KR102268157B1 - Method and apparatus for predicting high risk area for infectious disease - Google Patents

Abstract

An embodiment of the present invention is a method of predicting a high-risk area for an infectious disease among a specific area performed by an electronic device. The method comprises: dividing the specific region into unit cells of a preset size; obtaining floating population information for the specific area; deriving floating population information for each unit cell based on the floating population information; obtaining information on the occurrence of infection of an infectious disease in the specific region; deriving infection occurrence information for each unit cell based on the infection occurrence information of the infectious disease; and deriving high-risk area information for each unit cell for the specific region based on the floating population information for each unit cell, information on multiple-use facilities for each unit cell for the specific region, and infection occurrence information for each unit cell.

Description

Method and device for predicting high-risk areas for infectious diseases {METHOD AND APPARATUS FOR PREDICTING HIGH RISK AREA FOR INFECTIOUS DISEASE}

The present invention relates to a method and apparatus for predicting high-risk areas for infectious diseases using big data.

Infectious diseases are one of the most important problems related to human health, and mankind is making great efforts to treat and prevent various infectious diseases. In order to cope with an infectious disease, an infectious disease analysis and prevention system is needed that can take precautionary measures to prevent the outbreak of an infectious disease and take measures to end the epidemic when it occurs.

However, general infectious disease analysis and prevention systems simply provide information on risk areas using past infection data as static data, and do not properly consider places/buildings and floating populations where people gather, so it is effective data as a practical precautionary measure. There is a problem that is difficult to say.

An object of the present invention for solving the above problems is to provide a method for predicting a high-risk area for an infectious disease.

Another object of the present invention to solve the above problems is to provide an apparatus for predicting a high-risk area for an infectious disease.

To achieve the above object, a method for predicting a high-risk area for an infectious disease according to an embodiment of the present invention includes the steps of dividing the specific area into unit cells of a preset size, obtaining floating population information for the specific area, and the deriving the floating population information for each unit cell based on the floating population information; obtaining information on the occurrence of infection of an infectious disease for the specific region; and obtaining information on the occurrence of infection for each unit cell based on the information on the occurrence of infection of the infectious disease. Deriving high-risk area information for each unit cell for each unit cell based on the step of deriving and floating population information for each unit cell, multi-use facility information for each unit cell for the specific region, and infection occurrence information for each unit cell may include the step of

Here, the multi-use facility information per unit cell may include information counting the number of multi-use facilities per unit cell, and the floating population information is an average floating population per hour based on floating population information for a preset time. may contain information.

Here, the steps of obtaining information about the buildings in the specific area, deriving the status information of the buildings for each unit cell based on the address information of the buildings included in the information on the buildings, and the information on the buildings The method may further include deriving multi-use facility information for each unit cell based on the included usage information for each floor of the buildings.

Here, the step of deriving the high-risk area information for each unit cell for the specific area is a step of deriving first ranking information based on the multi-use facility information for each unit cell, the second based on the floating population information for each unit cell deriving ranking information, deriving third ranking information based on infection occurrence information of infectious diseases for each unit cell, unit cell based on the first ranking information, the second ranking information, and the third ranking information It may include deriving the high-risk area prediction index information for each unit cell and deriving the high-risk area information for each unit cell based on the high-risk area prediction index information for each unit cell.

Here, obtaining information on a plurality of medical facilities in the specific area, deriving at least one first medical facility capable of responding to the infectious disease based on the information on the plurality of medical facilities, the unit deriving the sum of the distances from the location corresponding to the cell to the location of the at least one first medical facility to derive medical facility distance information for each unit cell, and the high-risk area prediction index information for each unit cell and the unit cell The method may further include deriving infectious disease response index information for each unit cell based on the distance information from the medical facility.

According to an embodiment of the present invention, an apparatus for predicting a high-risk area for an infectious disease for achieving the above other object includes at least one processor and a memory for storing at least one instruction executed by the at least one processor. ), wherein the at least one command is executed to classify the specific area into unit cells of a preset size, and is executed to obtain floating population information for the specific area, and based on the floating population information, the unit cell is executed to derive information on each floating population, is executed to acquire infection occurrence information of an infectious disease for the specific region, is executed to derive infection occurrence information for each unit cell based on the infection occurrence information of the infectious disease, and the unit Based on the floating population information for each cell, the multi-use facility information for each unit cell for the specific region, and the infection occurrence information for each unit cell, the high-risk area information for each unit cell for the specific region may be derived.

Here, the multi-use facility information per unit cell may include information counting the number of multi-use facilities per unit cell, and the floating population information is an average floating population per hour based on floating population information for a preset time. may contain information.

Here, the at least one command is executed to obtain information about the buildings in the specific area, and based on the address information of the buildings included in the information on the buildings, it is executed to derive the status information of the buildings for each unit cell and, based on the use information for each floor of the buildings included in the information about the building, it may be executed to derive information on multiple-use facilities for each unit cell.

