KR102380397B1 - METHOD FOR MANAGING SMART BUILDING USING IoT SENSOR AND ARTIFICIAL INTELIGENCE - Google Patents

Abstract

A smart building management method using IoT sensors and artificial intelligence is provided. The smart building management method using the IoT sensor and artificial intelligence includes the steps of acquiring data about a building, collected by one or more IoT sensors (S110), pre-processing the acquired data (S120), one or more user feedback Obtaining (S130), pre-processing the obtained user feedback (S140), inputting the pre-processed data and user feedback into an artificial intelligence model (S150), the building equipment from the output of the artificial intelligence model Obtaining data for the control of (S160), controlling the building equipment based on the determined and acquired data (S170) and obtaining user feedback according to the control of the equipment to update the artificial intelligence model Step S180 is included.

Description

Smart building management method using IoT sensor and artificial intelligence

The present invention relates to a smart building management method using an IoT sensor and artificial intelligence.

As artificial intelligence technology emerges and develops, automation systems in various fields are emerging. This is to build a more efficient and systematic system while minimizing the consumption of human resources. With the advent of various IoT sensor modules with more sensitive and advanced technology, an autonomous system based on artificial intelligence is established in managing various facilities in buildings. The technology that can do it is materialized.

On the other hand, the operation of maintenance facilities of a building and various power consumption facilities is a complex process in which a wide variety of factors must be considered, and it is difficult for a facility manager to respond in real time or to establish and execute a long-term building management plan.

Building management includes factors related to the external environment such as temperature, humidity, season, sunlight, and climatic conditions outside the building, as well as temperature, humidity, air quality, Volatile Organic Compounds (VOC), lighting, power consumption, and facilities inside the building. A wide variety of variables must be considered, including the number of users, floating population, or factors related to the internal environment such as the size and location of windows. It is very difficult for humans to directly infer and adjust individual causal relationships about how factors affect the construction of the final internal environment.

Moreover, VOC is a well-known group of substances that cause sick building syndrome (SBS). However, there is a practical difficulty in carrying out the necessary ventilation in the necessary places by considering the humidity and temperature through real-time monitoring as the facility manager resides there.

Due to these practical difficulties, most of the buildings are managed inefficiently and consume electricity inefficiently, resulting in huge social costs. there is something to go

Therefore, we establish a building operation strategy that efficiently manages facilities through the minimum input from facility managers and minimizes the cost and energy consumption required for facility operation while maintaining comfortable facilities inside the building by analyzing the needs and reactions of building users It can be said that there is a social request regarding the establishment of a building management system that can do this.

The technical idea disclosed in the present invention has been created in response to the need for minimization of cost and energy consumption in relation to the development of artificial intelligence and IoT technology and building management.

Registered Patent Publication No. 10-1838554, 2018.03.08

The problem to be solved by the present invention is to provide a smart building management method using an IoT sensor and artificial intelligence.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

A smart building management method using an IoT sensor and artificial intelligence according to an aspect of the present invention for solving the above-described problems includes the steps of acquiring data about a building, collected by one or more IoT sensors (S110), the collection pre-processing the data (S120), obtaining one or more user feedbacks (S130), pre-processing the obtained user feedback (S140), and inputting the pre-processed data and user feedback into an artificial intelligence model (S150), obtaining data for controlling the equipment of the building from the output of the artificial intelligence model (S160), controlling the equipment of the building based on the obtained data (S170) and the equipment and updating the artificial intelligence model by obtaining user feedback according to the control (S180).

In addition, the step (S180) includes the steps of converting the user's feedback obtained in the step (S180) into parameters based on fuzzy logic (S210), and inputting the converted parameters into the artificial intelligence model (S220). ), re-acquiring data for controlling the facilities of the building based on the output value of the artificial intelligence model (S230) and controlling the facilities of the building using the re-obtained data (S240) can do.

In addition, the step (S120) may include a step (S310) of classifying the data on the building, collected by the one or more IoT sensors, into data on the external environment and data on the internal environment (S310). (S140) may include a step (S320) of converting the obtained user feedback into parameters based on fuzzy logic, and in the step (S160), the output of the artificial intelligence is data related to the internal environment , the user's optimal preference state learned from the data on the external environment and user feedback converted into the parameter, and the internal environment is changed to the optimal preference state, learned from the data on the internal environment and the user's optimal preference state It may include a control value related to facility control for

In addition, the step (S120) includes the steps of preprocessing the data obtained in the step (S110) (S410), and inputting the preprocessed data in the step (S410) into the pre-trained second artificial intelligence model (S420). ) and predicting the future power consumption of the building from the output of the second artificial intelligence model (S430).

In addition, in the step (S160), obtaining a plurality of candidate data for controlling the equipment of the building (S510), calculating the power consumption when the equipment of the building is controlled according to each of the plurality of candidate data step (S520), estimating the state of the building when the equipment of the building is controlled according to each of the plurality of candidate data (S530), and the state and power consumption of the building according to each of the plurality of candidate data The method may further include determining one final data based on ( S540 ).

In addition, the step (S180) includes the step of receiving a facility control input from the first user (S610), the step of controlling the facility of the building according to the control input of the first user (S620), and the control of the first user The method may further include updating the artificial intelligence model based on the input (S630).

