KR102450024B1 - Neural network based building fire detection system - Google Patents

Abstract

Embodiments of the present invention provide a neural network-based building fire prevention system, which can guarantee the reliability of fire detection. According to an embodiment of the present invention, the neural network-based building fire prevention system comprises a data processing unit receiving a plurality of pieces of fire detection information from each of a plurality of detection units, and extracting an acquisition value for each of the plurality of pieces of fire detection information. The plurality of detection units include at least one of a temperature detection unit detecting the temperature of a specific area, a smoke detection unit detecting smoke generated in the specific area, and a gas detection unit detecting gas generated in the specific area, and a database storing a reference value for the acquisition value. The reference value includes: a safe range border value for the acquisition value, a threshold value for the acquisition value, and a warning value for the acquisition value. Also, included are: an alert generation unit generating a fire alert when the data processing unit determines that fire has occurred in the specific area; and a data correction unit correcting the threshold value for the acquisition value based on the acquisition value. The data processing unit is able to determine whether fire has occurred or not in the specific area by comparing the reference value stored in the database with the acquisition value.

Description

Neural network-based building fire prevention system {NEURAL NETWORK BASED BUILDING FIRE DETECTION SYSTEM}

Embodiments of the present invention are fire prevention systems that reduce fire detection operation errors using a neural network, and more specifically, detect smoke, objects, temperature, etc. based on a plurality of sensors, and prevent a fire occurring in a building based on this. The present invention relates to a system for reducing operation errors by automatically adjusting detection thresholds of a plurality of sensors included in a fire detection device using a neural network. In addition, the present invention is to apply the fourth industrial technology artificial intelligence (artificial intelligence) technology to adjust the sensor detection threshold of the fire prevention system in order to reduce a fire detection operation error.

A general fire detector is a device for detecting a fire by using a sensor that detects a physical quantity changed by the fire when a fire occurs in a building, and notifying people inside the building to warn them. A fire detector according to the prior art is a device for detecting a fire through a sensor for detecting a change in one physical quantity and generating a fire alarm, and the sensor includes a temperature sensor for detecting a temperature change, a gas sensor for detecting a gas concentration, and smoke generation. A smoke sensor to detect In the case of such a fire detector, a fire alarm is generated by detecting only one fire element through one sensor. However, as in the prior art, performing detection of one fire element may generate an alarm in a non-fire situation, and may cause a problem in that an alarm does not occur or an alarm malfunctions when a sensor malfunctions.

In addition, when a single sensor is used, whenever a physical quantity is measured by the sensor, the measured value may vary due to a measurement error of the sensor or the influence of the surrounding environment, so the reliability of fire detection may be reduced.

The background art described above is possessed or acquired by the inventor in the process of deriving the disclosure of the present application, and cannot necessarily be said to be a known technology disclosed to the general public prior to the present application.

Embodiments provide a building fire prevention system that detects changes in various types of physical quantities using a plurality of detection units (sensors) and comprehensively analyzes the detection results using a neural network to detect a fire in a building.

The technical problems to be achieved in the embodiments are not limited to those mentioned above, and other technical problems not mentioned may be considered by those of ordinary skill in the art from various embodiments to be described below. can

A building fire prevention system based on a neural network according to an embodiment of the present invention includes: a data processing unit for receiving a plurality of fire detection information from each of a plurality of detection units, and extracting an acquired value for each of the plurality of fire detection information; Including, wherein the plurality of sensing units, at least one of a temperature sensing unit sensing a temperature of a specific area, a smoke sensing unit sensing smoke generated in the specific area, and a gas sensing unit sensing gas generated in the specific area a database including one and storing a reference value for the acquired value; the reference value includes a safety range boundary value for the acquired value, a threshold value for the acquired value, and a warning value for the acquired value and a notification generating unit configured to generate a fire alarm when the data processing unit determines that a fire has occurred in the specific area; and a data correction unit for correcting a threshold value for the acquired value based on the acquired value, wherein the data processing unit compares the acquired value with the reference value stored in the database to cause a fire in the specific area can determine whether or not

The plurality of sensing units include only a first sensing unit, a second sensing unit that is physically separated from the first sensing unit, and a third sensing unit that is physically separated from the first sensing unit and the second sensing unit. including, wherein the data processing unit extracts a first obtained value from the first fire detection information obtained by the first detection unit, and extracts a second obtained value from the second fire detection information obtained by the second detection unit, A third obtained value is extracted from the third fire detection information obtained by the third detection unit, and the threshold value stored in the database is a first threshold value for the first obtained value and a second value for the second obtained value 2 threshold values and a third threshold value for the third acquired value, wherein the warning value stored in the database is a first warning value for the first acquired value and a second warning value for the second acquired value value, and a third warning value for the third acquired value, wherein the data processing unit is configured to: the first acquired value is greater than or equal to the first threshold value, less than the first warning value, and the second acquired value If the value is greater than or equal to the second threshold value, less than the second warning value, the third obtained value is equal to or greater than the third threshold value, and less than the third warning value, there is a fire in the specific area. can be considered to have occurred.

The data correction unit receives a plurality of first obtained values from a plurality of first fire detection information obtained by the first detection unit, and the plurality of first obtained values is a first obtained value obtained by the first detection unit at a first time point. 1 acquisition value, a first acquisition value obtained by the first sensing unit at a second time point subsequent to the first time point, and a first acquisition value obtained by the first sensing unit at a third time point subsequent to the second time point including a value, the first obtained value obtained by the first detection unit at the first time point, the first obtained value obtained by the first detection unit at the second time point, and the first detection unit at the third time point Based on the first acquisition value obtained in , a first technology acquisition value for a specific time interval is generated, wherein the specific time interval means a time interval from the first time point to the third time point, and The first technology acquired value for a specific time period is the first acquired value acquired by the first detection unit at the first time point, the first acquired value acquired by the first detection unit at the second time point, and the second The first detection unit may be greater than an average value of the first acquisition values acquired at the third time point.

<Equation>

The data correction unit generates the first technology acquisition value for the specific time period by using the equation,

In the above equation, v_T is the first technology acquired value for a specific time interval, v_di is the acquired value acquired by the first detector at the i-th time, v_d1 is the acquired value acquired by the first detector at the first time, v_d2 is the acquisition value obtained by the first detection unit at the second time point, v_d3 is the acquisition value obtained by the first detection unit at the third time point, and n is the number of acquisition values obtained by the first detection unit during a specific time period can do.