Here, the at least one command is executed to derive first ranking information based on the multi-use facility information for each unit cell, and is executed to derive second ranking information based on the floating population information for each unit cell, the It is executed to derive the third ranking information based on the infection occurrence information of the infectious disease for each unit cell, and the high-risk area prediction index information for each unit cell based on the first ranking information, the second ranking information, and the third ranking information It may be executed to derive, and may be executed to derive the high-risk area information for each unit cell based on the high-risk area prediction index information for each unit cell.

Here, the at least one command is executed to obtain information on a plurality of medical facilities within the specific area, and at least one first medical facility capable of responding to the infectious disease based on the information on the plurality of medical facilities. is executed to derive, and is executed to derive the medical facility distance information for each unit cell by deriving the sum of the distances from the position corresponding to the unit cell to the position of the at least one first medical facility, and the high risk for each unit cell It may be executed to derive the infectious disease response index information for each unit cell based on the regional prediction index information and the medical facility distance information for each unit cell.

According to the present invention, it is possible to effectively provide a high-risk area vulnerable to an infectious disease by analyzing the use data and floating population for each building in the area.

According to the present invention, the public can easily check information on a high-risk area for an infectious disease to avoid the area, thereby preventing the spread of an infectious disease.

According to the present invention, information on a high-risk area for an infectious disease can provide a real-time risk level through real-time floating population information as well as a prediction through existing data, so that it is possible to flexibly respond to the spread of an infectious disease.

According to the present invention, by providing an infectious disease response index according to a location based on medical facility information in a specific area, it is possible to make the user aware in advance of the level of coping when an infection of an infectious disease occurs in the corresponding location or area.

1 is a block diagram illustrating a method for predicting a high-risk area for an infectious disease according to an embodiment of the present invention.
2 is a block diagram illustrating a method of deriving information on an infectious disease vulnerable area according to an embodiment of the present invention.
3 is a diagram illustrating a unit cell according to an embodiment of the present invention.
4 is a diagram illustrating a state in which a specific region is divided into unit cells according to an embodiment of the present invention.
5 is a view showing a building ledger for information about a building according to an embodiment of the present invention.
6 is a diagram illustrating an analysis data set according to an embodiment of the present invention.
7 is a diagram illustrating a data set corresponding to information on multiple-use facilities for each unit cell obtained by processing an analysis data set based on a unit cell according to an embodiment of the present invention.
8 is a diagram illustrating a visualization of an infectious disease vulnerable area for each unit cell based on multi-use facility information for each unit cell according to an embodiment of the present invention.
FIG. 9 is a diagram illustrating a visualization of TOP100 of an infectious disease vulnerable area for each unit cell in a specific area based on information on multiple-use facilities for each unit cell according to an embodiment of the present invention.
10 is a diagram illustrating floating population information for each unit cell according to an embodiment of the present invention.
11 is a diagram illustrating a data set for deriving a correlation between information on multiple-use facilities for each unit cell and floating population information for each unit cell according to an embodiment of the present invention.
12 is a diagram for explaining the correlation between information on multiple-use facilities for each unit cell and floating population information for each unit cell according to an embodiment of the present invention.
13 is a block diagram illustrating a method of deriving high-risk area information for each unit cell according to an embodiment of the present invention.
14 is a diagram illustrating a data set corresponding to high-risk area information for each unit cell including a high-risk area prediction index according to an embodiment of the present invention.
15 is a diagram visualizing TOP10, TOP50, and TOP100 of a high-risk area for each unit cell in a specific area based on a high-risk area prediction index for each unit cell according to an embodiment of the present invention.
16 is a diagram illustrating medical facilities in a specific area for deriving an infectious disease response index according to an embodiment of the present invention.
17 is a diagram illustrating a plurality of clusters for deriving an infectious disease response index according to an embodiment of the present invention.
18 is a diagram illustrating a data set corresponding to the derived infectious disease response index information for each unit cell according to an embodiment of the present invention.
19 is a block diagram of an apparatus for predicting a high-risk area for an infectious disease according to an embodiment of the present invention.
20 is a flowchart of a method for predicting a high-risk area for an infectious disease according to an embodiment of the present invention.

Since the present invention can have various changes and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. In describing each figure, like reference numerals have been used for like elements.

Terms such as first, second, A, and B may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. The term “and/or” includes a combination of a plurality of related listed items or any of a plurality of related listed items.

When a component is referred to as being “connected” or “connected” to another component, it is understood that the other component may be directly connected or connected to the other component, but other components may exist in between. it should be On the other hand, when it is mentioned that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings. In describing the present invention, in order to facilitate the overall understanding, the same reference numerals are used for the same components in the drawings, and duplicate descriptions of the same components are omitted.

1 is a block diagram illustrating a method for predicting a high-risk area for an infectious disease according to an embodiment of the present invention.

Referring to FIG. 1 , an embodiment of the present invention can predict a high-risk area for an infectious disease within a specific area through a series of procedures, and further derive and provide an infectious disease response index. Here, the high-risk area and the infectious disease response index can be provided by predicting/deriving the specific area in units of cells divided by a preset size, which will be described in detail with reference to FIGS. 3 and 4 .