In addition, the data about the facility, collected in the step (S110), are VOC (Volatile Organic Compunds) concentration, fine dust concentration, carbon dioxide concentration, overall air quality, lighting, natural light, indoor temperature, outdoor temperature, humidity, vibration, building It is characterized in that it is at least one of external climatic conditions, seasons, power consumption of facilities inside the building, the number of users inside the facility, or movement inside the facility, and in the step (S170), the controlled facility is HVAC (Heating, Ventilation and Air) Conditioning) equipment, air conditioning equipment, heating equipment, elevator, security system, lighting equipment, air purifier, window, light shielding equipment, door shielding equipment, humidifier, dehumidifier, equipment for moving internal facilities, firefighting equipment or home appliances It may be characterized by more than one.

An apparatus according to an aspect of the present invention for solving the above problems includes a memory storing one or more instructions and a processor executing the one or more instructions stored in the memory, wherein the processor executes the one or more instructions , a smart building management method using an IoT sensor and artificial intelligence according to the disclosed embodiment is performed.

A computer program according to an aspect of the present invention for solving the above problems is combined with a computer as hardware, and a computer-readable record to perform the smart building management method using the IoT sensor and artificial intelligence according to the disclosed embodiment stored on the medium.

Other specific details of the invention are included in the detailed description and drawings.

By the technology disclosed in the present invention, data on the internal and external environment of a building that affects the internal environment or power consumption of a building can be easily collected, and it can be efficiently Building management can take place.

In addition, even if the facility manager of the building performs minimal manipulation or control, the artificial intelligence that updates itself by autonomous environmental adaptation provides various optimization scenarios to the user or facility manager of the building, collects user feedback, and uses fuzzy logic By processing by , more flexible control can be achieved.

Artificial intelligence converts users' verbal feedback into parameters by fuzzy logic, learning the internal environment that provides optimal comfort compared to the external environment that real users feel, and can set it as a standard or target state, and to maintain that state It has the effect of minimizing energy consumption, increasing user productivity, improving building operation, and contributing to the sustainability of society.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a flowchart illustrating a process of acquiring data for managing a building using artificial intelligence.
2 is a flowchart illustrating a process of obtaining user feedback and processing it by fuzzy logic.
3 is a flowchart illustrating a process of obtaining a control value of a facility for changing the internal environment of a building by using collected sensor data and user feedback.
4 is a flowchart illustrating a process of predicting power consumption of a building using sensor data.
5 is a flowchart illustrating a process of obtaining a plurality of candidate data for building management and determining one final data.
6 is a flowchart illustrating a process of receiving an input related to facility control from a user and updating artificial intelligence through this input.
7 is a block diagram of an apparatus according to an embodiment.
8 is a diagram illustrating a process of obtaining a user's optimal preference state and deriving a control value through this.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium or to reproduce one or more processors. Thus, as an example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or as additional components and “parts” or “modules”. can be further separated.

In this specification, a computer refers to all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

The building referred to in the present specification may refer to various types of buildings, such as large buildings used by multiple people, as well as general residential buildings of households made up of one or more members.

The membership function (or membership function) used for fuzzyization mentioned in this specification may be defined in various forms, such as a triangular shape, a trapezoidal shape, or an arbitrary curved shape, but is not limited thereto.

Fuzzy inference referred to in the present specification may mean one of Maldini-type fuzzy inference and Sugeno-type fuzzy inference, but is not limited thereto.

The de-fuzzy referred to in this specification mainly refers to a cetroid technique, but a method such as a mean of maximum may be variously employed, but is not limited thereto.
1 is a flowchart illustrating a process of acquiring data for managing a building using artificial intelligence.

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In step S110, the computer acquires data about the building, collected by one or more IoT sensors.

In an embodiment, the one or more IoT sensors described above may employ one or more of common wireless communication technologies, and may transmit collected data to a computer using this.

In an embodiment, the above-described data about the building may include data about the outside of the building and data about the inside of the building.

In addition, the above-mentioned data on the outside of the building includes temperature, humidity, weather conditions, wind speed, air composition ratio, fine dust concentration, illuminance, sunlight irradiation angle, amount of sunlight, movement of external objects, presence or absence of toxic components, or building It may include a weather forecast for the area where it is located.

In addition, the above-mentioned data on the interior of the building includes the temperature, humidity, air composition ratio, fine dust concentration, illuminance, the number of users inside the building, the usage time for each space division inside the building, the occupancy rate for each space within the building, or the building Usage patterns of internal facilities may be included.

In step S120, the computer pre-processes the collected data.

In an embodiment, the above-described pre-processing may mean processing the collected data into a form for inputting into the artificial intelligence model.

For example, a computer can convert values that cannot be expressed as numbers into numeric codes. Then, the computer can fill the missing measured values with the median value, the average value of a specific section, the mode, the corresponding value by regression analysis, or the predicted value by pattern estimation, and delete meaningless values.

In addition, the computer may delete an abnormal measurement value due to a malfunction of the sensor. For example, a measured value in a range that is physically difficult to occur, such as when the external temperature at one point in time is measured at 200 degrees Celsius, may be processed as an error, deleted, and replaced with an estimated appropriate value.

