KR102490062B1 - Method, apparatus and computer program of AI-based user-tailored disaster threat prediction and safety management - Google Patents

Abstract

An embodiment of the present invention can provide an AI-based user-customized disaster risk prediction and safety management method performed by a computing device, wherein the method comprises the process of: obtaining pre-input information about a plurality of assets being managed by a user; selecting a plurality of first disaster response modules that each match the plurality of assets based on the pre-input information from among a plurality of preset disaster response modules and configuring a user-customized disaster response system by calculating priorities for the selected plurality of first disaster response modules; acquiring disaster monitoring information to determine whether a disaster has occurred in real time; determining the occurrence of a real-time disaster based on the disaster monitoring information; determining relevance and urgency of the real-time disaster to the plurality of first disaster response modules based on the real-time disaster occurrence; performing a plurality of second disaster response modules among the plurality of first disaster response modules belonging to the disaster response system according to the relevance and the urgency; and receiving disaster response results based on the plurality of second disaster response modules. The present invention has the effect of providing customized response measures to facilities or sites actually managed by users.

Description

AI-based user-tailored disaster risk prediction and safety management method, apparatus and computer program {Method, apparatus and computer program of AI-based user-tailored disaster threat prediction and safety management}

The present invention relates to an AI-based user-customized disaster threat prediction and safety management method, device, and computer program.

When a natural disaster such as a typhoon occurs, a conventional disaster management solution provides an alarm on whether a typhoon has occurred and provides a universal response manual for the typhoon.

However, universal response manuals only describe countermeasures to be generally taken in the event of a typhoon, but are often ineffective because they do not fit the facilities or sites actually managed by users.

In addition, the conventional disaster management solution simply guides only evacuation locations or provides the location of relief materials in the event of a disaster, and cannot respond to a crisis situation because it simply provides a response manual as a document.

In addition, conventional disaster management solutions have a problem in that they cannot provide training scenarios suitable for facilities or sites actually managed by users.

Republic of Korea Patent Registration No. 10-2230258 (Method of providing mapping contents to strengthen disaster situation management capability)

In order to solve the above problems, an embodiment of the present invention aims to provide customized response measures to a facility or site actually managed by a user by providing a user-customized response manual as well as a universal response manual in case of a disaster.

Alternatively, an embodiment of the present invention aims to set a customized disaster response system in a facility or site actually managed by a user, and perform disaster occurrence determination and disaster response using the customized disaster response system.

In addition, an embodiment of the present invention aims to provide education and training scenarios and disaster response contents suitable for facilities or sites actually managed by users.

In order to solve the above problems, an embodiment of the present invention is a method performed by a computing device, comprising: acquiring pre-input information about a plurality of assets being managed by a user; Among a plurality of preset disaster response modules, a plurality of first disaster response modules each matched with the plurality of assets are selected based on the pre-input information, and priorities for the selected plurality of first disaster response modules are calculated. the process of constructing a user-customized disaster response system; Acquiring disaster monitoring information to determine whether a disaster has occurred in real time; determining a real-time disaster based on the disaster monitoring information; determining the urgency of the real-time disaster for the plurality of first disaster response modules and the correlation with the plurality of first disaster response modules based on the real-time disaster; performing a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to the correlation and the urgency; And it is possible to provide an AI-based user-customized disaster risk prediction and safety management method including a process of receiving a disaster response result based on the plurality of second disaster response modules.

An embodiment of the present invention, after the process of configuring a user-customized disaster response system, further comprising the process of obtaining a customized manual for each asset for each of the selected plurality of first disaster response modules, AI-based user-customized disaster risk Forecasting and safety management methods can be provided.

In an embodiment of the present invention, the process of obtaining disaster monitoring information for determining whether a disaster has occurred in real time may include obtaining public data provided by the government or a research institute; obtaining user sensing data measured through a sensing device installed by the user; obtaining IT monitoring data measured from security devices of IT facilities; and a process in which the person in charge periodically obtains periodic inspection data on the asset.

In an embodiment of the present invention, the process of determining a disaster occurring in real time based on the disaster monitoring information occurs in real time using at least one of the public data, the user sensing data, the IT monitoring data, and the regular inspection data. It may include the process of determining the type of disaster.

In an embodiment of the present invention, the process of determining the relationship between the plurality of first disaster response modules and the urgency of the real-time disaster for the plurality of first disaster response modules based on the real-time disaster, selecting the plurality of second disaster response modules having relevance to the real-time disaster from among the plurality of first disaster response modules; and determining the urgency of a disaster occurring in real time for the plurality of first disaster response modules.

An embodiment of the present invention provides an AI-based user-customized disaster risk prediction and safety management method, further comprising a process of generating the real-time disaster based on a pre-stored disaster scenario for training and training for disaster response training. can

In addition, an embodiment of the present invention, a processor; network interface; Memory; and a computer program loaded into the memory and executed by the processor, wherein the computer program includes instructions for obtaining pre-input information about a plurality of assets being managed by a user; Among a plurality of preset disaster response modules, a plurality of first disaster response modules each matched with the plurality of assets are selected based on the pre-input information, and priorities for the selected plurality of first disaster response modules are calculated. Instructions to configure a user-customized disaster response system; An instruction for obtaining disaster monitoring information for determining whether a disaster has occurred in real time; an instruction for determining a real-time disaster based on the disaster monitoring information; an instruction for determining correlation with the plurality of first disaster response modules and urgency of the real-time disaster with respect to the plurality of first disaster response modules based on the real-time disaster; instructions for executing a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to the correlation and the urgency; And it is possible to provide an AI-based user-customized disaster risk prediction and safety management device including an instruction for receiving a disaster response result based on the plurality of second disaster response modules.