In one embodiment, in order to predict a high-risk area, information about an infectious disease vulnerable area and floating population information may be first obtained, and spatial density may be analyzed based on each information. Here, information on areas vulnerable to infectious diseases can be derived based on building ledger information, which will be described in detail with FIG. 2 . In addition, the floating population information may include location information based on communication data obtainable from a communication company. In particular, the floating population information may include historical information on a monthly, daily, or hourly basis, and may also include real-time information. In addition, a method of analyzing spatial density based on each piece of information will be described in detail with FIGS. 9 and 10 .

Thereafter, in one embodiment, correlation analysis may be performed based on the information on the infectious disease vulnerable area and the floating population information according to the spatial density analysis result in order to utilize the two information to predict the high-risk area, and to check the correlation between the vulnerable area and the floating population. can This will be described in detail with reference to FIGS. 11 and 12 .

In one embodiment, information on the occurrence status of infectious diseases can be obtained, and the number of infectious disease outbreaks in a specific region can be derived through this, and an analysis data set can be generated by processing the above-mentioned vulnerable area information and floating population information together. have. Thereafter, an embodiment may predict a high-risk area within a specific area based on the analysis data set, and visually provide it to the user terminal. This will be described in detail with reference to FIGS. 13 to 15 .

Furthermore, according to an embodiment, an infectious disease response index of a specific area may be derived based on the derived high-risk area prediction result and medical facility status information, and this may be provided to the user terminal. This will be described in detail with reference to FIGS. 16 to 18 .

2 is a block diagram illustrating a method of deriving information on an infectious disease vulnerable area according to an embodiment of the present invention.

Referring to FIG. 2 , according to an embodiment, information on a building ledger in a specific area may be acquired in order to derive information on areas vulnerable to infectious diseases. In addition, according to an embodiment, information on the use of each floor of a building can be derived from the building ledger information, and through this, a multi-use facility in a specific area can be analyzed.

Here, in an embodiment, since the analysis can be performed in units of cells according to a preset size, a specific region can be firstly divided into a unit area, that is, in units of cells. This will be described in detail later with reference to FIGS. 3 and 4 . Thereafter, according to an embodiment, a multi-use facility for each cell may be analyzed based on the use information for each floor of the building. According to an embodiment, information on multiple-use facilities for each unit cell may be obtained through the analysis, and this may be used to predict the high-risk area. This will be described in detail later with reference to FIGS. 4 to 8 .

3 is a diagram illustrating a unit cell according to an embodiment of the present invention, and FIG. 4 is a diagram illustrating a state in which a specific region is divided into unit cells according to an embodiment of the present invention.

According to an embodiment of the present invention, a specific region may be divided into cells of a preset size. Here, the area of the cell, that is, the size of the cell may be set in a square shape of 500 m x 500 m based on the measured distance. A specific region divided by such a cell unit may be represented as shown in FIG. 4 .

Since the data processing procedure and result derivation according to an embodiment are performed for each cell, the area or size of the cell is not limited to the 500m×500m, and may be modified by an administrator or the like. However, the area or size of the cell may be determined to be a size capable of preventing excessive computational work and deriving effective result information.

Referring to FIG. 3 , the vulnerable area information according to an embodiment may be derived in the cell unit based on the building ledger information, but since the floating population information is information obtained from a telecommunication company, the cell unit of the initial floating population information is It may be different from the cell unit used for the vulnerable area information, such as a size of 50m x 50m. In this case, an embodiment may scale the cell unit of the initial floating population information to the same size as the cell unit used in the embodiment. For example, if the size of the unit cell for regional analysis is 500m x 500m and the size of the unit cell of the initial floating population information is 50m x 50m, the size of the cell of the initial floating population information can be scaled to x100. have. According to an embodiment, initial floating population information may be processed according to x100 scaling to derive floating population information to be used in a subsequent procedure.

5 is a view showing a building ledger for information about a building according to an embodiment of the present invention.

An embodiment of the present invention may acquire information about a building for deriving vulnerable area information based on building ledger information of a specific area. Here, the information about the building may include usage information for each floor of the building.

For example, referring to FIG. 5 , an embodiment may acquire/collect collective building ledger information for buildings located in a specific area, and the street name address information and floor-specific usage information of the building included in the collective building ledger information can be extracted. Here, the collective building ledger information can be obtained through the building data private open system. The use information for each floor may include use and/or area information according to each floor of the building. Here, the collective building ledger information may be referred to as building ledger information.

For example, the use information for each floor for a three-story building may include information on the first floor, information on the second floor, and information on the third floor, and may include use information and area information for each information. have. Also, each piece of information constitutes different information depending on the floor, but may include the same address information as it is the same building.

Here, the use information may indicate a neighborhood living facility or a business facility, and may also include information on a more detailed use by additionally denoted by "how many". Alternatively, detailed information about the purpose of the floor may also be obtained, for example, through an external server. For example, the use information may indicate a use such as a private academy, a billiard room, an office, a clinic, and the like.

According to an embodiment, it may be determined whether a multi-use facility exists for each floor based on the use information for each floor of the buildings, and only information on a floor corresponding to the multi-use facility may be extracted.