In addition, the computer may generate a new variable by using an existing measured value. For example, a computer may perform a specific operation using temperature and humidity as variables to determine the internal state of a building, and use a variable derived as a result of this operation as a new parameter for determining the internal state of the building.

In addition, the computer can obtain more appropriate variables by using calculation methods such as log function, square root, standard deviation, variance, and mean distribution for specific variables in order to determine the internal state of the building.

In addition, the computer can create a dataset for learning artificial intelligence by selectively collecting only specific variables by unifying the time period. In addition, when a specific variable is controlled, the computer can create a dataset by collecting changes in other variables. For example, the computer collects the values of the remaining variables when the temperature is 25 degrees, and sends the data in a form for input to the artificial intelligence model so that it can learn the patterns of changes in other variables when the temperature inside the building is 25 degrees. can be processed Of course, the processed data can be processed in a state where variables other than temperature are controlled, and it is also possible to set a specific time period or to create a dataset in a state in which one or more variables are controlled.

In this way, the computer can transform the data into a form that the AI model can learn for input into the AI model. This may be in the form of correlating one or more variables with each other for operation, or may be in the form of changing one variable into another factor through operation.

In step S130, the computer obtains one or more user feedbacks.

In an embodiment, the computer may recognize a voice through an IoT sensor to obtain user feedback, provide an application capable of receiving user feedback through a user device, or additionally have a display device for inputting feedback.

In an embodiment, the user feedback may be in the form of a score based on an oral input by voice, a character string input, direct facility control by a user, or a specific upper limit value.

For example, if the user adjusts the desired internal temperature to 20 degrees while the computer adjusts the internal temperature to 25 degrees, the computer may determine and obtain the feedback that the user prefers 20 degrees to 25 degrees in the corresponding condition.

And, when a user input to stop the operation of the temperature control device is received at the moment the actual temperature reaches 23 degrees while the internal temperature is being adjusted to a control value of 20 degrees, the preferred feedback for the above-mentioned 20 degrees is 23 degrees. It can be replaced with preferred feedback.

In one embodiment, the computer may request user feedback through the user's device. For example, it is possible to request and obtain feedback on the internal environment of a building or computer equipment control through a request to install an application on the user's device.

In step S140, the computer pre-processes the obtained user feedback.

In an embodiment, the computer may convert the above-described verbal user feedback voice into a character string, and in this process, a separate artificial intelligence model capable of converting voice into text may be additionally utilized.

In an embodiment, the computer may utilize fuzzy logic in the process of preprocessing user feedback.

For example, in the process of fuzzing the temperature, the computer performs 'very cold', 'cold', 'cool', 'moderate', 'warm', 'hot', 'very hot', etc. It is possible to set various sets of , and approximate their membership functions to the shapes of triangles, trapezoids, bells, and normal distributions.

For example, 26 degrees Celsius may have a degree of belonging of 0.3 in 'moderate' and 0.7 in 'warm'. If the computer judges the internal temperature as exemplified above by fuzzy logic, it can provide a 'slightly warm' temperature that the actual user feels.

In addition, the use of this fuzzy logic is to allow the machine to flexibly receive verbal expressions about senses with unclear human boundaries. Considering the sensible temperature, different membership functions can be used.

For example, if the computer determines that the season is early winter, 20 degrees Celsius may have a degree of belonging changed from 'cool' to 'slightly cold'.

In addition, the computer can utilize fuzzy logic in the process of processing data about the external or internal environment.

In an embodiment, the computer may determine the season based on the input date and time information, and the determination regarding the season may be made based on fuzzy logic.

For example, the computer may determine September 25 as 'end of September', 'late summer', 'early autumn' or 'a combination of 0.4 late summer and 0.6 early autumn'.

In addition, the computer may change the standard of the range of the internal environment parameter that is judged to have high comfort inside the building based on the seasonal judgment. In addition, the computer may change the reference of the above-described internal environment parameter range and at the same time change the membership function of the corresponding parameter. Here, the parameter means a numerical value indicating the state of the internal environment, such as temperature and humidity.

The standard of this parameter range means the user's optimal preference state, which will be described later, and is a value determined based on the feedback of users who have used the existing building. am.

In addition, when the computer determines that it is 'midsummer' or 'midwinter' based on the above-mentioned seasonal determination, the computer sets the temperature lower than the comfort standard in consideration of the entry time and body temperature or sensible temperature of the person entering the room after outdoor activities. A corresponding thermostat may be controlled to perform cooling or heating at a high temperature.

For example, if there is a building user who enters the room after performing outdoor activities in midsummer, even if the appropriate temperature for the user staying inside the building is determined to be 25 degrees, the computer Based on the entry point of the user entering the room, the cooling system is operated at 18 degrees in advance, and the temperature control device can be operated so that the user can gradually achieve the appropriateness of the comfortable environment from the time the user enters the room.

In an embodiment, the computer may acquire information about the body temperature of a user of a building to be managed. The detection of body temperature for providing information on body temperature may be accomplished by shooting an infrared-based thermal image based on the IoT sensor, or it may be detected by an IoT temperature sensor attached to a refrigerator handle or door handle. It may be detected by various devices or methods without being limited by means and methods.

In addition, the computer may obtain information about the body temperature of the building user and operate the device inside the building based on the information.