The data correction unit may include: a difference between the first obtained value obtained by the first detection unit at the first time point and the first technology obtained value, the first obtained value obtained by the first sensing unit at the second time point; Calculating the average measurement error range of the first detection unit based on the difference between the first technology acquisition value and the difference between the first acquisition value and the first technology acquisition value acquired by the first detection unit at the third time point, , a first corrected threshold value may be calculated by correcting the first threshold value based on the average measurement error range.

<Equation>

The data correction unit calculates the first corrected threshold value by using the equation, where v_c is a threshold value, v_c' is the corrected threshold value, and n is the number of sensing units included in the plurality of sensing units. , σ may mean an average measurement error range, and w may mean a correction coefficient.

The data correction unit generates a correction coefficient based on a threshold value, a corrected threshold value, and an average measurement error range using a data correction model, and the correction coefficient means a coefficient applied to the average measurement error range, The data correction model includes an input layer, one or more hidden layers, and an output layer, and the plurality of training data relating to the threshold value, the corrected threshold value, and the average measurement error range are in the input layer of the data correction model. inputted, passed through the one or more hidden layers and the output layer, and outputted as an output vector, the output vector is input to a loss function layer connected to the output layer, and the loss function layer is the output vector and each of the learning A loss value may be output using a loss function that compares the correct vector with respect to data, and the parameter of the data correction model may be learned in a direction in which the loss value decreases.

According to embodiments, it is possible to quickly and accurately determine whether a fire has occurred based on the fire detection information obtained from a plurality of detection units. In addition, the reliability of fire detection can be ensured by adjusting the reference value for determining the occurrence of fire using artificial intelligence and correcting the measurement error of the detection unit based on this.

Effects obtainable from the embodiments are not limited to the effects mentioned above, and other effects not mentioned are clearly derived and understood by those of ordinary skill in the art based on the following detailed description. can be

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and, together with the detailed description, explain technical features of the various embodiments.
1 is a diagram illustrating a configuration of an electronic device according to an embodiment of the present invention.
2 is a diagram showing the configuration of a program according to an embodiment of the present invention.
3 is a diagram schematically illustrating a building fire prevention system according to an embodiment of the present invention.
FIG. 4 is a block diagram illustrating the fire warning device of FIG. 3 .
5 is a flowchart for explaining the operation of the fire prevention system according to an embodiment of the present invention.
6 is a flowchart for explaining operation S560 of FIG. 5 .

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. In addition, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may be changed. Some features or features of one embodiment may be included in another embodiment, or may be replaced with corresponding features or features of another embodiment.

In the description of the drawings, procedures or steps that may obscure the gist of various embodiments are not described, and procedures or steps that can be understood at the level of those of ordinary skill in the art are also not described. did.

Throughout the specification, when a part is said to "comprising or including" a certain component, it does not exclude other components unless otherwise stated, meaning that other components may be further included. do. In addition, terms such as "... unit", "... group", and "module" described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, "a or an", "one", "the" and like related terms are used herein in the context of describing various embodiments (especially in the context of the claims that follow). Unless otherwise indicated or clearly contradicted by context, it may be used in a sense including both the singular and the plural.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION The detailed description set forth below in conjunction with the appended drawings is intended to describe exemplary embodiments of various embodiments, and is not intended to represent the only embodiments.

In addition, specific terms used in various embodiments are provided to help the understanding of various embodiments, and the use of these specific terms may be changed to other forms without departing from the technical spirit of various embodiments. .

1 is a diagram illustrating a configuration of an electronic device according to an embodiment of the present invention.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100 , an electronic device 101 communicates with an electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or a second network 199 . It may communicate with at least one of the electronic device 104 and the server 108 through (eg, a long-distance wireless communication network). According to an embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120 , a memory 130 , an input module 150 , a sound output module 155 , a display module 160 , an audio module 170 , and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or an antenna module 197 . In some embodiments, at least one of these components (eg, the connection terminal 178 ) may be omitted or one or more other components may be added to the electronic device 101 . In some embodiments, some of these components (eg, sensor module 176 , camera module 180 , or antenna module 197 ) are integrated into one component (eg, display module 160 ). can be The electronic device 101 may also be referred to as a client, a terminal, or a peer.

The processor 120, for example, executes software (eg, a program 140) to execute at least one other component (eg, a hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or operations. According to one embodiment, as at least part of data processing or operation, the processor 120 converts commands or data received from other components (eg, the sensor module 176 or the communication module 190 ) to the volatile memory 132 . may be stored in , process commands or data stored in the volatile memory 132 , and store the result data in the non-volatile memory 134 . According to an embodiment, the processor 120 is the main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit (eg, a graphic processing unit, a neural network processing unit) a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, when the electronic device 101 includes the main processor 121 and the sub-processor 123 , the sub-processor 123 uses less power than the main processor 121 or is set to be specialized for a specified function. can The auxiliary processor 123 may be implemented separately from or as a part of the main processor 121 .

The secondary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or when the main processor 121 is active (eg, executing an application). ), together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to an embodiment, the coprocessor 123 (eg, an image signal processor or a communication processor) may be implemented as part of another functionally related component (eg, the camera module 180 or the communication module 190 ). have. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. Artificial intelligence models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but in the above example not limited The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the above example. The artificial intelligence model may include, in addition to, or alternatively, a software structure in addition to the hardware structure.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176 ) of the electronic device 101 . The data may include, for example, input data or output data for software (eg, the program 140 ) and instructions related thereto. The memory 130 may include a volatile memory 132 or a non-volatile memory 134 .

The program 140 may be stored as software in the memory 130 , and may include, for example, an operating system 142 , middleware 144 , or an application 146 .

The input module 150 may receive a command or data to be used by a component (eg, the processor 120 ) of the electronic device 101 from the outside (eg, a user) of the electronic device 101 . The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output a sound signal to the outside of the electronic device 101 . The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from or as part of the speaker.

The display module 160 may visually provide information to the outside (eg, a user) of the electronic device 101 . The display module 160 may include, for example, a control circuit for controlling a display, a hologram device, or a projector and a corresponding device. According to an embodiment, the display module 160 may include a touch sensor configured to sense a touch or a pressure sensor configured to measure the intensity of a force generated by the touch.

The audio module 170 may convert a sound into an electric signal or, conversely, convert an electric signal into a sound. According to an embodiment, the audio module 170 acquires a sound through the input module 150 , or an external electronic device (eg, a sound output module 155 ) connected directly or wirelessly with the electronic device 101 . The electronic device 102) (eg, a speaker or headphones) may output a sound.