Here, the process of determining whether a multi-use facility exists may be performed based on the use information and a preset multi-use facility classification table, and the multi-use facility classification table may be set as shown in Table 1 below, but may be changed by Or, it may change according to the Infectious Disease Countermeasure Headquarters or policy.

That is, in one embodiment, it is possible to determine the existence of multi-use facilities of buildings in a specific area based on the multi-use facility classification table and use information, and extract information on buildings/floors in which multi-use facilities exist, which will be described later. It is possible to construct an analysis data set that

6 is a diagram illustrating an analysis data set according to an embodiment of the present invention.

According to an embodiment, an analysis data set may be generated based on address information obtained based on building ledger information, use information for each floor, and area information for each floor.

Referring to FIG. 6 , the analysis data set may include information on whether it is a multi-use facility, location information of a unit cell, and identification information of a unit cell, as well as information based on building target information.

For example, the address information may include information indicating a street name address, city-gu-gun code information, eup-myeon-dong code information, and floor information, and the use information for each floor may include use information of the corresponding floor and code information according to the purpose. . Also, the area information for each layer may include area information of the corresponding layer.

The information on whether a multi-use facility is a multi-use facility may include information indicating whether the corresponding floor is a multi-use facility, but filtering was performed based on the multi-use facility classification table in FIG. 5 for the configuration of the analysis data set. It can indicate information about the facility being used. Here, information on whether a multi-use facility is a multi-use facility may indicate all 1's. However, when the facility items on the multi-use facility classification table are divided into a plurality of grades and weights are set according to grades, the weights may be reflected and a higher value may be assigned to a risky multi-use facility. In this case, the information on whether it is a multi-use facility may be referred to as risk level related information of the multi-use facility.

The location information of the unit cell may include location information using a World Geodetic System (WGS). For example, the location information may include GPS information measured using the WGS84 ellipsoid.

The identification information of the unit cell may include information indicating which cell the building is included in among a plurality of unit cells in which a specific area is previously divided, and an x-coordinate and y-coordinate within a specific area with respect to the reference position of the unit cell. It may include coordinates, and may include ID information or unique identification information for a unit cell. Here, the reference position of the unit cell may indicate the center point of the unit cell, and each building may be determined in which cell is included based on the position of the center point of the building.

7 is a view showing a data set corresponding to multi-use facility information for each unit cell processed by processing an analysis data set based on the unit cell according to an embodiment of the present invention, and FIG. It is a diagram that visualizes areas vulnerable to infectious diseases for each unit cell based on information on multiple-use facilities for each unit cell.

An embodiment of the present invention may be further processed on a unit cell basis using the analysis data set described with reference to FIG. 6 . That is, in the analysis data set, information was classified based on the building/floor, but it can be reclassified based on the unit cell.

For example, if there are three floors of a building divided into multi-use facilities, the analysis data set of FIG. 6 may consist of three pieces of information, but the additionally processed data set of FIG. 7 consists of one piece of information can be Alternatively, if the first building and the second building divided into multi-use facilities exist, the analysis data set of FIG. 6 may consist of two pieces of information, but the additionally processed data set of FIG. 7 includes the first building and the second building. When a building is included in the same unit cell, it may consist of one piece of information.

That is, according to an embodiment, data extracted to multiple-use facilities for each unit cell may be collected, and the additionally processed dataset as shown in FIG. 7 includes unit cell identification information, unit cell location information, and multiple-use facilities within the unit cell. It may include information on the number of , and information on the class of vulnerable areas.

For example, identification information of a unit cell and location information of a unit cell may be the same as information in FIG. 6 .

Information on the number of multi-use facilities in a unit cell may indicate information on how many facilities are classified as multi-use facilities in a cell using the analysis data set of FIG. 6 . However, when the facility items on the multi-use facility classification table are classified by grade and a weight is set, the information on the number of multi-use facilities in a unit cell does not simply count the multi-use facilities, but the higher the number of dangerous multi-use facilities A combination of the allocated information may indicate information indicating the degree of danger that multiple-use facilities are clustered. That is, in this case, information on the number of multi-use facilities in the unit cell may be referred to as risk level information related to multi-use facilities in the unit cell.

Vulnerable area grade information may be determined based on information on the number of multi-use facilities in a unit cell. That is, one of a plurality of preset levels may be determined according to the number of multi-use facilities in a unit cell. For example, the plurality of grades may include 5 grades, and when the number of multiple-use facilities in a unit cell is 400 or more, it is determined as Grade 1, and the number of multiple-use facilities in a unit cell is 200 or more 400 If the number of multi-use facilities in a unit cell is less than 80 but less than 200, it is determined as Level 3, and when the number of multi-use facilities in a unit cell is between 30 and less than 80, it is determined as Level 4 and if the number of multi-use facilities in a unit cell is 1 or more and less than 30, it may be determined as 5 grades.

For example, when the facility items on the multi-use facility classification table are classified by grade and a weight is set, the vulnerable area grade information is information indicating the degree of danger because the multi-use facility is clustered, that is, related to the multi-use facility in the unit cell. It may also be determined based on risk level information.