For example, when it is determined that the body temperature of a building user is higher than usual and it is determined that he has a cold, the appropriate temperature or appropriate humidity may be set higher, the ventilation function of the air conditioner may be weakly adjusted, and the humidity may be adjusted.

In addition, if the building user is a woman, if it is detected that the user's basal body temperature is higher than usual, the computer determines that the user is in the high-temperature phase of the menstrual cycle and increases the user's comfort according to the value set by the user For this purpose, the membership function of various parameters can be changed.

Fuzzy theories, such as fuzzy inference, fuzzy inference, and reverse fuzzy, have reached a sufficiently mature level of technology, so that, of course, in the above-described embodiment, a variety of techniques that those skilled in the art can utilize can be utilized. The technical idea disclosed in is not limited to the above-described embodiment.

In step S150, the computer inputs the pre-processed data and user feedback into the artificial intelligence model.

In an embodiment, the aforementioned artificial intelligence model may be one or more different artificial intelligence models, and may be artificial intelligence models having different purposes with input values and output values different from each other.

For example, the above-mentioned artificial intelligence model may be one artificial intelligence that comprehensively determines the state of the internal environment, but it is necessary to adjust one or more parameters among parameters such as temperature, humidity, or internal VOC (Volatile Organic Compounds) concentration. It may be an artificial intelligence model for

In step S160, the computer obtains data for controlling the building equipment from the output of the artificial intelligence model.

In one embodiment, the data for the control of the above-described building equipment is a control value of individual equipment itself, a weight in consideration of the influence between equipment and equipment, power consumption, and the effect on one or more internal environmental parameters when operating a specific equipment , the optimal preference state of one or more current building users, a difference value between the optimal preference state and the current internal environment, and a control value of the facility in consideration of a target time required or time required to reach the optimal preference state.

For example, if the current temperature is 30 degrees Celsius, the user's optimal preference state is 25 degrees Celsius, and the user's body temperature and external temperature are assumed, the target time required is 5 minutes. It is possible to satisfy the target time required by operating the intensity of the temperature control device more strongly than the case.

In addition, as in the above case, when the equipment is operated with a stronger output in consideration of the target time required, the control value may be differently adjusted or other equipment may be controlled together in consideration of the power consumption.

For example, if it is necessary to quickly lower the temperature, if the outside temperature is lower than the inside temperature, the computer opens the window or operates the ventilation system first to lower the inside temperature partially by introducing outside air, and then turn on the cooling system. can make it work

In step S170, the computer controls the building equipment based on the obtained data.

In an embodiment, the computer may transmit various control signals to a facility connected to the computer using wireless communication or wired communication.

The above-mentioned building equipment includes ventilation and air conditioning equipment including HVAC (Heating, Ventilation and Air Conditioning), a motor for opening and closing windows, a motor that can move a awning or curtain, a dehumidifier, an elevator, lighting equipment, water supply equipment, regardless of type. A maintenance facility or security facility may be included.

In an embodiment, the computer may control the building equipment with a control value obtained by newly processing the acquired data.

In step S180, the computer obtains user feedback according to the control of the equipment and updates the artificial intelligence model.

In an embodiment, when the computer operates the equipment at one point in time and the internal environment is changed after a certain period of time has elapsed due to this, the computer may request feedback from the user. Also, the computer may fuzzy a state in which the user does not give any feedback to positive feedback. In addition, the computer may fuzzy the user feedback in consideration of a time interval between the time when the feedback is input and the time when the first control signal is transmitted.

For example, if a new control input from the user is received within a very short time after the user has been controlled by the computer, the narrower the time interval, the greater the difference between the control value input by the user and the control value transmitted by the computer. , it can be judged that there is negative feedback.

2 is a flowchart illustrating a process of obtaining user feedback and processing it by fuzzy logic.

In step S210, the computer converts the user feedback obtained in step S180 into parameters based on fuzzy logic.

Here, the above-described parameter means a value obtained by converting user feedback into a quantified numerical value.

In one embodiment, the computer may convert the user's verbal voice feedback into a string, and input this into an artificial intelligence model to obtain a quantified output.

For example, if the user says 'Oh, it seems a little cold', the computer can approximate this with feedback of 'a little cold', and quantify the approximated feedback by fuzzing it.

Specifically, if the user's utterance voice, 'Oh, it seems a little cold' is collected by the IoT sensor, and the computer converts it into a string and inputs it into the AI model, the AI model can As input, this can be quantified as 'very cold', 'cold', 'slightly cold', 'cool', 'moderate', 'warm', 'slightly hot', 'hot', 'very hot', etc. It is possible to output which one of the expressions belongs to. The computer may convert the user's verbal feedback into a quantified value, that is, the above-described parameter by performing fuzzy, fuzzy inference, and reverse fuzzy on the output of the artificial intelligence model.

In an embodiment, the computer may receive the input as a score in a form that does not need to convert the user feedback into a quantified parameter. In addition, when receiving user feedback as a score, the computer may distinguish and recognize the user who gives the user feedback, and may fuzzy the user feedback considering that criteria for giving a score may be different for each person.

