building control server that remotely controls the building load according to the building load value set at the user terminal.

[0013] And the cloud-based building control server can be configured to monitor the environmental status detected in real time by the building sensor. Effects of the invention

[0015] According to the KNX protocol-based building control system using the aforementioned AI facial recognition, it is configured to recognize users through facial recognition of users in each space within the building and learn the usage patterns of users regarding existing building loads to create a customized indoor environment for users, thereby having the effect of automatically creating an indoor environment for users in their spaces.

[0016] In particular, by configuring integrated control of various building loads from different manufacturers using the KNX protocol on a cloud server, it is possible to automatically adjust building load values ​​even for building loads using different manufacturers and different protocols.

[0017] Meanwhile, by configuring the building load to control it based on the presence or absence of users in each building space, it has the effect of contributing to energy savings for the entire building. Brief explanation of the drawing

[0019] FIG. 1 is a block diagram of a KNX protocol-based building control system using AI facial recognition according to an embodiment of the present invention. Specific details for implementing the invention

[0020] The present invention is susceptible to various modifications and may have various embodiments; therefore, specific embodiments are illustrated in the drawings and described in detail in the specific details for carrying out the invention. However, this is not intended to limit the invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0021] Terms such as first, second, A, B, etc., may be used to describe various components, but the components should not be limited by these terms. The terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0022] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0023] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to indicate the presence 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.

[0024] 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 present invention pertains. 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.

[0025] Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings.

[0026] FIG. 1 is a block diagram of a KNX protocol-based building control system using AI facial recognition according to an embodiment of the present invention.

[0027] Referring to FIG. 1, a KNX protocol-based building control system using AI facial recognition according to one embodiment of the present invention may be configured to include a camera device (100), a building sensor (200), a user terminal (300), and a cloud-based building control server (400).

[0028] The detailed composition is explained below.

[0029] The camera device (100) can be configured to photograph the user's face in each space within the building.

[0030] The building sensor (200) can be configured to detect the indoor environmental conditions of each space within the building in real time.

[0031] The user terminal (300) can be configured to set building load values ​​for each space within the building.

[0032] A cloud-based building control server (400) can be configured to remotely control a building load (10) within a user's space in accordance with the user's load usage pattern captured by a camera device (100) while monitoring the environmental conditions detected in real time by a building sensor (200), and to remotely control the building load (10) according to a building load value set by a user terminal (300).

[0033] The building sensor (200) may be configured to include a temperature sensor (201), a humidity sensor (202), an illuminance sensor (203), and an indoor air quality detection sensor (204).

[0034] The detailed composition is explained below.

[0035] The temperature sensor (201) can be configured to detect the temperature of each space within the building in real time.

[0036] The humidity sensor (202) can be configured to detect the humidity of each space within the building in real time.

[0037] The light sensor (203) can be configured to detect the light level of each space within the building in real time.

[0038] The indoor air quality detection sensor (204) can be configured to detect the indoor air quality of each space within the building in real time.

[0039] A cloud-based building control server (400) may be configured to include a facial recognition module (401), a facial information database (402), a facial information comparison module (403), a facial information addition module (404), a user-specific module (405), a cooling setting value collection module (406), a heating setting value collection module (407), a dehumidification setting value collection module (408), a lighting setting value collection module (409), a ventilation setting value collection module (410), a load setting value storage module (411), a spatial information database (412), a user load usage pattern learning module (413), a user load usage pattern database (414), a real-time indoor environment status collection module (415), a user-based load automatic control module (416), and a multiple user load usage average pattern calculation module (417).

[0040] The detailed composition is explained below.

[0041] The face recognition module (401) can be configured to recognize a face captured by the camera device (100) and generate face information.

[0042] The facial information database (402) can be configured to store each user's facial information in advance.

[0043] The facial information comparison module (403) can be configured to compare the facial information generated by the facial recognition module (401) with the facial information of each user that is pre-stored in the facial information database (402).

[0044] The facial information addition module (404) may be configured to add and store the facial information generated by the facial recognition module (401) in the facial information database (402) when facial information identical to the facial information generated by the facial recognition module (401) is not found in the facial information database (402) as a result of comparison by the facial information comparison module (403).

[0045] The user-specific module (405) can be configured to specify the user of the searched facial information as the user of the corresponding space when facial information identical to the facial information generated by the facial recognition module (401) is searched in the facial information database (402) of the comparison result of the facial information comparison module (403).

[0046] The cooling setting value collection module (406) can be configured to collect the cooling setting value of the cooling device (11) installed in the space in real time.

[0047] The heating setting value collection module (407) can be configured to collect the heating setting value of the heating device (12) installed in the space in real time.

[0048] The dehumidification setting value collection module (408) can be configured to collect the dehumidification setting value of the dehumidification device (13) installed in the space in real time.

[0049] The lighting setting value collection module (409) can be configured to collect the lighting setting value of the lighting device (14) installed in the space in real time.