Alternatively, an embodiment of the present invention may be combined with a computing device to obtain pre-input information about a plurality of assets being managed by a user; Among a plurality of preset disaster response modules, a plurality of first disaster response modules each matched with the plurality of assets are selected based on the pre-input information, and priorities for the selected plurality of first disaster response modules are calculated. the process of constructing a user-customized disaster response system; Acquiring disaster monitoring information to determine whether a disaster has occurred in real time; determining a real-time disaster based on the disaster monitoring information; determining the urgency of the real-time disaster for the plurality of first disaster response modules and the correlation with the plurality of first disaster response modules based on the real-time disaster; performing a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to the correlation and the urgency; And in order to execute a process of receiving a disaster response result based on the plurality of second disaster response modules, an AI-based user-customized disaster risk prediction and safety management computer program stored in a computer-readable recording medium can be provided.

Through the above solution, the embodiment of the present invention may have the following effects.

Embodiments of the present invention provide a user-customized response manual as well as a universal response manual in the event of a disaster, and have an effect of providing customized response measures to a facility or site actually managed by a user.

Alternatively, the embodiment of the present invention has an effect of setting a customized disaster response system in a facility or site actually managed by a user, and performing disaster occurrence determination and disaster response using the customized disaster response system.

In addition, the embodiment of the present invention has an effect of providing education and training scenarios and disaster response contents suitable for facilities or sites actually managed by users.

1 is a diagram illustrating an AI-based user-customized disaster threat prediction and safety management system according to an embodiment of the present invention.
2 is a hardware configuration diagram of a disaster prediction management apparatus according to an embodiment of the present invention.
3 shows disaster information according to an embodiment of the present invention.
4 is a flowchart of an AI-based user-customized disaster risk prediction and safety management method according to an embodiment of the present invention.
5 is a conceptual diagram of a setting method of a disaster response system according to an embodiment of the present invention.
6 is an example of a screen for inputting pre-input information.
7 is an example of a response manual for each emergency step included in each of a plurality of disaster response modules.
8(a) and 8(b) illustrate setting examples of a disaster response system.
9 is a conceptual diagram of a disaster monitoring method using a disaster response system according to an embodiment of the present invention.
10 illustrates an example of disaster classification.
11 is an example of disaster related information for each asset.
12 is an example of a screen provided to a user or a person in charge through operation of a disaster response module.
13 is a flowchart of a method for evaluating disaster response system performance and training personnel in accordance with an embodiment of the present invention.
14 is an example of disaster scenarios and disaster response contents for education and training.
15 is an example of a real-time disaster prediction management monitoring screen provided on a display of a user or manager terminal.

Since the embodiments of the present invention can be applied with various changes, the embodiments are illustrated in the drawings and described in detail herein. However, this is not intended to limit the embodiments of the present invention to specific forms, and includes all modifications, equivalents, or substitutes included in the spirit and scope of the present invention.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, 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 components, these components 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 element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. Alternatively, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

The term "unit" or "module" used in the specification means a hardware component such as software, FPGA or ASIC, and "unit" or "module" performs certain roles. However, "unit" or "module" is not meant to be limited to software or hardware. A "unit" or "module" may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a “unit” or “module” may refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Functions provided within components and "units" or "modules" may be combined into fewer components and "units" or "modules" or may be combined into additional components and "units" or "modules". can be further separated.

Hereinafter, an AI-based user-customized disaster threat prediction and safety management method, device, and computer program according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating an AI-based user-customized disaster threat prediction and safety management system according to an embodiment of the present invention.

Referring to FIG. 1, the AI-based user-customized disaster threat prediction and safety management system according to an embodiment of the present invention includes an AI-based user-customized disaster threat prediction and safety management device 100 (hereinafter, a disaster prediction management device or a computing device) (100)), a manager terminal 200, an external server 300, a user terminal 410, a staff terminal 420, and a general staff terminal 430.

The system of FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1, and may be added, changed, or deleted as necessary.

In various embodiments, the computing device 100 provides a disaster management system customized for each user, predicts the threat of disaster based on AI, and manages the safety of assets and people to be protected according to the predicted disaster. .

2 is a hardware configuration diagram of a disaster prediction management apparatus according to an embodiment of the present invention.

Referring to FIG. 2, the disaster prediction management device 100 (hereinafter referred to as computing device 100) according to an embodiment of the present invention includes one or more processors 110 and a computer program 141 executed by the processor 110. It may include a memory 120 for loading (Load), a communication interface 130, and a storage 140 for storing the computer program 141.

Here, the computing device 100 of FIG. 2 shows components related to the embodiment of the present invention, but a general-purpose configuration other than the components shown in FIG. 2 for those skilled in the art to which the present invention belongs. It can be seen that more elements may be included.

In various embodiments, the computing device 100 refers to all types of hardware devices including at least one processor 110, and is understood as encompassing software configurations operating on the hardware device according to embodiments. It can be. For example, the computing device 100 may be understood as including a smart phone, a tablet PC, a desktop computer, a laptop computer, a server, and clients and applications running on each device, or is not limited thereto. Although each process described in this specification is described as being performed by the computing device 100, the subject of each process is not limited thereto, and at least a part of each process may be performed in different devices according to embodiments. .

The processor 110 controls the overall operation of each component of the computing device 100 . The processor 110 includes a Central Processing Unit (CPU), a Micro Processor Unit (MPU), a Micro Controller Unit (MCU), a Graphic Processing Unit (GPU), or any type of processor well known in the art of the present invention. It can be.

Alternatively, the processor 110 may perform an operation for at least one application or program for executing a method according to embodiments of the present invention, and the computing device 100 may include one or more processors.