For example, a specific user may evaluate himself to be very satisfied with 100 points and dissatisfied with 70 points, but other users may evaluate his or her satisfaction as 90 points and dissatisfaction as 45 points. points can be evaluated. The numerical input is subject to the individual user's subjectivity, and each user can objectively evaluate different scores for the same environmental state.

For example, the computer generates a graph that takes the approximate value by normalizing the difference between the lowest and highest scores and various feedback scores of a specific first user, and synthesizes the difference between the lowest and highest scores of the second user and various feedback scores. By creating a graph taking an approximation normalized to , and comparing the above two graphs, it is possible to objectify evaluation based on different standards for each individual. This is to make the subjectively assigned score as objective as possible by measuring the degree to which a specific score corresponding to user feedback is far from the mean based on the standard deviation.

In an embodiment, the fuzzy logic technique employed to convert the above-described user feedback into a parameter may be achieved by employing all techniques commonly used by those skilled in the art.

In step S220, the computer inputs the transformed parameter into the artificial intelligence model.

In an embodiment, the computer may pre-process the transformed parameters into different datasets and input them into the AI model for learning or outputting the results of the AI model for different purposes.

In step S230, the computer re-acquires data for controlling the building equipment based on the output value of the artificial intelligence model.

This process is to allow the computer to calculate the control value in which the user feedback is reflected using artificial intelligence.

In an embodiment, the re-acquired data for facility control may have a control value that is not different from a previously obtained and used control value as a result.

For example, if the user feedback is feedback indicating that the current state is satisfied, or if the user feedback is input before sufficient time for the internal environment to change to correspond to the control value has elapsed, the same control value may still be obtained. .

In an embodiment, the data for controlling the facility may be data for changing or updating the optimal preference state of one or more building users.

The above-described optimal preference state means an internal environment state in which the sum of the satisfaction scores for the internal environment of one or more building users is maximized. When a new building user enters the building or an existing building user inputs new feedback, the optimal preference state may also be changed to a new state, and data for equipment control may mean a numerical value related to this state.

In one embodiment, the data for the above-mentioned control may simply be a control value for controlling the equipment, and it is newly adjusted in consideration of how the operation of each equipment affects each other by operating one or more different equipment at the same time. It may be a control value.

For example, a computer may operate an air conditioner to lower the temperature and a dehumidifier at the same time to control the humidity. can

As another embodiment, there may be a case where two or more users input different feedbacks for the same internal environment state in the same indoor space. In this case, the computer recognizes the location where the user who has inputted the feedback resides through the IoT sensor or receives input from the user and controls the facility to adjust the sensible temperature at a specific location by turning the wind direction of the air conditioner to that location. can

And, when the gap between feedback felt by two or more users cannot be narrowed through facility control of the computer, the computer may suggest a change in the partitioning of a specific user's use space or the installation of an additional shield.

In step S240, the computer uses the re-obtained data to control the equipment of the building.

This is to control the equipment by reflecting the fuzzy-processed user feedback.

3 is a flowchart illustrating a process of obtaining a control value of a facility for changing the internal environment of a building by using collected sensor data and user feedback.

Also, FIG. 8 is a diagram illustrating a process in which a user's optimal preference state is obtained and a control value is derived through this.

In step S310, the computer classifies the data about the building, collected by the one or more IoT sensors, into data about the external environment and data about the internal environment.

This is because it is necessary to use both separately, so the data on the external environment is to perform a building management method that considers how to operate the facilities of the building or the user's perceived temperature, and data on the internal environment The separation is to obtain a control value for controlling the internal equipment by calculating a difference value from the current internal environment and the target internal environment.

In addition, the separation of data regarding the external environment and the internal environment is not limited to the above-described purpose, and may be processed, edited, and utilized for various purposes.

The above-described data on the external environment may include all factors that may affect the living environment inside and outside the building, such as wind direction, wind speed, sunrise and sunset times outside the building.

In step S320, the computer converts the obtained user feedback into parameters based on fuzzy logic.

Converting the user feedback into quantified parameters in the above-described step (S320) is not different from the above-described method described by exemplifying how other steps are performed in its method, but for that purpose, data related to the above-described internal environment A method for calculating an optimal preference state to be described later through comparison, combination, or analysis with .

In addition, in the step S160, the output of the artificial intelligence may be the user's optimal preference state learned from the data on the internal environment, the data on the external environment, and the user feedback converted into the parameters.

In an embodiment, the above-described artificial intelligence may transmit to the computer whether and to what extent the user feels satisfaction or comfort when a specific internal environment is created under conditions related to a specific external environment.

For example, on a rainy day outside the building, if the indoor temperature is slightly higher than the outdoor temperature and the humidity is very lower than the outdoor temperature, if the user feedback has the highest comfort parameter, artificial intelligence will The corresponding state can be output as the user's optimal preference state.

Here, the 'slightly high' temperature and 'very low' humidity are converted into quantified values using fuzzy logic in consideration of the user's feedback.

Specifically, the user's optimal preference state means the internal environment state in which the user feels the highest satisfaction, but the emotional expression of 'satisfaction' with unclear human boundaries can be easily changed by various factors, so a more flexible It is designed to additionally consider the data on the external environment to implement the autonomous management method of the building.

For example, if the external environment is cold, the user may feel greater comfort and satisfaction at a slightly higher temperature and humidity than usual. And, such satisfaction may be different for each user even in the same internal environment, and even for the same user, it may differ depending on the condition or health condition of the day or time.