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the sensed state. can do. According to an embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface 177 may support one or more specified protocols that may be used by the electronic device 101 to directly or wirelessly connect with an external electronic device (eg, the electronic device 102 ). According to an embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102 ). According to an embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 may convert an electrical signal into a mechanical stimulus (eg, vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic sense. According to an embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to an embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied to the electronic device 101 . According to an embodiment, the power management module 188 may be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). It can support establishment and communication performance through the established communication channel. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : It may include a local area network (LAN) communication module, or a power line communication module). A corresponding communication module among these communication modules is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (eg, a telecommunication network such as a LAN or a WAN). These various types of communication modules may be integrated into one component (eg, a single chip) or may be implemented as a plurality of components (eg, multiple chips) separate from each other. The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199 . The electronic device 101 may be identified or authenticated.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, a new radio access technology (NR). NR access technology includes high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency) -latency communications)). The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate, for example. The wireless communication module 192 uses various techniques for securing performance in a high-frequency band, for example, beamforming, massive multiple-input and multiple-output (MIMO), all-dimensional multiplexing. It may support technologies such as full dimensional MIMO (FD-MIMO), an array antenna, analog beam-forming, or a large scale antenna. The wireless communication module 192 may support various requirements defined in the electronic device 101 , an external electronic device (eg, the electronic device 104 ), or a network system (eg, the second network 199 ). According to an embodiment, the wireless communication module 192 may include a peak data rate (eg, 20 Gbps or more) for realizing eMBB, loss coverage (eg, 164 dB or less) for realizing mMTC, or U-plane latency for realizing URLLC ( Example: Downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive a signal or power to the outside (eg, an external electronic device). According to an embodiment, the antenna module 197 may include an antenna including a conductor formed on a substrate (eg, a PCB) or a radiator formed of a conductive pattern. According to an embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected from the plurality of antennas by, for example, the communication module 190 . can be selected. A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) other than the radiator may be additionally formed as a part of the antenna module 197 .

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module comprises a printed circuit board, an RFIC disposed on or adjacent to a first side (eg, bottom side) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, an array antenna) disposed on or adjacent to a second side (eg, top or side) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and a signal ( e.g. commands or data) can be exchanged with each other.

According to an embodiment, the command or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or a part of operations executed in the electronic device 101 may be executed in one or more external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a function or service automatically or in response to a request from a user or other device, the electronic device 101 may perform the function or service itself instead of executing the function or service itself. Alternatively or additionally, one or more external electronic devices may be requested to perform at least a part of the function or the service. One or more external electronic devices that have received the request may execute at least a part of the requested function or service, or an additional function or service related to the request, and transmit a result of the execution to the electronic device 101 . The electronic device 101 may process the result as it is or additionally and provide it as at least a part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of things (IoT) device. The server 108 may be an intelligent server using machine learning and/or neural networks. According to an embodiment, the external electronic device 104 or the server 108 may be included in the second network 199 . The electronic device 101 may be applied to an intelligent service (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and may provide a service to the connected electronic device 101 . In addition, the server 108 may store and manage various types of information of users who have joined as members by performing a membership registration procedure, and may provide various purchase and payment functions related to services. Also, the server 108 may share execution data of a service application executed in each of the plurality of electronic devices 101 in real time so that the service can be shared among users. The server 108 may have the same configuration as a typical web server (Web Server) or WAP server (WAP Server) in terms of hardware. However, in terms of software, it may include a program module that is implemented through any language such as C, C++, Java, Visual Basic, or Visual C and performs various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, receives a request to perform a task from a client or other server, and derives and provides the work result. It means a computer system and the computer software (server program) installed therefor. In addition, the server 108, in addition to the above-described server program, a series of application programs (Application Program) operating on the server 108 and in some cases, various databases (DB: Database, hereinafter " DB") should be understood as a broad concept including. Accordingly, the server 108 classifies, stores and manages member registration information, various information and data about the game in a DB, and this DB may be implemented inside or outside the server 108 . In addition, the server 108 uses a server program that is provided in various ways according to operating systems such as DOS, Windows, Linux, UNIX, and Macintosh in general server hardware. As a representative example, a website used in a Windows environment, Internet Information Server (IIS), and CERN, NCSA, APPACH, etc. used in a Unix environment may be used. In addition, the server 108 may cooperate with an authentication system and a payment system for user authentication of a service or a purchase payment related to a service.

The first network 198 and the second network 199 are a connection structure capable of exchanging information between respective nodes such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104 . (Network). The first network 198 and the second network 199 are Internet (Internet), LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), 3G , 4G, LTE, 5G, Wi-Fi, and the like. The first network 198 and the second network 199 may be the closed first network 198 and the second network 199 such as a LAN or a WAN, but preferably an open type such as the Internet. The Internet is a TCP/IP protocol and several services existing in its upper layers, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP ( Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service) means a worldwide open computer first network 198 and second network 199 structure.

The database may have a general data structure implemented in a storage space (hard disk or memory) of a computer system using a database management program (DBMS). The database may have a data storage form in which data can be freely searched (extracted), deleted, edited, added, and the like. Database is a relational database management system (RDBMS) such as Oracle, Infomix, Sybase, DB2, or object-oriented database management such as Gemston, Orion, O2, etc. It can be implemented for the purpose of an embodiment of the present disclosure by using a system (OODBMS) and an XML-only database (XML Native Database) such as Excelon, Tamino, and Sekaiju, and its function It may have an appropriate field or elements to achieve .

2 is a diagram showing the configuration of a program according to an embodiment of the present invention.

2 is a block diagram 200 illustrating a program 140 in accordance with various embodiments. According to an embodiment, the program 140 executes an operating system 142 , middleware 144 , or an application 146 executable in the operating system 142 for controlling one or more resources of the electronic device 101 . may include Operating system 142 may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs 140 are, for example, preloaded into the electronic device 101 at the time of manufacture, or an external electronic device (eg, the electronic device 102 or 104 ), or a server (eg, the electronic device 102 or 104 ) when used by a user ( 108)) or may be updated. All or part of the program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or retrieval) of one or more system resources (eg, a process, memory, or power) of the electronic device 101 . The operating system 142 may additionally or alternatively include other hardware devices of the electronic device 101 , for example, the input module 150 , the sound output module 155 , the display module 160 , and the audio module 170 . , sensor module 176 , interface 177 , haptic module 179 , camera module 180 , power management module 188 , battery 189 , communication module 190 , subscriber identification module 196 , or It may include one or more driver programs for driving the antenna module 197 .