Alternatively, the processor 110 may temporarily and/or permanently store signals (or data) processed in the processor 110 (RAM: Random Access Memory, not shown) and ROM (ROM: Read-Only Memory) , not shown) may be further included. Alternatively, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, RAM, and ROM.

Memory 120 stores various data, commands and/or information. Memory 120 may load computer program 141 from storage 140 to execute methods/operations according to various embodiments of the present invention. When the computer program 141 is loaded into the memory 120, the processor 110 may perform the method/operation by executing one or more instructions constituting the computer program 141. The memory 120 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

The bus BUS provides a communication function between components of the computing device 100 . The bus BUS may be implemented in various forms such as an address bus, a data bus, and a control bus.

The communication interface 130 supports wired and wireless Internet communication of the computing device 100 . Alternatively, the communication interface 130 may support various communication methods other than Internet communication. For example, the communication interface 130 may support at least one of short-distance communication, mobile communication, and broadcast communication. To this end, the communication interface 130 may include a communication module well known in the art. In some embodiments, communication interface 130 may be omitted.

The storage 140 may non-temporarily store the computer program 141 . The storage 140 may be a non-volatile memory such as read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or the like, a hard disk, a removable disk, or a device well known in the art. It may be configured to include any known type of computer-readable recording medium.

Computer program 141 may include one or more instructions that when loaded into memory 120 cause processor 110 to perform methods/operations in accordance with various embodiments of the invention. That is, the processor 110 may perform the method/operation according to various embodiments of the present disclosure by executing the one or more instructions.

In various embodiments, the computer program 141 includes obtaining instructions for obtaining pre-entry information about a plurality of assets being managed by a user; Among a plurality of preset disaster response modules, a plurality of first disaster response modules each matched with the plurality of assets are selected based on the pre-input information, and priorities for the selected plurality of first disaster response modules are calculated. Instructions to configure a user-customized disaster response system; An instruction for obtaining disaster monitoring information for determining whether a disaster has occurred in real time; an instruction for determining a real-time disaster based on the disaster monitoring information; an instruction for determining correlation with the plurality of first disaster response modules and urgency of the real-time disaster with respect to the plurality of first disaster response modules based on the real-time disaster; instructions for executing a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to the correlation and the urgency; and an instruction for receiving a disaster response result based on the plurality of second disaster response modules.

Processes of a method or algorithm described in connection with an embodiment of the present invention may be directly implemented in hardware, implemented in a software module executed by hardware, or implemented 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 form of computer readable recording medium well known in the art to which the present invention pertains.

In various embodiments, the computer program 141 may include obtaining pre-input information about a plurality of assets being managed by a user; Among a plurality of preset disaster response modules, a plurality of first disaster response modules each matched with the plurality of assets are selected based on the pre-input information, and priorities for the selected plurality of first disaster response modules are calculated. the process of constructing a user-customized disaster response system; Acquiring disaster monitoring information to determine whether a disaster has occurred in real time; determining a real-time disaster based on the disaster monitoring information; determining the urgency of the real-time disaster for the plurality of first disaster response modules and the correlation with the plurality of first disaster response modules based on the real-time disaster; performing a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to the correlation and the urgency; And it may be a computer program stored in a computer-readable recording medium in order to execute a process of receiving a disaster response result based on the plurality of second disaster response modules.

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 elements, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, such as C, C++ , Java (Java), can be implemented in a programming or scripting language such as assembler (assembler). Functional aspects may be implemented in an algorithm running on one or more processors.

Referring back to FIG. 1 , in various embodiments, the computing device 100 includes a manager terminal 200, an external server 300, a user terminal 410, a staff terminal 420, and general staff through a network 500. It may be communicatively connected to the terminal 430 .

In various embodiments, the computing device 100 may receive and obtain pre-input information about a plurality of assets being managed by a user through the manager terminal 200 or the user terminal 410 . Here, the plurality of assets being managed by the user may be objects that the user wants to protect or manage in the event of a disaster.

The pre-input information may include information on assets managed by the user, which can be obtained by the user directly or by a manager visiting the user online and offline to investigate and analyze the disaster response system customized for the user, which will be described later.

In various embodiments, the user's assets may include at least one of manpower, information, IT equipment, disaster prevention facilities, facilities, user definition, and the like. Here, manpower may include the head of an institution, safety manager, safety manager, person in charge, etc., information may include documents, etc., IT equipment may include servers, network equipment, computers, etc., and disaster prevention facilities may include emergency Broadcasting facilities, earth leakage alarms, fire detectors, gas detectors, machine control facilities, power control facilities, lighting control facilities, elevator control facilities, CCTV facilities, sprinklers, fire hydrants, etc. may be included. Fences, roofs, etc.), electrical facilities, mechanical installations, mechanical devices, etc., and customization may include chemical storage, storage, escalators, machine storage rooms, smoking rooms, etc. However, the types of assets are not limited to the configurations listed above, and may include all types of tangible and intangible assets owned by the user.

In various embodiments, the manager refers to an external person other than the user for objective investigation and analysis of the user's assets, and is a person who sets, maintains, and manages an AI-based user-customized disaster risk prediction and safety management system. The manager terminal 200 may transmit pre-input information input by the manager to the computing device 100 .

In various embodiments, the computing device 100 may configure a user-customized disaster response system based on pre-input information, and may start disaster monitoring using the user-customized disaster response system.

In various embodiments, the computing device 100 may receive and obtain disaster monitoring information for determining whether a disaster has occurred in real time from the external server 300 .