The vulnerable area grade information is determined for each unit cell, and when visually expressed in a specific area, it can be represented as shown in FIG. 8 . That is, according to an embodiment, a different color may be given to a unit cell within a specific area according to the vulnerable area grade, and information on the specific area indicating the vulnerable area grade may be provided to the user terminal.

FIG. 9 is a diagram illustrating a visualization of TOP100 of an infectious disease vulnerable area for each unit cell in a specific area based on information on multiple-use facilities for each unit cell according to an embodiment of the present invention.

According to an embodiment, the vulnerable area information includes information on the number of multiple-use facilities included for each unit cell or includes information on the degree of risk related to multiple-use facilities in the unit cell. Based on this, specific information for spatial density analysis is included. Ranking can be given between unit cells in an area.

In addition, an embodiment may visualize the most vulnerable TOP10 for unit cells in a specific area and provide it to the user terminal based on the ranking given to the unit cells through spatial density analysis, and visualize the most vulnerable TOP50 and provide it to the user terminal Also, as shown in FIG. 9 , the most vulnerable TOP100 may be visualized and provided to the user terminal. In this case, the ranking for each unit cell may also be displayed, and the unit cells belonging to TOP10, TOP50, and TOP100 may be displayed in different colors.

10 is a diagram illustrating floating population information for each unit cell according to an embodiment of the present invention.

Referring to FIG. 10 , according to an embodiment of the present invention, initial floating population information obtained from a telecommunication company may be processed to fit the size of a unit cell, and flow for a preset period based on the processed floating population information Population average information can be derived. For example, the floating population average information may include information about a specific month, and for this purpose, information about the floating population of the specific month for several years may be used.

11 is a diagram illustrating a data set for deriving a correlation between information on multiple-use facilities for each unit cell and floating population information for each unit cell according to an embodiment of the present invention, and FIG. 12 is a diagram showing a data set according to an embodiment of the present invention. It is a diagram for explaining the correlation between information on multi-use facilities for each unit cell and information on floating population for each unit cell.

In one embodiment, in order to use the multi-use facility information and the floating population information together in deriving a high-risk area, the correlation between the multi-use facility information, that is, the vulnerable area information and the floating population information, may be checked. Here, the correlation between the two can be confirmed through correlation analysis.

First, according to an embodiment, as shown in FIG. 11 , a data set including floating population average information and information on the number of multi-use facilities for each unit cell may be configured, and correlation analysis may be performed using the configured data set.

Correlation analysis may refer to an analysis that confirms the association between two factors, and a correlation coefficient may be derived as a result. For example, the correlation coefficient may be derived as a specific value within the range of -1 to +1, and it may be determined that the higher the value of the correlation coefficient is, the higher the correlation between the two factors.

According to an embodiment, correlation analysis may be performed using the data set of FIG. 11 , and a correlation coefficient between two factors may be derived as shown in FIG. 12 . That is, the correlation coefficient between the multi-use facility and the floating population can be derived as 0.54, and the strength of the correlation between the two factors can be evaluated as "medium".

13 is a block diagram for explaining a method of deriving high-risk area information for each unit cell according to an embodiment of the present invention, and FIG. 14 is a high-risk area for each unit cell including a high-risk area prediction index according to an embodiment of the present invention. It is a diagram showing a data set corresponding to information.

Referring to FIG. 13 , in an embodiment of the present invention, information on the number of multiple-use facilities for each unit cell (or information on the degree of risk associated with multiple-use facilities in a unit cell) and the number of floating populations per unit cell (or High-risk areas can be predicted using information on the current status of infectious diseases in addition to the floating population information for each unit cell). Here, the infectious disease outbreak status information may indicate information on the status of an infectious disease outbreak in a specific region, and in one embodiment, information on the number of infectious disease outbreaks per unit cell in a specific region is derived based on the infectious disease outbreak status information. Available.

For example, an embodiment may derive a ranking for each unit cell with respect to the above three pieces of information, and based on each ranking information for each unit cell of the derived three pieces of information, a high risk area based on a unit cell, that is, a high risk A unit cell corresponding to a region may be predicted or derived. For example, in one embodiment, first ranking information may be derived based on the multi-use facility information for each unit cell, and second ranking information may be derived based on the floating population information for each unit cell, and the The third ranking information may be derived based on the infection occurrence information of the infectious disease for each unit cell. In addition, an embodiment may derive high-risk area prediction index information for each unit cell based on the first ranking information, the second ranking information, and the third ranking information, and the high-risk area prediction index information for each unit cell Based on the high risk area information for each unit cell can be derived.

Referring to FIG. 14 , in one embodiment, identification information of a unit cell, information on multiple-use facilities for each unit cell (information counting multiple-use facilities/information on the number of multiple-use facilities), information on a vulnerable area for each unit cell, and unit cell Information on the number of floating populations for each unit cell, the number of infectious disease infections per unit cell, the first ranking information (r1), the second ranking information (r2), the third ranking information (r3), and the high-risk area prediction index information for each unit cell. can do.

Each ranking information represents information indicating a ranking between unit cells in a specific area, the first ranking information has a higher value as the number of multi-use facilities increases, and the second ranking information has a higher value as the number of floating populations increases, The third ranking information may have a higher value as the number of infectious disease infections, that is, the number of infectious disease outbreaks increases.