The middleware 144 may provide various functions to the application 146 so that functions or information provided from one or more resources of the electronic device 101 may be used by the application 146 . The middleware 144 includes, for example, an application manager 201 , a window manager 203 , a multimedia manager 205 , a resource manager 207 , a power manager 209 , a database manager 211 , and a package manager 213 . ), a connectivity manager 215 , a notification manager 217 , a location manager 219 , a graphics manager 221 , a security manager 223 , a call manager 225 , or a voice recognition manager 227 . can

The application manager 201 may manage the life cycle of the application 146 , for example. The window manager 203 may manage one or more GUI resources used in the screen, for example. The multimedia manager 205, for example, identifies one or more formats required for playback of media files, and encodes or decodes a corresponding media file among the media files using a codec suitable for the selected format. can be done The resource manager 207 may manage the space of the source code of the application 146 or the memory of the memory 130 , for example. The power manager 209 may, for example, manage the capacity, temperature, or power of the battery 189 , and determine or provide related information required for the operation of the electronic device 101 by using the corresponding information. . According to an embodiment, the power manager 209 may interwork with a basic input/output system (BIOS) (not shown) of the electronic device 101 .

The database manager 211 may create, retrieve, or change a database to be used by the application 146 , for example. The package manager 213 may manage installation or update of an application distributed in the form of a package file, for example. The connectivity manager 215 may manage, for example, a wireless connection or a direct connection between the electronic device 101 and an external electronic device. The notification manager 217 may provide, for example, a function for notifying the user of the occurrence of a specified event (eg, an incoming call, a message, or an alarm). The location manager 219 may manage location information of the electronic device 101 , for example. The graphic manager 221 may manage, for example, one or more graphic effects to be provided to a user or a user interface related thereto.

Security manager 223 may provide, for example, system security or user authentication. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101 . The voice recognition manager 227, for example, transmits the user's voice data to the server 108, and based at least in part on the voice data, a command corresponding to a function to be performed in the electronic device 101; Alternatively, the converted text data may be received from the server 108 based at least in part on the voice data. According to an embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to an embodiment, at least a portion of the middleware 144 may be included as a part of the operating system 142 or implemented as software separate from the operating system 142 .

Application 146 includes, for example, home 251 , dialer 253 , SMS/MMS 255 , instant message (IM) 257 , browser 259 , camera 261 , alarm 263 . , contacts 265, voice recognition 267, email 269, calendar 271, media player 273, album 275, watch 277, health 279 (such as exercise or blood sugar) measuring biometric information), or environmental information 281 (eg, measuring atmospheric pressure, humidity, or temperature information). According to an embodiment, the application 146 may further include an information exchange application (not shown) capable of supporting information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit specified information (eg, call, message, or alarm) to an external electronic device, or a device management application configured to manage the external electronic device. have. The notification relay application, for example, transmits notification information corresponding to a specified event (eg, mail reception) generated in another application (eg, the email application 269 ) of the electronic device 101 to the external electronic device. can Additionally or alternatively, the notification relay application may receive notification information from the external electronic device and provide it to the user of the electronic device 101 .

The device management application is, for example, a power source (eg, turn-on or turn-off) of an external electronic device that communicates with the electronic device 101 or some components thereof (eg, a display module or a camera module of the external electronic device). ) or a function (eg brightness, resolution, or focus). The device management application may additionally or alternatively support installation, deletion, or update of an application operating in an external electronic device.

3 is a diagram schematically illustrating a building fire prevention system according to an embodiment of the present invention. FIG. 4 is a block diagram illustrating the fire warning device 300 of FIG. 3 .

Referring to FIG. 3 , the building fire prevention system according to an embodiment of the present invention includes a fire detection device 400 including a plurality of detection units, and a fire warning device 300 for receiving fire detection information from the plurality of detection units. may include. Each of the plurality of detection units may be installed inside or outside the building to detect whether a fire occurs in a specific area (S) of the building. A plurality of sensing units may be installed to be spaced apart from the specific area (S). The plurality of sensing units may include a first sensing unit (A) and a second sensing unit (B). Here, the first and second are for specifying the sensing unit, and do not limit the order of the sensing unit. The first sensing unit (A) or the second sensing unit (B) may be installed to monitor a specific area (S) inside or outside the building. The first sensing unit A and the second sensing unit B may be disposed to be physically separated from each other. The fire detection method of the first detection unit A and the second detection unit B may be different from each other. That is, the first sensing unit A and the second sensing unit B may detect a fire occurring in a specific area S inside or outside the building in different ways. The plurality of detection units include a temperature sensing unit for sensing the temperature of a specific area (S), a smoke sensing unit for sensing smoke generated in the specific area (S), a gas sensing unit for sensing gas generated in the specific area (S), It may include at least one of a thermal image camera for acquiring a thermal image or a thermal image of the specific region (S). For example, the first detector A may be a thermal imaging camera, and the second detector B may be a gas detector. However, the embodiment of the present invention is not limited thereto, and various fire detection technologies may be applied to the first detection unit A and/or the second detection unit B.

The fire warning device 300 detects a fire obtained by the first detection unit (A) and the second detection unit (B) from a plurality of detection units including the first detection unit (A) and the second detection unit (B) information can be received. Here, the fire detection information may be information obtained by detecting a specific area (S) by the detection unit. The type of fire detection information may be determined according to technical characteristics of the detection unit. For example, when the sensing unit is a temperature sensing unit, the fire detection information may include temperature information. When the detection unit is a smoke detection unit that detects smoke by irradiating a light source to a specific area (S), the fire detection information may include information on the amount of light or information indicating a smoke generation value. When the detector is a gas detector, the fire detection information may include gas concentration information. In addition to the above examples, the types of fire detection information acquired by the detection unit may vary according to technical characteristics of the detection unit.

A method for the fire warning device 300 to receive fire detection information from a plurality of detection units including the first detection unit A and the second detection unit B may be various. For example, the fire warning device 300 uses a wireless or wired communication method from a plurality of sensing units including the first sensing unit A and the second sensing unit B to the first sensing unit A and the second sensing unit 300 . Information obtained from a plurality of sensing units including the sensing unit B may be received. The fire warning device 300 analyzes the fire detection information obtained from a plurality of detection units including the first detection unit (A) and the second detection unit (B) to create a fire in a specific area (S) inside or outside the building. It can be determined whether or not The fire warning device 300 may generate a fire alarm when it is determined that a fire has occurred in a specific area S inside or outside the building.