Here, the external server 300 is a first electronic device that provides public data provided by the government or a research institute, a second electronic device that stores and provides data sensed by a sensing device installed by a user, and a security device of an IT facility. It may include at least one of a third electronic device that stores and provides measured IT monitoring data and a fourth electronic device that stores and provides periodic inspection data regularly inspected by a person in charge. Here, for convenience of description, the first to fourth electronic devices are described as being included in the external server 300, but each of the first to fourth electronic devices may exist as separate devices, and the computing device 100 ) and can communicate independently through the network 500 to transmit and receive data.

In various embodiments, the computing device 100 determines a real-time disaster based on disaster monitoring information, and a plurality of first disasters that are part of a plurality of disaster response modules belonging to a disaster response system based on the type of a real-time disaster. The response module can be activated.

In one embodiment, the computing device 100 may provide real-time disaster-related information to at least one of a user, a person in charge, a general employee, and a manager. Here, information on a disaster occurring in real time may be transmitted through the user terminal 410, the staff terminal 420, the general staff terminal 430, and the manager terminal 200.

3 shows disaster information according to an embodiment of the present invention.

Referring to FIG. 3 , information on real-time disasters may include at least one of the occurrence date, type, content, development, damage situation, and scale of a disaster or crisis situation.

In various embodiments, when the computing device 100 operates a plurality of first disaster response modules, information about a disaster occurring in real time is provided to at least one of a user, a person in charge, and general staff included in each of a plurality of first disaster response modules. It is possible to provide a priority for a plurality of first assets corresponding to the response manual and the plurality of first disaster response modules included in each of the plurality of first disaster response modules to the user or the person in charge, and to general staff It can provide information on evacuation plans from disasters. Each disaster response module may have different information about a user, a person in charge, and a general employee for protecting and managing corresponding assets in case of a disaster.

Here, the user means the owner or overall manager of the asset to be managed, the person in charge means the person in charge of managing each asset and carrying out the response manual in case of a disaster, and the general employee is responsible for protecting the asset in the event of a disaster. means no one

As described above, even if the types of assets are different for each user, the computing device 100 may set a customized disaster management system for each user and provide a different response manual for the user's protected assets for each disaster.

Thus, the AI-based user-customized disaster risk prediction and safety management system according to an embodiment of the present invention provides a response scenario for each asset (eg, asset) rather than a conventional response scenario for each disaster in the event of a disaster, making the user's assets more secure. can protect you from disaster.

Hereinafter, with reference to the drawings, an AI-based user-customized disaster risk prediction and safety management method performed by the computing device 100 will be described in detail.

4 is a flowchart of an AI-based user-customized disaster risk prediction and safety management method according to an embodiment of the present invention. 5 is a conceptual diagram of a setting method of a disaster response system according to an embodiment of the present invention. 6 is an example of a screen for inputting pre-input information. 7 is an example of a response manual for each emergency step included in each of a plurality of disaster response modules. 8(a) and 8(b) illustrate setting examples of a disaster response system.

Referring to FIGS. 4 and 5 , in a process S100 , the computing device 100 may obtain pre-input information about a plurality of assets being managed by a user. A user or manager may investigate and analyze a plurality of assets being managed by the user, and may provide information on the plurality of assets to the computing device 100 as pre-input information.

Pre-input information is information input by a user or manager, and then the computing device 100 may set a disaster response system using the pre-input information.

Furthermore, the pre-input information may include not only a response manual for each asset in the event of a disaster, but also a safety checklist for regular asset safety checks, a training manual for training to prepare for a disaster, and a safety check manual for safety checks. Specifically, the checklist may include daily, weekly, monthly, quarterly, annual, and emergency safety checklists. Alternatively, the education and training manuals may include manuals for each type, facility, and industry, and may include, for example, disaster scenarios and disaster response contents for education and training. Safety inspection manuals may include manuals by type, facility, and industry.

Referring to FIG. 6 , in various embodiments, the computing device 100 provides a user or manager with a selection screen for an asset through the user terminal 410 or the manager terminal 200 to obtain pre-input information, Select information about an asset may be received.

In various embodiments, the computing device 100 stores a list of managed assets, and displays a screen for selecting an asset list and an asset through a display unit (not shown) of the user terminal 410 or the manager terminal 200. can provide

For example, when the computing device 100 provides a screen as shown in FIG. 6 to the user or manager through the user terminal 410 or the manager terminal 200 to protect a user's specific building from a disaster, the corresponding Based on the results of investigation and analysis of a specific building, information on selected assets to be managed can be received.

Furthermore, when there is no asset to be managed in the asset list, the computing device 100 may receive additional information of new assets added by a user or manager and additional information of a disaster response module corresponding to the new asset. In this case, the computing device 100 may provide an asset addition button and a disaster response module addition button through a display unit (not shown) of the user terminal 410 or the manager terminal 200 .

The pre-input information may include asset selection information from a pre-stored asset list, additional information on an asset not included in the pre-stored asset list, additional information on a disaster response module, and importance information for each asset. In various embodiments, the pre-input information may further include a user-customized asset-specific disaster response manual.

In process S110, the computing device 100 may configure a user-customized disaster response system based on the acquired pre-input information. Since users have different assets, the computing device 100 may configure different customized disaster response systems for each user. In other words, the computing device 100 may configure different disaster response systems according to assets managed by the user and provide them to the user.

Through this, the existing disaster response system provides a response system according to a disaster, whereas the disaster response system of the present invention can provide a response system according to an asset possessed by a user.

In various embodiments, the computing device 100 selects a plurality of first disaster response modules respectively matched to a plurality of assets based on pre-input information from among a plurality of preset disaster response modules, and selects a plurality of first disaster response modules selected. A user-customized disaster response system may be configured by calculating priorities for response modules.