The high-risk area prediction index information for each unit cell may be derived based on the first ranking information, the second ranking information, and the third ranking information, and the higher the degree of risk, the higher the index value may be derived. For example, in one embodiment, before deriving the high-risk area prediction index information for each unit cell, a lower weight (eg, 1/100) is added to the third ranking information compared to the first ranking information and the second ranking information. It can be set, and through this, when deriving high-risk area prediction index information for each unit cell, the influence of the third ranking information, that is, the number of infectious diseases, can be relatively reduced. Or, for example, an embodiment may set a weight in consideration of a correlation analysis result between the first ranking information and the second ranking information, but is not limited thereto.

15 is a diagram visualizing TOP10, TOP50, and TOP100 of high-risk areas for each unit cell in a specific area based on the high-risk area prediction index for each unit cell according to an embodiment of the present invention.

Referring to FIG. 15 , according to an embodiment, a high-risk area for an infectious disease in a specific area may be derived based on the data set derived as shown in FIG. 14 and provided to the user terminal. That is, in one embodiment, it can be determined that a unit cell having a high risk area prediction index information for each unit cell is a high risk area, and the high risk area unit cell TOP10, TOP50, or TOP100 is extracted according to the order of the prediction index information high to the user terminal. may be provided to the user, or the location of the TOP10, TOP50, or TOP100 unit cell may be mapped to map information of a specific area and provided to the user terminal. In this case, the ranking (ranking according to the high-risk area prediction index) for each unit cell may also be displayed, and the unit cells belonging to TOP10, TOP50, and TOP100 may be displayed in different colors.

16 is a diagram illustrating medical facilities in a specific area for deriving an infectious disease response index according to an embodiment of the present invention.

In one embodiment, not only the operation of deriving and providing a high-risk area for each unit cell, but also an infectious disease response index for each unit cell may be derived and provided to the user terminal.

Referring to FIG. 16 , according to an embodiment, information on a plurality of medical facilities in a specific area may be obtained to derive an infectious disease response index, and the obtained plurality of medical facilities may be classified according to types. Here, the information on the plurality of medical facilities may include type information and location information of the medical facilities, and the classification may be performed based on the type information.

For example, the plurality of medical facilities may be classified into a medical facility capable of responding to an infectious disease (eg, a general hospital) and a medical facility that cannot cope with it (eg, a hospital other than a general hospital), but a general hospital, a general hospital , a nursing hospital, oriental medicine hospital, and others may be classified as, but not limited thereto. In addition, it is obvious that the classification of medical facilities capable of responding to infectious diseases and medical facilities not responding to infectious diseases may be changed depending on the type of infectious disease.

17 is a view showing a plurality of clusters for deriving an infectious disease response index according to an embodiment of the present invention, and FIG. 18 is a data set corresponding to the derived infectious disease response index information for each unit cell according to an embodiment of the present invention. is a diagram showing

According to an embodiment, information on an infectious disease response index for each unit cell may be derived using location information of a medical facility capable of responding to an infectious disease. For example, in one embodiment, the distance from the center point of a unit cell to a medical facility capable of responding to the infectious disease may be derived for each unit cell, and when there are a plurality of medical facilities capable of responding to the infectious disease, from the central point to each infectious disease By summing distances to available medical facilities, it is possible to derive medical facility distance information for each unit cell.

Alternatively, for example, in an embodiment, when there are a plurality of medical facilities capable of responding to the infectious disease, the distance from the central point to the nearest medical facility capable of responding to an infectious disease may be derived as the medical facility distance information for each unit cell. Or, for example, in an embodiment, when there are a plurality of medical facilities capable of responding to the infectious disease, the first distance information may be derived by summing the distances from the central point to the medical facilities capable of responding to each infectious disease, and from the central point The distance to a medical facility capable of responding to the nearest infectious disease may be derived as the second distance information, and may be derived as the medical facility distance information for each unit cell based on the first distance information and the second distance information. In this case, the first distance information and the second distance information may have different weights, and when priority is given to being able to quickly receive help from a medical facility in a corresponding unit cell, the weight for the first distance information may be set to a second weight. The weight for distance information can be relatively higher than the weight for distance information, and when priority is given to being able to receive help from multiple medical facilities in consideration of the saturation of medical facilities, the weight for the second distance information is applied to the weight of the first distance information rather than the weight for the first distance information. It can be relatively high.

Thereafter, as shown in FIG. 17 , clustering may be performed for each unit cell based on the high-risk area prediction index information for each unit cell and the medical facility distance information for each unit cell, and all unit cells are clustered into a plurality of clusters. It can be classified as belonging to one of the For example, an embodiment may use a K-means clustering technique for this, and classify all unit cells to belong to one of five clusters.

According to an embodiment, information on one cluster to which the corresponding unit cell belongs among a plurality of clusters may be obtained for all unit cells through the above-described process, and this may be included in the final data set for each unit cell.