The fire warning device 300 may transmit or receive data through a wirelessly or wired network. The network may include a network based on a wired Internet technology, a wireless Internet technology, and a short-range communication technology. Wired Internet technology may include, for example, at least one of a local area network (LAN) and a wide area network (WAN).

Wireless Internet technology is, for example, wireless LAN (WLAN), DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband: Wibro), Wimax (World Interoperability for Microwave Access: Wimax), HSDPA (High Speed Downlink Packet) Access), High Speed Uplink Packet Access (HSUPA), IEEE 802.16, Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), Wireless Mobile Broadband Service (WMBS) and 5G New Radio (NR) technology. However, the present embodiment is not limited thereto.

When the fire warning device 300 transmits/receives data using a wireless communication method, the fire warning device 300 may comply with technical standards and standard communication methods for mobile communication. For example, standard communication methods include Global System for Mobile communication (GSM), Code Division Multi Access (CDMA), Code Division Multi Access 2000 (CDMA2000), Enhanced Voice-Data Optimized or Enhanced Voice-Data Only (EV-DO). , at least one of Wideband CDMA (WCDMA), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), Long Term Evolution-Advanced (LTEA), and 5G New Radio (NR). may include. However, the present embodiment is not limited thereto.

To this end, referring to FIG. 4 , the fire warning device 300 may include a data processing unit 310 , a database 320 , a notification generating unit 330 , and a data correcting unit 340 .

The data processing unit 310 may receive a plurality of fire detection information acquired by a plurality of detection units.

Although only two sensing units (a first sensing unit (A) and a second sensing unit (B)) are illustrated in FIG. 3 , the embodiment of the present invention is not limited thereto, and the plurality of sensing units includes three or more sensing units. can do. That is, the plurality of sensing units may include a first sensing unit, a second sensing unit, ??, and an n-th sensing unit. A plurality of detection units may each monitor a specific area (S) to acquire fire detection information. That is, the fire detection information may be information obtained by the detection unit detecting a specific area (S). The first fire detection information acquired by the first detector may include a first acquired value. The first fire detection information may include information in a specific unit according to a technical method in which the first detection unit detects a fire. Accordingly, the first acquired value may be composed of scalar values of various units. For example, when the first sensing unit is a temperature sensing unit sensing the temperature of the specific area (S), the first acquired value included in the first fire detection information may indicate the temperature of the specific area (S). In this case, the first acquired value may be a real value having a unit of Celsius (°C). Alternatively, when the first detection unit is a smoke detection unit that detects smoke in the specific area (S), the first acquired value included in the first fire detection information may indicate the smoke density of the specific area (S). In this case, the first obtained value may be a real value having a particle density unit (g/L). Similarly, when the first detection unit is a gas detection unit that detects gas in a specific area (S), the first acquired value included in the first fire detection information may indicate the gas concentration of the specific gas generated in the specific area (S). . In this case, the first acquired value may be a real value having a concentration unit (ppm).

The second fire detection information acquired from the second detector may include a second acquired value. Similarly, the n-th fire detection information acquired from the n-th detector may include an n-th acquired value. That is, the data processing unit 310 may receive the first fire detection information, the second fire detection information, ??, the n-th fire detection information from the plurality of detection units, and the first fire detection information is the first obtained value. , the second fire detection information may include a second acquired value, ??, and the nth fire detection information may include an nth acquired value.

The data processing unit 310 may extract an acquired value for each of the plurality of fire detection information received from the plurality of detection units. That is, the data processing unit 310 receives the first fire detection information, the second fire detection information, ??, the nth fire detection information from the plurality of detection units, and obtains a first obtained value from the first fire detection information, 2 It is possible to extract a second acquired value from the fire detection information, ??, and an nth acquired value from the nth fire detection information. The data processing unit 310 may compare the acquired value with a reference value for the acquired value stored in the database 320 to determine whether a fire has occurred in the specific area (S).

To this end, the database 320 may store reference values for each of the first acquired value, the second acquired value, ??, and the nth acquired value. The reference value is a value that is preset for each of the first detection unit, the second detection unit, ??, and the nth detection unit, and is a reference value for the data processing unit 310 to evaluate the acquired value obtained by each sensing unit. can mean The reference value for the specific acquisition value may include a safety range boundary value, a threshold value, and a warning value. For example, the reference value for the first acquired value may include a first safe range boundary value, a first threshold value, and a first warning value. If the sensing unit is a gas sensing unit, the fire detection information acquired by the gas sensing unit may include an acquired value indicating the gas concentration of the specific area (S). In this case, the database 320 may store a reference value for comparison with the obtained value indicating the gas concentration, for example, reference values for each type of gas generated when a material or a material in the vicinity of a specific area S is heated. .

The database 320 may have a general data structure implemented in the storage space of the fire warning device 300 using a database management program (DBMS). The database 320 may select any one of one or more reference values stored in the database 320 in response to a control signal from an external device or data processing unit 310 using a database management program. Specifically, the database 320 may select and output one or more reference values stored in the database 320 based on the control of the data processing unit 310 .

The safe range boundary value may include a safe range minimum value and a safe range maximum value. The safe range boundary value may be a reference value for determining whether the acquired value obtained by the specific sensing unit is within the safe range. Here, the minimum safe range value may be smaller than the maximum safe range value. That is, the maximum value of the safe range may be greater than the minimum value of the safe range. The data processing unit 310 may determine that the acquired value acquired by the specific sensing unit is within the safe range when the acquired value obtained by the specific sensing unit is equal to or greater than the minimum safe range value and is less than or equal to the safe range maximum value. Accordingly, the data processing unit 310 may determine that a fire does not occur in the specific area (S). For example, when the specific sensing unit is a temperature sensing unit measuring the temperature of the specific area S, the minimum safe range value may be 0°C, and the safe range maximum value may be 100°C. In this case, when the acquired value obtained by the specific sensing unit is 25° C., the data processing unit 310 receiving the acquired value may determine that a fire has not occurred in the specific area S.

The warning value may be a standard value for the data processing unit 310 to determine whether a fire has occurred in the specific area. That is, when the acquired value obtained by the specific detection unit is equal to or greater than the warning value, the data processing unit 310 may determine that a fire has occurred in the specific area. The warning value may be greater than the maximum value of the safe range. For example, when the specific sensing unit is a temperature sensing unit that measures the temperature of the specific area S, the warning value for the specific sensing unit may be 200°C. In this case, when the acquired value acquired by the specific sensing unit is 250° C., the data processing unit 310 receiving the acquired value may determine that a fire has occurred in the specific area S.