Accordingly, according to an embodiment, the data on the above-described internal environment may include information on body temperature or health status of a user using the inside of a building. The user's body temperature may be collected by a thermal imaging camera having a human scale, a door handle, a temperature sensor installed in a handle or contact part of a frequently used facility, such as a refrigerator, and the like.

In addition, the computer may request the user to input information on the health condition of the building user through a separate display and input device or the user's device.

In an embodiment, when there are two or more building users, each user may provide a different satisfaction score as feedback with respect to the same internal environment state. In this case, the computer adds up the satisfaction scores of a plurality of building users The internal environment can be created to maximize this.

In addition, in order for the user feedback regarding the composition or satisfaction of the above-described internal environment to be summed up and reflected in the judgment of the computer or to be used for learning the artificial intelligence model, it may be necessary to obtain user feedback and adjust the new control value cyclically several times. there is.

That is, in the above-described step, it can be said that the computer utilizes the user feedback for two main purposes: obtaining a new control value, and determining a state in which the user feels comfortable.

In step S330, the computer acquires a control value related to facility control for changing the internal environment of the building from the obtained optimal preference state of the user and the data on the internal environment.

In one embodiment, the computer acquires data on the current internal environment state, compares it with the user's optimal preference state obtained in the above-described step S320, and if there is a difference, the interior of the building in a direction to eliminate the difference A control value for controlling the equipment is calculated.

In addition, when the computer operates one individual facility in order to obtain the above-described control value, the operation of such facility may take into account the influence of the operation on various individual elements constituting the internal environment state.

For example, when cooling of a building is performed by operating an air conditioner, the air conditioner is operated to lower the temperature, but this may affect humidity. In consideration of this effect, if the computer needs to lower the temperature and humidity of the internal environment at the same time, consider the degree to which the humidity is lowered by operating the air conditioner. can be printed

Also, for example, if the temperature of the internal environment needs to be raised, if there is an effect that the humidity rises in the process of raising the temperature, the computer raises the temperature and simultaneously operates a dehumidifier to suppress the unnecessary increase in humidity. Control values can be output.

4 is a flowchart illustrating a process of predicting power consumption of a building using sensor data.

In step S410, the computer pre-processes the data obtained in step S110.

In one embodiment, the computer may separate data on the amount of power consumed while operating each facility in the building from the data on the above-described building.

In addition, the computer may transform, edit, process, or process the data related to the above-described power consumption into a form suitable for input to a second artificial intelligence model to be described later.

For example, data on messenger consumption can be deleted as an ambiguous or obvious error, similar to the pre-processing process described above, and empty data values due to such deletion or omission of data collection are averaged through regression analysis, etc. It can be replaced with an estimated value, and only relevant data can be bundled and processed into a new dataset.

In step S420, the computer inputs the data preprocessed in step S410 to the pre-trained second artificial intelligence model.

In step S430, the computer predicts the future power consumption of the building from the output of the second artificial intelligence model.

In an embodiment, the computer may predict the amount of power consumption at various time intervals that are preset or received from the user.

For example, the computer may predict the power consumption for one day, or the power consumption for a period of various units such as a week, a month, a year, and the like.

In an embodiment, the computer may change the power consumption prediction to a different value due to a change in the external environment.

For example, when heavy rain falls on a hot summer day and the outside temperature drops, which also affects the inside temperature, it can be predicted that the power consumption will be reduced as much as the cooling equipment is operated less due to this effect.

In an embodiment, the computer may predict the amount of power consumption based on data accumulated during a continuous period in the past, and this period may be determined by a preset numerical value or by a user input.

For example, in order to predict the annual power consumption, the computer may collect power consumption data for the past three years and estimate the current year's power consumption based on the data.

In one embodiment, the computer collects data on the power consumption on the day having a similar value to the data on the internal/external environment of the building for the date or period to be predicted in order to predict the power consumption at a specific point in time, and based on this, the power consumption can be predicted.

For example, if the highest temperature is 32 °C and the lowest temperature is 25 °C, if data is obtained about the forecast that rain with 40 mm of precipitation is expected, all data on the external environment most similar to this external environment as a label By collecting data on the power consumption of a day, it is possible to estimate the power consumption for that day.

In addition, when the external environment has little effect on the power consumption due to the characteristics of the building to be managed, the power consumption can be predicted by considering the number of users inside the building, and the number of users inside the building is the number of entrants passing through the entrance and exit. It can be estimated by counting the number of characters. In addition, in order to calculate the number of users inside the building, a motion sensor, a vibration sensor, etc. may be additionally provided, and the number of users may be estimated by allowing the computer to recognize or analyze the CCTV image that captures the point passing through the entrance. .

5 is a flowchart illustrating a process of obtaining a plurality of candidate data for building management and determining one final data.

In step S510, the computer acquires a plurality of candidate data for the control of the building equipment.

In an embodiment, the computer may acquire a plurality of candidate control values for varying the amount of time required or the amount of power consumed to create a single target internal environment.