Referring to FIG. 18 , the final data set for each unit cell according to an embodiment includes identification information of unit cells, high-risk area prediction index information for each unit cell, information on the sum of distances to medical facilities capable of responding to infectious diseases, and cluster information. The cluster information may also be referred to as infectious disease response index information because it indicates that the cluster information is included in a cluster displayed as a lower value as the sum of distances to medical facilities capable of responding to an infectious disease is smaller. Alternatively, a bar indicating one of a plurality of clusters may be referred to as infectious disease response level information.

19 is a block diagram of an apparatus for predicting a high-risk area for an infectious disease according to an embodiment of the present invention.

Referring to FIG. 19 , an apparatus 1900 for predicting a high-risk area for an infectious disease according to an embodiment of the present invention may include at least one processor 1910 , a memory 1920 , and a storage device 1930 . For example, the apparatus 1900 for predicting a high-risk area for an infectious disease may further include a communication module capable of performing various wired/wireless communication. For example, the apparatus 1900 for predicting a high-risk area for an infectious disease may include at least one server, and may transmit/receive information to/from at least one other server or at least one other terminal.

The processor 1910 may execute a program command stored in the memory 1920 and/or the storage device 1930 . The processor 1910 may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to the present invention are performed. The memory 1920 and the storage device 1930 may be configured as a volatile storage medium and/or a non-volatile storage medium. For example, the memory 1920 may include read only memory (ROM) and/or random access memory (RAM).

The memory 1920 may store at least one instruction executed through the processor 1910 . The at least one command is a command to divide the specific area into unit cells of a preset size, a command to obtain floating population information for the specific area, and a command to derive floating population information for each unit cell based on the floating population information , a command for obtaining infection occurrence information of an infectious disease for the specific region, a command for deriving infection occurrence information for each unit cell based on the infection occurrence information of the infectious disease, and floating population information for each unit cell, for the specific region It may include a command for deriving high-risk area information for each unit cell for the specific area based on information on multiple-use facilities for each unit cell and infection occurrence information for each unit cell.

For example, the multi-use facility information for each unit cell may include information counting the number of multi-use facilities for each unit cell, and the floating population information is an average per hour based on the floating population information for a preset time. It may include floating population information.

For example, a command for obtaining information about a building in the specific area, a command for deriving status information of the buildings for each unit cell based on address information of the buildings included in the information about the building, and a command related to the building The command for deriving multi-use facility information for each unit cell may be further included based on the usage information for each floor of the buildings included in the information.

For example, the command for deriving high-risk area information for each unit cell for the specific area is a command for deriving first ranking information based on the multiple-use facility information for each unit cell, based on the floating population information for each unit cell A command for deriving the second ranking information, a command for deriving third ranking information based on the infection occurrence information of the infectious disease for each unit cell, the first ranking information, the second ranking information, and the third ranking information based on the It may include a command for deriving high-risk area prediction index information for each unit cell and a command for deriving high-risk area information for each unit cell based on the high-risk area prediction index information for each unit cell.

For example, a command to obtain information on a plurality of medical facilities in the specific area, a command to derive at least one first medical facility capable of responding to the infectious disease based on the information on the plurality of medical facilities, A command for deriving the sum of distances from the location corresponding to the unit cell to the location of the at least one first medical facility to derive medical facility distance information for each unit cell, and high-risk area prediction index information for each unit cell and the unit It may further include a command for deriving infectious disease response index information for each unit cell based on the distance information of the medical facility for each cell.

For example, the communication module may represent a module capable of communicating with an external device. That is, the communication module may be connected to a network through wireless communication or wired communication to establish communication with an external device such as an external server, a user terminal, or other electronic device to transmit and/or receive predetermined information.

For example, the wireless communication is 5G (generation), LTE, LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband) , or may include cellular communication using at least one of Global System for Mobile Communications (GSM). In addition, the cellular communication may use one of next-generation mobile communication whose name has not yet been determined. Or the wireless communication is WiFi (wireless fidelity), Bluetooth, Bluetooth low energy (BLE), Zigbee (Zigbee), near field communication (NFC), magnetic secure transmission (Magnetic Secure Transmission), radio frequency (RF), or body area network It may include at least one of (BAN).

For example, the wired communication may include at least one of universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard232 (RS-232), power line communication, or plain old telephone service (POTS). have. The network 162 may include at least one of a telecommunication network, for example, a computer network (eg, LAN or WAN), the Internet, or a telephone network.

20 is a flowchart of a method for predicting a high-risk area for an infectious disease according to an embodiment of the present invention.

Referring to FIG. 20 , according to an embodiment, the specific region may be divided into unit cells having a preset size ( S2000 ). For example, the size of the cell may be set in a square shape of 500m x 500m based on the measured distance. However, the area or size of the cell may be determined to be a size capable of preventing excessive computational work and deriving effective result information. This has been described in detail with reference to FIGS. 3 and 4 .

Also, according to an embodiment, floating population information for the specific region may be obtained (S2010), and floating population information for each unit cell may be derived based on the floating population information (S2020). For example, the floating population information may include hourly average floating population information based on the floating population information for a preset time. This has been described in detail with reference to FIG. 10 .