The disaster response module may refer to an information set including information on a disaster response method for a specific asset or an instruction for executing a disaster response method for a specific asset in order to protect assets when a disaster occurs. . The computing device 100 operates/performs the disaster response module, thereby providing information capable of protecting the user's assets corresponding to the disaster response module from disasters or responding to disasters (eg, disaster response stored in the disaster response module). the act of sending the manual to the user).

Referring to FIG. 7 , the computing device 100 may store a plurality of preset disaster response modules in the storage 140 . Each disaster response module corresponds to each asset, and each disaster response module may include a response manual including action items to be taken in case of disaster for each corresponding asset. In this case, the disaster response module may correspond one-to-one to the asset.

In addition, each disaster response module may include different response manuals according to the urgency of the disaster. For example, the types of urgency of disasters include a safety level in which assets protected by real-time disasters are not in danger, a first emergency level with the lowest urgency of disasters as a situation in which prevention is required for real-time disasters, and a real-time disaster. It can be divided into the second emergency level, which requires immediate response, and the second emergency level, which has medium urgency, and the third emergency level, which has the highest disaster urgency, requiring immediate evacuation because it is dangerous to respond to real-time disasters. For reference, in the safety stage, the disaster response module may not operate/perform/activate.

In addition, the response manual may include a general manual having actions that can be generally taken to protect assets in case of a disaster, and a customized manual having actions that users want to take to protect their own assets.

In various embodiments, the computing device 100 reads a plurality of preset disaster response modules from the storage 140, and based on a plurality of assets requiring management in the pre-input information, a plurality of assets each matched to the plurality of assets. A first disaster response module may be selected.

In various embodiments, the computing device 100 may determine the priority of the selected plurality of first disaster response modules using at least one of the importance information for each asset input by the user and the pre-stored importance information for each asset. there is.

Alternatively, the computing device 100 may determine the priority of a plurality of assets based on evaluation results through preliminary investigation and analysis of the plurality of assets, and based on the priorities of the assets, the priority of the plurality of first disaster response modules. priorities can be determined.

The priority of multiple assets may be determined by the risk level of the asset based on the value of the asset, the degree of threat to the asset, and the degree of vulnerability to the asset. That is, the priority of an asset may be determined according to the risk level of the asset. The risk level of an asset can be determined as (value of the asset x threat level x vulnerability).

Here, the value of an asset is an indicator of confidentiality, which means the possibility of financial loss in the event of an asset leakage, an integrity index, which means the possibility of failure or financial loss in business performance in the event of asset loss, and a disability or financial loss until a replacement asset is put in. It can be determined by the availability indicator, which means the possibility of loss. Meanwhile, the degree of threat to an asset may be determined by the intensity of the threat of a disaster on the asset and the frequency of occurrence of the disaster. Also, the vulnerability of an asset may be determined by the importance of the asset input by the user.

The risk level of an asset is the risk acceptance stage in which the current risk is accepted and the potential loss cost is borne, the risk reduction stage in which measures to reduce the risk are adopted and implemented, and the risk aversion stage in which the risky process or business is abandoned without performing. This may include a risk shifting step, in which potential costs are transferred or allocated to a third party, such as by insurance or outsourcing. Here, for example, the asset priority may be determined in the order of a risk reduction step, a risk acceptance step, a risk transfer step, and a risk avoidance step. Depending on the priority of the asset, the priority of the disaster response module corresponding to the asset may also be determined.

Thereafter, the computing device 100 may configure a user-customized disaster response system including a plurality of first disaster response modules and their priority information.

Referring to FIGS. 8(a) and 8(b) , the computing device 100 may configure a disaster response system composed of different disaster response modules according to user assets. For example, in FIG. 8 (a), since user A has 'waterways' and 'electrical facilities' as assets, 'waterways' and 'electrical facilities' are selected as assets in the pre-input information, and the computing device 100 may configure a first disaster response system composed of a first disaster response module corresponding to 'waterway' and a 'second disaster response module' corresponding to 'electric facilities'. In FIG. 8(b), since user B has 'waterway', 'network equipment', and 'sprinkler' as assets, the computing device 100 selects 'waterway', 'network equipment', and 'sprinkler'. A second disaster response system composed of a first disaster response module, a third disaster response module, and an Nth disaster response module corresponding to may be configured.

In various embodiments, the computing device 100 may determine a priority among a plurality of first disaster response modules belonging to the disaster response system based on the importance information for each asset belonging to the pre-input information. Here, the reason for determining the priority among the first plurality of disaster response modules belonging to the disaster response system is that when a disaster occurs, one of the plurality of first disaster response modules belonging to the disaster response system is preferentially performed so that the user or This is because it is a criterion for determining whether to provide a response manual to the person in charge.

In various embodiments, after setting a user-customized disaster response system, the computing device 100 acquires, from a user, information about a customized manual for each asset for each of a plurality of first disaster response modules belonging to the disaster response system, or pre-inputs A customized manual for each asset can be obtained from the information.

Thereafter, the computing device 100 may start disaster monitoring using the set disaster response system.

Hereinafter, a method for the computing device 100 to determine whether a disaster has occurred and operate the disaster response system according to the disaster using the set disaster response system will be described.

9 is a conceptual diagram of a disaster monitoring method using a disaster response system according to an embodiment of the present invention. 10 illustrates an example of disaster classification. 11 is an example of disaster related information for each asset. 12 is an example of a screen provided to a user or a person in charge through operation of a disaster response module.

Referring to FIG. 4 , in a process S120 , the computing device 100 may acquire disaster monitoring information for determining whether a disaster has occurred.