In one embodiment, when the first acquired value obtained by the first sensing unit included in the plurality of sensing units is equal to or greater than the first warning value, the data processing unit 310 determines that a fire has occurred in the specific area (S). can judge Alternatively, when the second obtained value obtained by the second sensing unit is equal to or greater than the second warning value, the data processing unit 310 may determine that a fire has occurred in the specific area S. That is, when the acquired value obtained by any one of the first sensing unit, the second sensing unit, ??, and the nth sensing unit is equal to or greater than the warning value preset for the sensing unit, the data processing unit 310 ) can be determined that a fire has occurred in the specific area (S).

The threshold value may be a value referenced by the data processing unit 310 to determine whether a fire has occurred in the specific area. The threshold may be greater than the safe range maximum and less than the warning value. That is, the threshold value may be a value between the maximum safe range value and the warning value. The data processing unit 310 includes a plurality of sensing units to determine whether a fire has occurred in a specific area when the first obtained value obtained by the first sensing unit is equal to or greater than the first threshold value and is smaller than the first warning value. It is possible to refer to an acquired value obtained by other sensing units except for the first sensing unit among the sensing units included in the .

In an embodiment, when the plurality of sensing units includes only the first sensing unit and the second sensing unit, the data processing unit 310 determines that the first obtained value obtained by the first sensing unit is equal to or greater than the first threshold value, and When the first warning value and the second acquired value obtained by the second detection unit are equal to or greater than the second threshold value and less than the second warning value, it may be determined that a fire has occurred in a specific area. On the other hand, when the first acquisition value obtained by the first detection unit is equal to or greater than the first threshold value, smaller than the first warning value, and the second acquisition value obtained by the second detection unit is smaller than the second threshold value, a specific area It can be concluded that there was no fire.

In another embodiment, when the plurality of detection units includes only the first detection unit, the second detection unit, and the third detection unit, the data processing unit 310 determines that the first acquired value obtained by the first detection unit is greater than the first threshold value. equal to or greater than the first warning value, the second obtained value obtained by the second detection unit is equal to or greater than the second threshold value, and is less than the second warning value, and the third obtained value obtained by the third detection unit is the second 3 If it is greater than or equal to the threshold value and less than the third warning value, it can be determined that a fire has occurred in a specific area. That is, the data processing unit 310 can determine that a fire has occurred in a specific area only when the values obtained by the first to third sensing units are all equal to or greater than the threshold value set for each sensing unit. On the other hand, when the obtained value obtained by any one of the first to third sensing units is smaller than the threshold value set for the sensing unit, it may be determined that a fire has not occurred in the specific area.

That is, when the data processing unit 310 cannot determine that a fire has occurred in the specific area S based only on the acquired value because the acquired value acquired by the specific sensing unit is not greater than the warning value, the data processing unit 310 is It may be determined whether a fire has occurred in the specific area S by additionally referring to the acquired value obtained by a sensing unit different from the sensing unit. In this case, the data processing unit 310 may determine whether a fire has occurred in the specific area (S) by comparing the respective obtained values obtained from different sensing units with the threshold values for each obtained value.

In an embodiment, the threshold may include a plurality of sub-thresholds. The number of sub-threshold values that the threshold value may include may be smaller than the number of detection units included in the plurality of sensing units. In an embodiment, the threshold value may include n-1 sub-threshold values (threshold value_1, threshold value_2, ??, threshold value_(n-1)). Here, n may be the number of sensing units included in the plurality of sensing units.

The notification generating unit 330 may generate a fire alarm when the data processing unit 310 determines that a fire has occurred in a specific area.

The data corrector 340 may correct the acquired value obtained by the sensing unit. The acquired value obtained by the sensing unit may include noise. That is, the actual physical quantity (eg, temperature, light quantity, gas concentration, etc.) of the specific area S may be different from an acquired value obtained by the sensing unit sensing the physical quantity of the specific area S. For example, if the sensing unit is a temperature sensing unit, if the actual temperature of the specific area (S) is 100°C, the obtained value obtained by sensing the temperature of the specific area (S) obtained by the sensing unit is in the range of 90°C to 110°C. It can be measured in various ways. The reason for the difference between the actual physical quantity of the specific area S and the value obtained by the sensing unit may be various. For example, the actual physical quantity of the specific area (S) and the acquired value of the sensing unit due to various causes such as measurement error of the sensing unit itself, obstacles between the sensing unit and the specific area (S), ambient temperature, ambient pressure, ambient humidity, etc. difference may occur.

In order to remove such noise, the data corrector 340 may receive a plurality of acquisition values acquired by the detector from the data processing unit 310 . Here, the plurality of acquisition values acquired by the sensing unit may be acquired values acquired by the sensing unit sensing the specific area S for a specific time period. For example, the plurality of acquisition values acquired by the first detection unit may include a first acquisition value acquired by the first detection unit at a first time point, and a second acquisition value acquired by the first detection unit at a second time point subsequent to the first time point. The value and the third acquired value obtained by the first sensing unit at a third time point subsequent to the second time point may be included. The data compensator 340 includes a first obtained value obtained by the first detection unit at a first time point from the data processing unit 310 and a second obtained value obtained by the first detection unit at a second time point subsequent to the first time point; , the first detection unit may receive a third acquisition value acquired at a third time point subsequent to the second time point.

The data compensator 340 includes a first acquisition value obtained by the first detection unit at a first time point, a first acquisition value obtained by the first detection unit at a second time point, and a first acquisition value obtained by the first detection unit at a third time point. A first skill acquisition value for the first time interval may be generated based on the value. Here, the first technology acquired value for the first time period may mean a correction value generated based on the acquired values obtained by the first sensing unit in the first time period. The first time period may mean a time period from the first time point to the third time point. The first time interval may be any time interval. That is, the time section from the first time point to the second time point and the time section from the second time point to the third time point may be variously selected by the data corrector 340 . There may be various methods in which the data corrector 340 generates the first technology acquisition value for the first time interval.

In an embodiment, the first technology acquisition value for the first time interval is a first acquisition value obtained by the first sensing unit at a first time point, a first acquisition value obtained by the first sensing unit at a second time point, a first The sensing unit may be greater than an average value of the first acquisition values acquired at the third time point. That is, the data compensator 340 includes a first obtained value obtained by the first sensing unit at a first time point, a first obtained value obtained by the first sensing unit at a second time point, and a first obtained value obtained by the first sensing unit at a third time point. The first acquisition value obtained by the sensing unit at the first time point, the first acquisition value obtained by the first sensing unit at the second time point, the second technique acquisition value for the first time interval is greater than the average value of the first acquisition values. The first detection unit may generate a first technology acquisition value for the first time period by correcting an average value of the first acquisition values acquired at the third time point.