[0050] The ventilation setting value collection module (410) can be configured to collect the ventilation setting value of the ventilation device (15) installed in the space in real time.

[0051] The load setting value storage module (411) can be configured to store the cooling setting value collected in real time from the cooling setting value collection module (406), the heating setting value collected in real time from the heating setting value collection module (407), the dehumidification setting value collected in real time from the dehumidification setting value collection module (408), the lighting setting value collected in real time from the lighting setting value collection module (409), and the ventilation setting value collected in real time from the ventilation setting value collection module (410) as the load storage value of the space.

[0052] The spatial information database (412) can be configured to store spatial information for each space of the building in advance.

[0053] The user load usage pattern learning module (413) may be configured to generate a user load usage pattern by referring to spatial information previously stored in the spatial information database (412) and learning the load setting value of the space stored in the load setting value storage module (411) for the user specified in the user-specific module (405).

[0054] The user load usage pattern database (414) can be configured to store user load usage patterns generated by the user load usage pattern learning module (413).

[0055] The indoor environment condition real-time collection module (415) can be configured to collect the indoor environment condition of each space within the building in real-time detected by the building sensor (2000).

[0056] The user-based load automatic control module (416) can be configured to remotely control the building load (10) within the user's space according to the user load usage pattern stored in the user load usage pattern database (414).

[0057] The multiple user load usage average pattern calculation module (417) can be configured to calculate the multiple user load usage average pattern for each user load usage pattern of multiple users in real time when there are multiple users in the space.

[0058] The user-based load automatic control module (416) can be configured to provide feedback control of the building load (10) according to the indoor environment conditions of the space collected in real time by the indoor environment conditions real-time collection module (415).

[0059] The user-based load automatic control module (416) can be configured to remotely control the refrigeration unit (11), heating unit (12), dehumidification unit (13), lighting unit (14) and ventilation unit (15) based on the KNX protocol.

[0060] The user-based load automatic control module (416) can be configured to remotely control the building load (10) according to the building load value set at the user terminal (300).

[0061] Meanwhile, the cloud-based building control server (400) may be configured to further include a spatial load usage pattern learning module (not shown), a facial information / user load usage pattern encryption module (not shown), a blockchain generation module (not shown), a blockchain verification module (not shown), a facial information / user load usage pattern decryption module (not shown), and a user load usage pattern update module (not shown).

[0062] Here, a spatial load usage pattern learning module (not shown) may be configured to refer to spatial information stored in advance in a spatial information database (412) and to generate a spatial load usage pattern according to the use of the referenced spatial information.

[0063] For example, if the space is a special space for patients, such as a hospital room, the use of the space information is more important than the users of the space. Accordingly, a space load usage pattern learning module (not shown) can be configured to generate a space load usage pattern according to the use of the space information corresponding to the space.

[0064] The facial information / user load usage pattern encryption module (not shown) may be configured to encrypt the user load usage pattern stored in the user load usage pattern database (414) for the facial information stored in the facial information database (402) and the user of the facial information.

[0065] The blockchain generation module (not shown) can be configured to store the facial information and user load usage patterns encrypted by the facial information / user load usage pattern encryption module (not shown) in the blockchain.

[0066] Meanwhile, if the user load usage pattern of the facial information recognized by the facial recognition module (401) is not stored in the user load usage pattern database (414), the blockchain verification module (not shown) may be configured to verify the blockchain and extract the user load usage pattern of the facial information recognized by the facial recognition module (401).

[0067] And the facial information / user load usage pattern decryption module (not shown) can be configured to decrypt the user load usage pattern extracted from the blockchain verification module (not shown).

[0068] And the user load usage pattern update module (not shown) can be configured to update and store the user load usage pattern database (414) using the user load usage pattern extracted from the blockchain verification module (not shown).

[0069] And the user-based load automatic control module (416) can be configured to remotely control the building load within the user's space using the user load usage pattern that is updated and stored in the user load usage pattern update module (not shown).

[0070] In other words, even if user load usage patterns are not stored on a specific building's cloud-based building control server (not shown), user load usage patterns from other buildings stored on the blockchain can be queried and utilized. Consequently, the user's indoor environment can be applied even when the user visits a different building, based on the user load usage patterns from a specific building.

[0071] On the other hand, the user-based load automatic control module (416) can be configured to apply an energy saving mode to reduce the energy of the entire building when the user is not in a specific space.

[0072] On the other hand, the user carbon emission index calculation module (not shown) may be configured to calculate the user carbon emission index for each user by calculating the amount of energy used according to the user load usage pattern generated by the user load usage pattern learning module (413).

[0073] At this time, if the user carbon emission index calculated by the user carbon emission index calculation module (not shown) exceeds a predetermined standard, the user-based load automatic control module (416) may be configured to automatically reduce and apply the user load usage pattern according to a predetermined ratio so that the user carbon emission index does not exceed the predetermined standard.