For example, to bring the internal temperature of a building from 25 degrees Celsius to 20 degrees Celsius, the computer can simulate various combinations of facility controls to reach the desired state. In addition to simply operating the air conditioner, various scenarios can be provided to the user to achieve the desired state through more efficient power consumption by cooling the internal temperature or controlling the humidity more quickly by using the ventilation system, and the desired state It is also possible to provide the user with a control value for each required time by variously setting the required time required to reach .

In step S520, the computer calculates the power consumption when the building equipment is controlled according to each of the plurality of candidate data.

In an embodiment, the computer may calculate by simulating the amount of power consumed by the building while maintaining the internal environmental state when the candidates of the various control values are executed as they are.

In an embodiment, the computer may provide the most efficient control scenario of a building facility for each parameter when prioritizing a specific parameter along with power consumption.

In step S530, the computer estimates the state of the building when the building equipment is controlled according to each of the plurality of candidate data.

In one embodiment, when the equipment inside the building is operated or controlled by the corresponding candidate control values, the computer can display how parameters representing various environmental conditions inside the building can change over time, such as a graph method, etc. can be visualized and presented to the user.

In an embodiment, the computer may estimate the change pattern of the internal environment in which the influence of this is reflected in consideration of data on the external environment.

In step S540, the computer determines one final data based on the state and power consumption of the building according to each of the plurality of candidate data.

In an embodiment, the computer may request the user to select one control value set from among a plurality of candidate control values, and in this process, the prediction regarding the change aspect of the internal environment state or the amount of power consumption and the required time information can be provided.

In an embodiment, the computer may control and manage the building equipment by selecting one of a plurality of candidate control values according to a preset priority even if it does not receive an input from the user.

For example, if the power consumption is set to a minimum, the computer selects a scenario with the minimum power consumption in the building and controls the internal equipment of the building, which is premised on satisfying the optimal preferences of the building users. do it with

That is, the computer can provide a control value for a change in the state of the internal environment of a building considering the consumption of time and resources from various angles, depending on what the parameter with the first priority is, for each parameter, and the user can One of these is selected or the computer manages the building using the control values of the selected building equipment based on the preset priority.

6 is a flowchart illustrating a process of receiving an input related to equipment control from a user and updating artificial intelligence through this input.

In step S610, the computer receives a facility control input from the first user.

In one embodiment, the above-described facility control input may be of any type, and in addition to electrically inputting a control signal, if there is a motor that moves a curtain or awning, as previously exemplified, facility control may be a method of moving this by hand. there is. However, in this case, the computer can convert the offline motion through the sensor into an electrical signal and receive it.

In step S620, the computer controls the equipment of the building according to the control input of the first user.

In step S630, the computer updates the artificial intelligence model based on the control input of the first user.

That is, this is to allow the computer to accept this as user feedback even if the user simply controls the equipment without inputting a control signal of the equipment as user feedback. This can help the building's users to become a long-term optimization of the building's internal facilities even if they live daily lives without considering the optimization of building equipment separately.

In one embodiment, the data about the building collected in the step (S110) is VOC (Volatile Organic Compunds) concentration, fine dust concentration, carbon dioxide concentration, comprehensive air quality, lighting, natural light, indoor temperature, outdoor temperature, humidity , vibration, climatic conditions outside the building, season, power consumption of facilities inside the building, the number of users inside the building, or movement inside the building may be characterized as one or more.

In addition, in the step (S170), the controlled equipment is HVAC (Heating, Ventilation and Air Conditioning) equipment, air conditioning equipment, heating equipment, elevator, security system, lighting equipment, air purifier, window, light blocking equipment, door shielding equipment , a humidifier, a dehumidifier, a facility for moving the location of an internal facility, a firefighting facility, or a home appliance may be characterized.

In one embodiment, the above-described data on the building may further include a factor that may affect the state of the internal environment of the building, and a sensor module for detecting this may be additionally installed.

In an embodiment, the computer may build the information on the power consumption of the managed building as a database based on data collected from the IoT sensor. In addition, the information in the built-up database can be pre-processed and transmitted to the artificial intelligence model, and the artificial intelligence can predict the power consumption of the managed building through this.

For example, the process of pre-processing the information in the database described above is to extract data by determining the start and end points of the period based on the changed time points, such as building user members or facility usage patterns, and deliver it to artificial intelligence. It can mean processing to create data pairs in the form.

This is because the amount of power consumption may vary if the members of the building users change, the building usage patterns of building users change, or new internal facilities are installed.

And, since the power consumption of buildings is bound to vary according to the change of season and temperature, in order to predict the power consumption in July of this year, the power consumption in July of last year is extracted from the database, and the number of users or power consumption pattern, etc. can reflect changes in

In addition, the computer can create a data pair in the form of extracting only data of a specific date or time zone, rather than a continuous period, and transmitting it to AI.

For example, in order to predict the power consumption for a day reflecting today's weather, the power consumption for the day with weather information or external environment data that falls within a range similar to today's weather forecast is extracted from the database and delivered to the AI. You can create data pairs of the form for

Alternatively, the computer may predict power consumption in a shorter range. For example, when the weather changes rapidly unlike the forecast, it reflects the information about sudden heavy rain to predict the change pattern of the external environment such as temperature drop and humidity rise By extracting the data, it is possible to estimate the amount of power consumed during the rest of the day.