In an embodiment, the data corrector 340 may generate a technology acquisition value for a specific time section by using Equation (1). In <Equation 1>, v_T is the technology acquired value for a specific time period, v_di is the acquired value acquired by the first detector at the i-th time, v_d1 is the acquired value acquired by the first detector at the first time, and v_d2 is An acquisition value obtained by the first detector at the second time point, v_d3 may mean an acquisition value obtained by the first sensor unit at a third time point, and n may mean the number of acquisition values obtained by the first sensor unit during a specific time period.

In an embodiment, the data correction unit 340 may include a difference between the first obtained value obtained by the first detection unit at the first time point and the first technology obtained value for the first time period, the first detection unit obtained at the second time point based on the difference between the first acquisition value and the first skill acquisition value for the first time interval, the difference between the first acquisition value obtained by the first sensing unit at the third time point and the first technique acquisition value for the first time interval to calculate the average measurement error range of the first sensing unit.

The data corrector 340 may calculate the first correction threshold value by correcting the first threshold value of the first sensing unit based on the average measurement error range of the first sensing unit. In an embodiment, the data corrector 340 may downwardly correct the first threshold value of the first detector based on the average measurement error range of the first detector.

In an embodiment, the data corrector 340 may correct the threshold value using Equation 2 and calculate the corrected threshold value. In <Equation 2>, v_c is a threshold value, v_c' is a corrected threshold value, n is the number of sensing units included in a plurality of sensing units, σ is an average measurement error range, and w is a correction coefficient. . Here, the correction coefficient may mean a coefficient applied to the average measurement error range so that the data correction unit 340 accurately corrects the threshold value.

In an embodiment, the data correction unit 340 may generate a correction coefficient based on the threshold value, the corrected threshold value, and the average measurement error range using the data correction model. To this end, the data correction model may include an input layer, one or more hidden layers, and an output layer. The data correction model may be trained in advance to accurately generate correction coefficients based on a threshold value, a corrected threshold value, and an average measurement error range through a learning process. To this end, in the learning process, a plurality of training data regarding the threshold value, the corrected threshold value, and the average measurement error range are input to the input layer of the data correction model and pass through one or more hidden layers and output layers to output an output vector. have. The output vector may be input to a loss function layer connected to the output layer. The loss function layer may output a loss value using a loss function that compares the output vector with the correct vector for each training data. The parameters of the data correction model may be learned in a direction in which the loss value becomes smaller.

For example, the loss function may calculate a loss value according to [Equation 3]. In [Equation 3], N is the number of a plurality of training data, n is a natural number identifying the training data, k is a natural number identifying the value of the n-th training data, nk is the k-th value of the n-th training data , t denotes correct answer data, y denotes an output vector, and E denotes a loss value.

Alternatively, the loss function may calculate a loss value according to [Equation 4]. In [Equation 4], n is the number of training data for each class, y and j are identifiers representing classes, C is a constant value, M is the number of classes, x_y is the probability value that the training data belongs to class y, x_j is learning A probability value that data belongs to class j, L may mean a loss value.

To this end, the data correction unit 340 may include an artificial intelligence machine learning model. When the fire warning device 300 is implemented as hardware, the data corrector 340 may include a hardware structure specialized for processing a machine learning model. Machine learning models can be created through artificial intelligence machine learning.

The machine learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but the above-described example is not limited to The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the above example. The artificial intelligence model may include, in addition to, or alternatively, a software structure in addition to the hardware structure.

Each component included in the fire warning device 300 may be connected to a communication path connecting a software module or a hardware module inside the device, so that they can organically operate with each other. These components may communicate using one or more communication buses or signal lines. Each component of the fire warning device 300 means a unit that processes at least one function or operation, and may be implemented as a software module, a hardware module, or a combination of software and hardware.

The configuration of the fire warning device 300 shown in FIG. 4 is not necessarily essential, and some configurations may be omitted or other configurations not shown in FIG. 4 may be added as necessary. That is, the configuration shown in FIG. 4 is for convenience of description, and embodiments are not limited thereto. Those of ordinary skill in the art will be able to modify and practice the embodiments of the present invention without departing from the scope of the present invention.

5 is a flowchart for explaining the operation of the fire prevention system according to an embodiment of the present invention.

First, the obtained values are received from the plurality of sensing units by the data processing unit 310 (S510).

The data processing unit 310 compares the received acquired value with a warning value for the acquired value stored in the database 320 (S520).

획득값인 경우), 데이터 처리부(310)는 알림 생성부(330)가 화재 경보를 생성하도록 제어한다. 이에 따라, 알림 생성부(330)에 의해 화재 경보를 생성한다(S540).When the acquired value is equal to or greater than the warning value (warning value

obtained value), the data processing unit 310 controls the notification generation unit 330 to generate a fire alarm. Accordingly, a fire alarm is generated by the notification generating unit 330 (S540).

When the acquired value is smaller than the warning value (if the warning value > the acquired value), the data processing unit 310 compares the acquired value with the safety range boundary value (S550).

When the obtained value is equal to or greater than the minimum safety range and less than the maximum value of the safe range (minimum safe range =< obtained value < maximum value of safe range), the data processing unit 310 determines that a fire has not occurred in the specific area S and the operation of the fire prevention system 10 is terminated.

When the acquired value is less than the minimum safe range (acquisition value < safe range minimum), the data processing unit 310 may control the notification generating unit 330 to transmit an abnormal operation signal of the detection unit that obtained the acquired value to the outside. have.

When the obtained value is equal to or greater than the maximum safe range (safety range maximum = < obtained value), the data processing unit 310 additionally refers to the obtained value obtained from a sensing unit different from the detecting unit which obtained the obtained value by referring to the , it is possible to determine whether a fire has occurred in the specific area (S) (S560).

6 is a flowchart for explaining operation S560 of FIG. 5 .

The data processing unit 310 receives the first acquired value from the first sensing unit and the second acquired value from the second sensing unit ( S561 ).

The data processing unit 310 compares the first obtained value with a first threshold value for the first obtained value stored in the database 320 , and the second obtained value is compared with the second obtained value stored in the database 320 . A second threshold value is compared with the obtained value (S562).