Referring to FIG. 9 , disaster monitoring information includes public data provided by public institutions or research institutes, user sensing data measured by sensors installed by users, IT monitoring data measured from security devices of IT facilities, and users or personnel in charge. It may include at least one of periodic inspection data periodically input by

In various embodiments, the computing device 100 may perform a process of acquiring public data provided by the government or a research institute to obtain disaster monitoring information; obtaining user sensing data measured through a sensing device installed by the user; obtaining IT monitoring data measured from security devices of IT facilities; and a process in which a person in charge periodically obtains periodic inspection data on an asset.

Public data may refer to data that is difficult for individuals to measure, such as environmental data such as weather/climate provided by the Korea Meteorological Administration, measured by the government, research institutes, etc. and disclosed to the public. Meanwhile, the user sensing data may refer to data measured by a camera, a temperature sensor, an air pressure sensor, or the like, which is directly installed around the user's asset.

On the other hand, if there is an IT facility among the user's assets, IT monitoring data refers to the data obtained by monitoring and analyzing external signals passing through the IT facility by an IT supplementary device, and includes the inflow of malicious codes, DDoS attacks, and ransomware attacks. It may include sensing data for the like.

On the other hand, the regular inspection data may include inspection contents, inspection photos, etc. of the corresponding asset for each daily/weekly/monthly/quarterly/yearly/set period by the user or person in charge. To this end, the computing device 100 periodically or every set period provides a checklist and inspection details for the asset to the user or person in charge, and may alarm requesting input of photos before and after the inspection of the asset, allowing the user to Alternatively, the checked checklist input by the person in charge, inspection details, and photos before and after inspection may be stored in the storage 140 .

Referring to FIGS. 4 and 9 , in process S130 , the computing device 100 may determine a disaster occurring in real time based on disaster monitoring information.

In various embodiments, the computing device 100 may monitor at least one of natural disasters, social disasters, and cyber disasters in real time. Here, the computing device 100 may independently determine whether a disaster has occurred and the type of disaster. In another example, at least one of the manager 200, the external server 300, and the user 410 determines whether or not a disaster has occurred and the type of disaster, and determines whether or not a disaster has occurred and the type of disaster is received by the computing device 100. The type of disaster may be determined, and in this case, process S130 may be excluded.

Referring to FIG. 10 , disasters can be divided into physical disasters and cyber disasters. Physical disasters can be classified into natural disasters and social disasters. For example, natural disasters may include typhoons, floods, heavy rain, strong winds, wind waves, tidal waves, heavy snow, cold waves, lightning strikes, droughts, heat waves, earthquakes, and yellow dust. For example, social disasters may include fires, collapses, explosions, traffic accidents, chemical, biological and radiological accidents, environmental pollution accidents, infectious diseases, epidemics, and the like. For example, cyber disasters may include malicious code, DDoS, phishing, ransomware, and SQL injection.

In various embodiments, the disaster information generated by the computing device 100 in real time may include information on at least one of the type of disaster as well as the date and time, type, content of occurrence, development, damage situation and scale of the disaster or crisis situation. there is.

In various embodiments, the computing device 100 may receive and obtain information on real-time disasters from the external server 300 . In this case, the external server 300 may be an electronic device that specializes in detecting and analyzing disasters occurring in real time.

In various embodiments, the computing device 100 may determine a disaster occurring in real time by utilizing an artificial intelligence model learned based on disaster monitoring information.

Here, the artificial intelligence model is composed of one or more network functions, and the one or more network functions may be composed of a set of interconnected computational units generally referred to as 'nodes'. These 'nodes' may also be referred to as 'neurons'. One or more network functions include at least one or more nodes. Nodes (or neurons) constituting one or more network functions may be interconnected by one or more 'links'.

In an artificial intelligence model, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting an input node and an output node may have a weight. The weight may be variable, and may be variable by a user or an algorithm in order to perform a function desired by the artificial intelligence model. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the AI model, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the AI model. Characteristics of the artificial intelligence model may be determined according to the number of nodes and links in the artificial intelligence model, the relationship between the nodes and links, and the value of weight assigned to each link. For example, when there are two artificial intelligence models having the same number of nodes and links and different weight values between the links, the two artificial intelligence models may be recognized as different from each other.

Some of the nodes constituting the artificial intelligence model may constitute one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in an artificial intelligence model may be defined in a different way from the above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the artificial intelligence model. Alternatively, in an artificial intelligence model network, in a relationship between nodes based on a link, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the artificial intelligence model. Also, the hidden node may refer to nodes constituting an artificial intelligence model other than the first input node and the last output node. An artificial intelligence model according to an embodiment of the present invention may have more nodes of an input layer than nodes of a hidden layer close to an output layer, and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. can

AI models can include one or more hidden layers. A hidden node of a hidden layer may use outputs of previous layers and outputs of neighboring hidden nodes as inputs. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes of the input layer may be determined based on the number of data fields of the input data and may be the same as or different from the number of hidden nodes. Input data input to the input layer may be operated by a hidden node of the hidden layer and may be output by a fully connected layer (FCL) that is an output layer.

In addition, the artificial intelligence model may be trained in at least one of supervised learning, unsupervised learning, and semi-supervised learning. Training of AI models is aimed at minimizing errors in the output. In the learning of the artificial intelligence model, the training data is repeatedly input into the artificial intelligence model, the output of the artificial intelligence model for the training data and the error of the target are calculated, and the error of the artificial intelligence model is converted into the artificial intelligence model in the direction of reducing the error. This is the process of backpropagating from the output layer to the input layer to update the weight of each node of the AI model.

In the case of teacher learning, each learning data is labeled with the correct answer (ie, labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Training data is input to the artificial intelligence model, and an error can be calculated by comparing the output (category) of the artificial intelligence model with the label of the training data.