In addition, the computer predicts the amount of power consumption based on the information provided by the artificial intelligence model, but may take into account the weather, season, outside temperature, climate, or the facility usage pattern of building users.

In an embodiment, the computer may predict the amount of power consumption based on an expected power consumption pattern for a specific period in the future according to a request of a user or a facility manager.

7 is a block diagram of an apparatus according to an embodiment.

The processor 102 may include one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus, etc.) for transmitting and receiving signals to and from other components. .

The processor 102 according to an embodiment performs the method described with reference to FIGS. 1 to 6 and 8 by executing one or more instructions stored in the memory 104 .

For example, the processor 102 obtains new training data by executing one or more instructions stored in the memory, and performs a test on the acquired new training data using the learned model, and the test result, labeling Extracting the first learning data in which the obtained information is obtained with an accuracy greater than or equal to a predetermined first reference value, deleting the extracted first learning data from the new learning data, and removing the new learning data from which the extracted learning data is deleted It is possible to re-train the learned model using

On the other hand, the processor 102 is a RAM (Random Access Memory, not shown) and ROM (Read-Only Memory: ROM) for temporarily and / or permanently storing a signal (or, data) processed inside the processor 102. , not shown) may be further included. In addition, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

The memory 104 may store programs (one or more instructions) for processing and controlling the processor 102 . Programs stored in the memory 104 may be divided into a plurality of modules according to functions.

The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , may be implemented in a programming or scripting language such as Java, assembler, or the like. Functional aspects may be implemented in an algorithm running on one or more processors.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains know that the present invention may be embodied in other specific forms without changing the technical spirit or essential features thereof. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (9)

A computer-implemented method comprising:
Obtaining data about the building, collected by one or more IoT sensors (S110);
pre-processing the obtained data (S120);
obtaining one or more user feedback (S130);
pre-processing the obtained user feedback (S140);
inputting the pre-processed data and user feedback into an artificial intelligence model (S150);
obtaining data for controlling the equipment of the building from the output of the artificial intelligence model (S160);
controlling the equipment of the building based on the obtained data (S170); and
updating the artificial intelligence model by obtaining user feedback according to the control of the equipment (S180); includes,
The data about the building, collected in the step (S110), includes VOC (Volatile Organic Compunds) concentration, fine dust concentration, carbon dioxide concentration, overall air quality, lighting, natural light, indoor temperature, outdoor temperature, humidity, vibration, outside the building. is at least one of the climatic condition, season, power consumption of facilities inside the building, the number of users inside the facility, or movement inside the facility,
In the step (S170), the controlled equipment is HVAC (Heating, Ventilation and Air Conditioning) equipment, air conditioning equipment, heating equipment, elevator, security system, lighting equipment, air purifier, window, light blocking equipment, door shielding equipment, humidifier , a dehumidifier, a facility for moving internal facilities, a firefighting facility, or a smart building management method using an IoT sensor and artificial intelligence. According to claim 1,
The step (S180) is,
converting the user's feedback obtained in the step (S180) into a parameter based on fuzzy logic (S210);
inputting the converted parameter into the artificial intelligence model (S220);
Re-acquiring data for controlling the equipment of the building based on the output value of the artificial intelligence model (S230); and
controlling the equipment of the building using the re-obtained data (S240); containing,
Smart building management method using IoT sensor and artificial intelligence. According to claim 1,
The step (S120) is,
Classifying the data about the building, collected by the one or more IoT sensors, into data about the external environment and data about the internal environment (S310); including,
The step (S140) is,
converting the obtained user feedback into parameters based on fuzzy logic (S320); including,
In the step (S160), the output of the artificial intelligence is,
It is characterized in that it is the user's optimal preference state learned from the data on the internal environment, the data on the external environment, and the user feedback converted into the parameters,
obtaining a control value related to facility control for changing the internal environment of the building from the obtained optimal preference state of the user and data related to the internal environment (S330); containing,
Smart building management method using IoT sensor and artificial intelligence. According to claim 1,
The step (S120) is,
pre-processing the data obtained in the step (S110) (S410);
inputting the pre-processed data in the step (S410) to the pre-trained second artificial intelligence model (S420); and
Predicting the future power consumption of the building from the output of the second artificial intelligence model (S430); containing,
Smart building management method using IoT sensor and artificial intelligence. According to claim 1,
The step (S160) is,
obtaining a plurality of candidate data for controlling the equipment of the building (S510);
calculating power consumption when the equipment of the building is controlled according to each of the plurality of candidate data (S520);
estimating the state of the building when the equipment of the building is controlled according to each of the plurality of candidate data (S530); and
determining one final data based on the state and power consumption of the building according to each of the plurality of candidate data (S540); further comprising,
Smart building management method using IoT sensor and artificial intelligence. According to claim 1,
The step (S180) is,
receiving a facility control input from a first user (S610);
controlling the equipment of the building according to the control input of the first user (S620); and
updating the artificial intelligence model based on the control input of the first user (S630); further comprising,
Smart building management method using IoT sensor and artificial intelligence. delete a memory storing one or more instructions; and
a processor executing the one or more instructions stored in the memory;
The processor by executing the one or more instructions,
An apparatus for performing the method of claim 1 . A computer program stored in a computer-readable recording medium in combination with a computer, which is hardware, to perform the method of claim 1 .

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