[0074] Accordingly, if energy consumption due to a user's load usage pattern appears somewhat excessive, the overall energy consumption is reduced by automatically reducing the user's load usage pattern, thereby preventing the user from overconsuming energy. Ultimately, this can prevent waste of the building's total energy.

[0075] On the other hand, if a user has a cold or other characteristic disease, the user terminal (300) may be configured to receive the cold or the disease from the user and may be configured to provide the received cold or the disease in real time to the user-based load automatic control module (416).

[0076] And the user-based load automatic control module (416) can be configured to receive a cold or the corresponding disease in real time from the user terminal (300), and to automatically modify the user's user load usage pattern in consideration of the cold or the corresponding disease received in real time, thereby remotely controlling the building load (10).

[0077] For example, if you catch a cold, it can be configured to increase the heating temperature and humidity, and to ventilate frequently.

[0078] On the other hand, the user terminal (300) can be configured to provide the user's audio playlist to the user-based load automatic control module (416) in real time.

[0079] And the user-based load automatic control module (416) can be configured to receive a user’s audio playlist in real time from the user terminal (300), play the audio from the audio playlist received in real time, and output it through the speaker of the space.

[0080] Although the invention has been described with reference to the above embodiments, those skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as set forth in the following claims. Explanation of the symbols

[0082] 100: Camera device 200: Building Sensor 201: Temperature sensor 202: Humidity sensor 203: Light sensor 204: Indoor air quality detection sensor 300: User terminal 400: Cloud-based Building Control Server 401: Facial recognition module 402: Facial Information Database 403: Facial Information Contrast Module 404: Facial Information Addition Module 405: User-specific module 406: Cooling setting value collection module 407: Heating Setpoint Collection Module 408: Dehumidification setting value collection module 409: Lighting Setpoint Collection Module 410: Ventilation Setpoint Collection Module 411: Load setting value storage module 412: Spatial Information Database 413: User Load Usage Pattern Learning Module 414: User Load Usage Patterns Database 415: Real-time Indoor Environment Status Collection Module 416: User-based automatic load control module 417: Module for Calculating Average Patterns of Multiple User Load Usage

Claims

Claim 1 A camera device for capturing a user's face within each space within a building; a building sensor for detecting the indoor environmental conditions of each space within the building in real time; a user terminal for setting building load values ​​for each space within the building; and a cloud-based building control server that monitors the environmental conditions detected in real time by the building sensor, remotely controls the building load within the user's space according to the user load usage pattern captured by the camera device, and remotely controls the building load according to the building load value set by the user terminal, wherein the cloud-based building control server comprises: a face recognition module that recognizes the face captured by the camera device and generates face information; a face information database in which each user's face information is stored in advance; a face information comparison module that compares the face information generated by the face recognition module with each user's face information stored in advance in the face information database; a face information addition module that, if face information identical to the face information generated by the face recognition module is not found in the face information database as a result of comparison by the face information comparison module, adds and stores the face information generated by the face recognition module in the face information database; and, if the face information in the face information database as a result of comparison by the face information comparison module A user identification module that identifies the user of the searched facial information as the user of the corresponding space when facial information identical to the facial information generated by the recognition module is searched; a cooling setting value collection module that collects cooling setting values ​​of a cooling device installed in the space in real time; a user load usage pattern learning module that generates the user load usage pattern by referencing space information previously stored in a space information database and learning the load setting values ​​of the space stored in a load setting value storage module for the user identified by the user identification module; and a user load usage pattern database in which the user load usage pattern generated by the user load usage pattern learning module is stored.A KNX protocol using AI facial recognition, characterized by comprising: a real-time indoor environment status collection module that collects in real-time indoor environment status of each space within a building detected in real-time by the building sensor; a user-based automatic load control module that remotely controls the building load within the user's space according to user load usage patterns stored in the user load usage pattern database; and a multiple user load usage average pattern calculation module that calculates in real-time the multiple user load usage average pattern for each user load usage pattern of the multiple users when there are multiple users within the space; wherein the user-based automatic load control module is configured to provide feedback control of the building load according to the indoor environment status of the space collected in real-time by the real-time indoor environment status collection module; wherein the user-based automatic load control module is configured to remotely control a refrigeration device, a heating device, a dehumidification device, a lighting device, and a ventilation device based on the KNX protocol; wherein the user-based automatic load control module is configured to remotely control the corresponding building load according to a building load value set at the user terminal; and wherein the user-based automatic load control module is configured to apply an energy saving mode to the building load of the space when no user is present in a predetermined space of the building. Base Building Control System.; Claim 2 A building control system based on the KNX protocol using AI facial recognition, characterized in that, in claim 1, it further comprises a user carbon emission index calculation module that calculates the energy consumption based on the user load usage pattern generated by the user load usage pattern learning module and calculates the user carbon emission index for each user, and the user-based load automatic control module is configured to automatically reduce and apply the user load usage pattern according to a predetermined ratio so that the user carbon emission index calculated by the user carbon emission index calculation module does not exceed the predetermined standard when the user carbon emission index exceeds a predetermined standard. Claim 3 delete

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