As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with an artificial intelligence model output. The calculated error is back-propagated in the reverse direction (ie, from the output layer to the input layer) in the AI model, and the connection weight of each node in each layer of the AI model can be updated according to the back-propagation.

Here, the computing device 100 may learn the artificial intelligence model by using the accumulated disaster monitoring information as learning data. The learned artificial intelligence model may receive disaster monitoring information obtained in real time and provide at least one of whether a disaster has occurred, the date and time of the disaster, the type of the disaster, and the magnitude of the disaster as an output.

Referring to FIGS. 4 and 9 , in process S140 , the computing device 100 may determine the relevance and urgency of a disaster in real time.

In detail, the computing device 100 may determine the correlation with the plurality of first disaster response modules based on a real-time disaster.

Referring to FIG. 11 , the computing device 100 may store disaster information related to each asset in the storage 140 . Disaster information related to each asset may include a plurality of disaster lists that are related to a specific asset. Through this, since certain assets are affected by a disaster that occurred in real time, it is possible to classify assets that require response. Taking the table of FIG. 11 as an example, since the 'waterway' asset needs to respond to disasters such as 'typhoon', 'flood', and 'heavy rain', the 'waterway' asset is divided into 'typhoon', 'flood', 'heavy rain' It is related to the same multiple disasters.

Accordingly, the computing device 100 may perform only a plurality of second disaster response modules corresponding to assets having a relationship with a disaster occurring in real time from among a plurality of first disaster response modules belonging to a set disaster response system. In summary, the computing device 100 may check assets related to the type of disaster that occurred in real time by utilizing disaster information related to each asset, and operate only the disaster response module corresponding to the related assets.

In various embodiments, the computing device 100 may determine the urgency of the real-time disaster for the plurality of first disaster response modules based on the real-time disaster.

Here, the computing device 100 may determine the urgency of a real-time disaster affecting assets. The urgency of a disaster can be determined by various variables. For example, variables for determining the urgency of a disaster include the time of disaster, type of disaster, location of disaster, location of assets, distance between disaster and assets, scale of disaster, development of disaster, scale of disaster It may include at least one of the like.

Here, the computing device 100 may use the urgency determination variable to determine the level of urgency that a real-time disaster affects a specific asset. Types of urgency of disasters include the safety level in which a specific asset is not at all threatened by a real-time disaster, the first emergency level with the lowest urgency of a disaster as a situation in which prevention is required to protect assets against real-time disasters, and the real-time occurrence A situation in which an immediate response is required to protect assets in a disaster. The 2nd emergency stage, in which the urgency of the disaster is medium. It can be classified as the third highest emergency level.

In process S150, the computing device 100 may perform a disaster response module. In detail, the computing device 100 may perform a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to relevance and urgency.

In various embodiments, the computing device 100 may select a plurality of second disaster response modules corresponding to assets that are related to real-time disasters and operate the plurality of second disaster response modules according to the urgency of the disaster. .

In various embodiments, the computing device 100 operates each disaster response module belonging to a plurality of second disaster response modules based on the priority of the plurality of first disaster response modules generated when setting the disaster response system. can

Referring to FIG. 12 , in various embodiments, the computing device 100 transmits a response manual according to an emergency step for each disaster response module to a user or a person in charge through a user terminal 410 or a terminal in charge of the corresponding asset 420. can be provided to Here, the response manual may have information about the location of the asset, inspection items and inspection items of the asset, maintenance and repair methods, and the like.

In addition, the computing device 100 may provide an upload screen for uploading photos before and after responding to assets together with a response manual, and a user or person in charge may upload photos of the current situation of the asset and the situation after management. there is.

In various embodiments, the computing device 100 may provide information on an evacuation plan, an evacuation route, etc. in response to a disaster to a general employee through the general employee terminal 430 other than the user and the person in charge.

In detail with reference to FIG. 12, a first disaster response system customized for user A is in operation, and the first disaster response system may have a first disaster response module and a second disaster response module. A first asset corresponding to the first disaster response module may have a relationship with 'typhoon' and 'heavy rain', and a second asset corresponding to the second disaster response module may have a relationship with 'fire' and 'explosion'. In this case, the computing device 100 performs a first disaster response module corresponding to a first asset having a relationship with 'heavy rain' in the first disaster response system when it is determined that 'heavy rain' has occurred in real time, When the urgency of real-time 'heavy rain' for an asset is the second emergency level, the occurrence of 'heavy rain' and the second general and customized manual stored in the first disaster response module are provided to the user and the person in charge, and other general Information on 'heavy rain' occurrence and evacuation routes can be provided to employees so that they can evacuate safely.

Referring to FIGS. 4, 9, and 12 , in step S160, the computing device 100 may receive a disaster response result for an asset. The disaster response result for the asset may include information about the inspection items checked by the user or the person in charge of the asset, implementation information about the inspection items, and pictures of the asset before and after the inspection.

Hereinafter, the AI-based user-customized disaster risk prediction and safety management method according to an embodiment of the present invention may provide a method for evaluating the performance of a disaster response system and training a person in charge.

13 is a flowchart of a method for evaluating disaster response system performance and training personnel in accordance with an embodiment of the present invention. 14 is an example of disaster scenarios and disaster response contents for education and training.

Referring to FIGS. 9 and 13 , in process S210 , the computing device 100 may generate a disaster scenario for education and training. Process S210 may be performed after process S120 and before process S130.

In various embodiments, the computing device 100 may create a real-time disaster based on a training disaster scenario previously stored in the storage 140 or input by a user in real time.

Referring to FIG. 14 , a disaster scenario for education and training may include information about the date and time, type, occurrence, development, damage situation and scale of a disaster or crisis situation.