제1 획득값), 데이터 처리부(310)에 의해, 제2 획득값과 제2 임계값을 추가로 비교하고,When the first acquisition value is equal to or greater than the first threshold value (first threshold value)

first acquired value), by the data processing unit 310, to further compare the second acquired value and the second threshold,

When the first acquired value is smaller than the first threshold value (first acquired value < first threshold value), the data processing unit 310 determines that a fire has not occurred in the specific area S, and the fire prevention system 10 ) ends the operation (S563).

제1 획득값), 제2 획득값이 제2 임계값보다 같거나 큰 경우(제2 임계값

제2 획득값), 데이터 처리부(310)는 알림 생성부(330)가 화재 경보를 생성하도록 제어하고(S564), 이에 따라, 알림 생성부(330)에 의해 화재 경보를 생성한다(S565).the first acquisition value is greater than or equal to the first threshold value (the first threshold value

first acquisition value), if the second acquisition value is equal to or greater than the second threshold value (second threshold value)

second acquired value), the data processing unit 310 controls the notification generating unit 330 to generate a fire alarm (S564), and accordingly, the notification generating unit 330 generates a fire alarm (S565).

제1 획득값), 제2 획득값이 제2 임계값보다 작은 경우(제2 획득값<제2 임계값), 데이터 처리부(310)는 특정 구역(S)에 화재가 발생하지 않은 것으로 판단하고, 화재 예방 시스템(10)의 동작을 종료한다(S564). the first acquisition value is greater than or equal to the first threshold value (the first threshold value

first acquired value), when the second acquired value is less than the second threshold value (second acquired value < second threshold value), the data processing unit 310 determines that a fire has not occurred in the specific area S, and , ends the operation of the fire prevention system 10 (S564).

As described above, the fire prevention system according to an embodiment of the present invention includes a plurality of detection units, and by synthesizing the fire detection information obtained from the plurality of detection units, it is possible to accurately determine the fact that a fire has occurred in a specific area of a building. have. In addition, based on the fire detection information acquired by the plurality of detection units, a threshold value serving as a reference in determining whether a fire has occurred may be corrected using artificial intelligence. Through such a threshold correction operation, it is possible to ensure the reliability of the fire detection operation despite the measurement error of the detection unit.

The embodiments described above may be implemented by a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the apparatus, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that may include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (5)

A data processing unit for receiving a plurality of fire detection information from each of the plurality of detection units, and extracting an acquired value for each of the plurality of fire detection information;
The plurality of sensing units include at least one of a temperature sensing unit sensing a temperature of a specific area, a smoke sensing unit sensing smoke generated in the specific area, and a gas sensing unit sensing gas generated in the specific area, ,
Including; a database for storing a reference value for the acquired value;
The reference value includes a safety range boundary value for the acquired value, a threshold value for the acquired value, and a warning value for the acquired value,
a notification generating unit for generating a fire alarm when the data processing unit determines that a fire has occurred in the specific area; and
A data correction unit for correcting a threshold value for the acquired value based on the acquired value;
The data processing unit,
It is determined whether a fire has occurred in the specific area by comparing the obtained value with the reference value stored in the database,
The plurality of sensing units include only a first sensing unit, a second sensing unit that is physically separated from the first sensing unit, and a third sensing unit that is physically separated from the first sensing unit and the second sensing unit. including,
The data processing unit,
a first obtained value is extracted from the first fire detection information obtained by the first detection unit, a second obtained value is extracted from the second fire detection information obtained by the second detection unit, and a second obtained value obtained by the third detection unit is extracted. 3 Extract the third acquired value from the fire detection information,
The threshold value stored in the database includes a first threshold value for the first obtained value, a second threshold value for the second obtained value, and a third threshold value for the third obtained value,
The warning value stored in the database includes a first warning value for the first acquired value, a second warning value for the second acquired value, and a third warning value for the third acquired value,
The data processing unit,
the first acquired value is greater than or equal to the first threshold value and less than the first warning value, the second acquired value is greater than or equal to the second threshold value, and is less than the second warning value; 3 If the obtained value is equal to or greater than the third threshold value and less than the third warning value, it is determined that a fire has occurred in the specific area,
The data correction unit,
Receive a plurality of first acquisition values from a plurality of first fire detection information acquired by the first detection unit, wherein the plurality of first acquisition values include a first acquisition value obtained by the first detection unit at a first time point; a first obtained value obtained by the first detection unit at a second time point subsequent to the first time point, and a first obtained value obtained by the first detection unit at a third time point subsequent to the second time point,
The first acquisition value obtained by the first sensing unit at the first time point, the first acquisition value obtained by the first sensing unit at the second time point, and the third value obtained by the first sensing unit at the third time point Based on the 1 acquisition value, generating a first skill acquisition value for a specific time interval,
The specific time interval means a time interval from the first time point to the third time point,
The first technology acquisition value for the specific time period is the first acquisition value obtained by the first detection unit at the first time point, the first acquisition value obtained by the first detection unit at the second time point, the greater than the average value of the first acquisition value acquired at the third time point by the first detection unit,
The data correction unit,
a difference between the first obtained value and the first technology obtained value obtained by the first detection unit at the first time point, the first obtained value obtained by the first detection unit at the second time point and the first technology obtained Calculate the average measurement error range of the first detection unit based on the difference between the values and the difference between the first obtained value and the first technology obtained value obtained at the third time by the first detection unit,
calculating a first corrected threshold value by correcting the first threshold value based on the average measurement error range;
<Equation>

The data correction unit generates the first technology acquisition value for the specific time period by using the equation,
In the above equation, v_T is the first technology acquired value for a specific time interval, v_di is the acquired value acquired by the first detector at the i-th time, v_d1 is the acquired value acquired by the first detector at the first time, v_d2 is the acquisition value obtained by the first detection unit at the second time point, v_d3 is the acquisition value obtained by the first detection unit at the third time point, and n is the number of acquisition values obtained by the first detection unit during a specific time period doing,
Building fire prevention system based on neural network. The method of claim 1,
The data correction unit generates a correction coefficient based on a threshold value, a corrected threshold value, and an average measurement error range using a data correction model, and the correction coefficient means a coefficient applied to the average measurement error range,
The data correction model includes an input layer, one or more hidden layers and an output layer,
A plurality of training data relating to the threshold value, the corrected threshold value, and the average measurement error range are input to the input layer of the data correction model, pass through the one or more hidden layers and the output layer, and output as an output vector become,
The output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with a correct vector for each of the training data,
The parameters of the data correction model are learned in a direction in which the loss value becomes smaller,
Building fire prevention system based on neural network.
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