Thereafter, according to an embodiment, infection occurrence information of an infectious disease for the specific region may be obtained (S2030), and infection occurrence information for each unit cell may be derived based on the infection occurrence information of the infectious disease (S2040). In addition, one embodiment is based on the floating population information for each unit cell, multi-use facility information for each unit cell for the specific region, and infection occurrence information for each unit cell, high-risk area information for each unit cell for the specific region. can be derived (S2050). For example, the multi-use facility information per unit cell may include information counting the number of multi-use facilities per unit cell. This has been described in detail with reference to FIGS. 13 to 15 .

In addition, although not shown in FIG. 20 , in one embodiment, information about a building in the specific area can be obtained in order to derive information on multiple-use facilities for each unit cell, and information about the buildings included in the information about the building can be obtained. Based on the address information, it is possible to derive the status information of the buildings for each unit cell, and based on the usage information for each floor of the buildings included in the information about the building, information on the multi-use facilities for each unit cell can be generated.

For example, in one embodiment, in order to derive high-risk area information for each unit cell for a specific area, the first ranking information may be derived based on the multi-use facility information for each unit cell, and the floating population information for each unit cell Second ranking information may be derived based on , and third ranking information may be derived based on infection occurrence information of infectious diseases for each unit cell. In addition, an embodiment may derive high-risk area prediction index information for each unit cell based on the first ranking information, the second ranking information, and the third ranking information, and the high-risk area prediction index information for each unit cell Based on the high-risk area information for each unit cell can be derived.

Furthermore, according to an embodiment, information on a plurality of medical facilities in the specific area may be obtained, and at least one first medical facility capable of responding to the infectious disease is derived based on the information on the plurality of medical facilities. and, by deriving the sum of distances from the location corresponding to the unit cell to the location of the at least one first medical facility, medical facility distance information for each unit cell may be derived. In addition, an embodiment may derive infectious disease response index information for each unit cell based on the high-risk area prediction index information for each unit cell and the medical facility distance information for each unit cell.

The operation according to the embodiment of the present invention can be implemented as a computer-readable program or code on a computer-readable recording medium. The computer-readable recording medium includes all types of recording devices in which data readable by a computer system is stored. In addition, the computer-readable recording medium is distributed in a computer system connected to a network so that computer-readable programs or codes can be stored and executed in a distributed manner.

In addition, the computer-readable recording medium may include a hardware device specially configured to store and execute program instructions, such as ROM, RAM, and flash memory. The program instructions may 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.

Although some aspects of the invention have been described in the context of an apparatus, it may also represent a description according to a corresponding method, wherein a block or apparatus corresponds to a method step or feature of a method step. Similarly, aspects described in the context of a method may also represent a corresponding block or item or a corresponding device feature. Some or all of the method steps may be performed by (or using) a hardware device such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most important method steps may be performed by such an apparatus.

In embodiments, a programmable logic device (eg, a field programmable gate array) may be used to perform some or all of the functions of the methods described herein. In embodiments, the field programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described herein. In general, the methods are preferably performed by some hardware device.

Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art can variously modify and change the present invention within the scope without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that it can be done.

Claims (5)

A method of predicting a high-risk area for an infectious disease among a specific area performed by an electronic device, the method comprising:
dividing the specific region into unit cells of a preset size;
obtaining floating population information for the specific area;
deriving floating population information for each unit cell based on the floating population information;
Acquiring infection occurrence information of an infectious disease for the specific region:
deriving infection occurrence information for each unit cell based on the infection occurrence information of the infectious disease; and
Deriving high-risk area information for each unit cell for the specific region based on the floating population information for each unit cell, information on multiple-use facilities for each unit cell for the specific region, and infection occurrence information for each unit cell for each unit cell and,
obtaining information about a building in the specific area;
deriving status information of the buildings for each unit cell based on the address information of the buildings included in the information on the buildings; and
Further comprising the step of deriving multi-use facility information for each unit cell based on the use information for each floor of the buildings included in the information about the building,
The step of deriving high-risk area information for each unit cell for the specific area includes:
deriving first ranking information based on the multi-use facility information for each unit cell;
deriving second ranking information based on the floating population information for each unit cell;
deriving third ranking information based on the infection occurrence information of the infectious disease for each unit cell;
deriving high-risk area prediction index information for each unit cell based on the first ranking information, the second ranking information, and the third ranking information; and
Including the step of deriving the high-risk area information for each unit cell based on the high-risk area prediction index information for each unit cell,
obtaining information about a plurality of medical facilities in the specific area;
deriving at least one first medical facility capable of responding to the infectious disease based on the information on the plurality of medical facilities;
deriving a sum of distances from a location corresponding to the unit cell to a location of the at least one first medical facility to derive medical facility distance information for each unit cell; and
Further comprising the step of deriving infectious disease response index information for each unit cell based on the high-risk area prediction index information for each unit cell and the medical facility distance information for each unit cell,
A method of predicting high-risk areas for infectious diseases. The method according to claim 1,
The multi-use facility information per unit cell includes information counting the number of multi-use facilities per unit cell,
The floating population information includes an average floating population information per hour based on the floating population information for a preset time,
A method of predicting high risk areas for infectious diseases delete delete delete

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