The computing device 100 may generate a real-time disaster based on a disaster scenario for educational training selected by a user. Thereafter, the computing device 100 may perform process S130.

Referring to FIG. 13 , in a process S220 , the computing device 100 may provide disaster response contents for education and training to a staff terminal 420 or a general staff terminal 430 .

Disaster response contents for education and training may include educational materials for responding to a virtual disaster caused by a disaster scenario for education and training. For example, disaster response contents for education and training may consist of videos, images, documents, etc., and may include disaster-specific education and training, situation-specific education and training, and VR/AR utilization education and training materials.

Here, the computing device 100 may or may not perform process S220 according to the user's selection. For example, the computing device 100 may sequentially perform steps S210, S220, and S130, or may perform steps S210 and S130 except for step S220. Process S220 may be performed after process S120 and before process S130.

15 is an example of a real-time disaster prediction management monitoring screen provided on a display of a user or manager terminal.

Referring to FIG. 15 , the computing device 100 may provide disaster situation information to the user and manager in real time through the display units of the user terminal 410 and the manager terminal 200 . The display unit 100 of the user terminal 410 and the manager terminal 200 displays physical disaster information, cyber disaster information, contents of inspection items for assets on a regular basis or in the event of a disaster, manuals for disasters, and contents for education and training. , at least one of disaster alarm information may be provided.

Although the present invention has been described with reference to the embodiments shown in the drawings, this is only exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the attached claims.

100: computing device
200: manager terminal
300: external server
410: user terminal
420: person in charge terminal
430: general staff terminal

Claims (7)

In a method performed by a computing device,
obtaining pre-input information about a plurality of assets being managed by a user;
Based on the pre-input information, a plurality of first disaster response modules each matched with the plurality of assets are selected from among a plurality of preset disaster response modules, and priorities for the selected plurality of first disaster response modules are calculated. A process of constructing a user-customized disaster response system, -One disaster response module includes information on a disaster response method for one asset-;
Acquiring disaster monitoring information to determine whether a disaster has occurred in real time;
determining a real-time disaster based on the disaster monitoring information;
determining the urgency of the real-time disaster for the plurality of first disaster response modules and the correlation with the plurality of first disaster response modules based on the real-time disaster;
performing a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to the correlation and the urgency; and
Including the process of receiving a disaster response result based on the plurality of second disaster response modules,
AI-based user-customized disaster risk prediction and safety management method. According to claim 1,
After the process of configuring a user-customized disaster response system,
Further comprising the process of obtaining a customized manual for each asset for each of the selected plurality of first disaster response modules,
AI-based user-customized disaster risk prediction and safety management method. According to claim 1,
The process of obtaining disaster monitoring information for determining whether a disaster has occurred in real time,
The process of obtaining public data provided by governments or research institutes;
obtaining user sensing data measured through a sensing device installed by the user;
obtaining IT monitoring data measured from security devices of IT facilities; and
Including the process of obtaining periodic inspection data on assets on a regular basis by the person in charge,
The process of determining a real-time disaster based on the disaster monitoring information,
Including the process of determining the type of the real-time disaster using at least one of the public data, the user sensing data, the IT monitoring data, and the regular inspection data,
AI-based user-customized disaster risk prediction and safety management method. According to claim 1,
The process of determining the relevance of the plurality of first disaster response modules and the urgency of the real-time disaster for the plurality of first disaster response modules based on the real-time disaster,
selecting the plurality of second disaster response modules having relevance to the real-time disaster from among the plurality of first disaster response modules; and
Including the process of determining the urgency of a disaster occurring in real time for the plurality of first disaster response modules,
AI-based user-customized disaster risk prediction and safety management method. According to claim 1,
Further comprising a process of generating the real-time disaster based on a pre-stored disaster scenario for training for disaster response training,
AI-based user-customized disaster risk prediction and safety management method. processor;
network interface;
Memory; and
A computer program loaded into the memory and executed by the processor,
The computer program,
obtaining instructions for acquiring pre-input information on a plurality of assets being managed by a user;
Based on the pre-input information, a plurality of first disaster response modules each matched with the plurality of assets are selected from among a plurality of preset disaster response modules, and priorities for the selected plurality of first disaster response modules are calculated. Instructions constituting a user-customized disaster response system, -One disaster response module includes information on a disaster response method for one asset-;
An instruction for obtaining disaster monitoring information for determining whether a disaster has occurred in real time;
an instruction for determining a real-time disaster based on the disaster monitoring information;
an instruction for determining correlation with the plurality of first disaster response modules and urgency of the real-time disaster with respect to the plurality of first disaster response modules based on the real-time disaster;
instructions for executing a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to the correlation and the urgency; and
Including instructions for receiving a disaster response result based on the plurality of second disaster response modules,
AI-based user-customized disaster risk prediction and safety management device. Combined with a computing device,
obtaining pre-input information about a plurality of assets being managed by a user;
Based on the pre-input information, a plurality of first disaster response modules each matched with the plurality of assets are selected from among a plurality of preset disaster response modules, and priorities for the selected plurality of first disaster response modules are calculated. A process of constructing a user-customized disaster response system, -One disaster response module includes information on a disaster response method for one asset-;
Acquiring disaster monitoring information to determine whether a disaster has occurred in real time;
determining a real-time disaster based on the disaster monitoring information;
determining the urgency of the real-time disaster for the plurality of first disaster response modules and the correlation with the plurality of first disaster response modules based on the real-time disaster;
performing a plurality of second disaster response modules among a plurality of first disaster response modules belonging to the disaster response system according to the correlation and the urgency; and
Stored in a computer-readable recording medium to execute a process of receiving a disaster response result based on the plurality of second disaster response modules,
AI-based user-customized disaster risk prediction and safety management computer program.

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