KR20230021037A - Device, system, method and program for disease spread prediction service using big data - Google Patents

Abstract

According to an embodiment of the present invention, a device for providing a disease spread prediction service using big data is disclosed. The device includes: at least one processor; and a memory storing instructions that instruct the at least one processor to perform at least one operation. In addition, the at least one operation includes the operations of: generating a first value for each infectious disease using symptoms received from a plurality of pharmacist terminals matching a first area; generating a second value for each infectious disease using symptoms received from a plurality of doctor terminals matching the first area; providing confirmation-requiring symptoms that match a confirmation-requiring infectious disease whose first value is greater than a preset first reference value or a second value is greater than a preset second reference value to the plurality of doctor terminals matching the first area; and determining a first risk of developing the confirmation-requiring infectious disease using the confirmation-requiring symptoms received from the plurality of doctor terminals matching the first area. According to an embodiment of the present invention, necessary educational information can be provided to doctors and pharmacists who use the disease spread prediction service.

Description

Device, system, method and program for providing disease spread prediction service using big data {DEVICE, SYSTEM, METHOD AND PROGRAM FOR DISEASE SPREAD PREDICTION SERVICE USING BIG DATA}

The present invention relates to an apparatus, system, method, and program for providing a disease spread prediction service using big data.

Unless otherwise indicated herein, material described in this section is not prior art to the claims in this application, and inclusion in this section is not an admission that it is prior art.

Public emergencies, such as the spread of infectious diseases, biohazards, meteorological disasters, etc., have a great impact on people's production, livelihoods and even safety. If the risk of occurrence and spread of an infectious disease is predicted quickly and accurately, the spread of the risk of an accident can be effectively prevented and appropriate preventive measures can be taken. Accordingly, the need for a technology capable of estimating the risk of occurrence and spread of an infectious disease is emerging.

An object of the present invention is to provide an apparatus, system, method, and program for providing a disease spread prediction service using big data, which can provide educational information to doctors and pharmacists using the disease spread prediction service.

An object of the present invention is to provide an apparatus, system, method, and program for providing a disease spread prediction service using big data, which can determine the risk of occurrence and spread of an infectious disease using information received from doctors and pharmacists.

One aspect of the present invention for achieving the above object provides an apparatus for providing a disease spread prediction service using big data.

The device may include at least one processor; and a memory for storing instructions instructing the at least one processor to perform at least one operation.

The at least one operation may include generating a first value for each infectious disease by using symptoms received from a plurality of pharmacist terminals matching the first area; generating a second value for each infectious disease by using symptoms received from a plurality of pseudo terminals matched with the first region; An operation of providing, to a plurality of doctor terminals matched with the first region, symptoms requiring confirmation that are matched with an infectious disease requiring confirmation, which is an infectious disease having a first value greater than a preset first reference value or a second value greater than a preset second reference value. ; and determining a first risk level of the infectious disease requiring confirmation by using symptoms requiring confirmation received from a plurality of doctor terminals matched with the first area.

In addition, the operation of generating a first value for each infectious disease by using the symptoms received from the plurality of pharmacist terminals matching the first area occurs for each symptom using the symptoms received from the plurality of pharmacist terminals matching the first area. generating counts and generating a first input vector having each symptom count as a dimensional value; and inputting the first input vector as an input value to the pre-learned first artificial neural network, and receiving a first value for each infectious disease from the first artificial neural network.

In addition, the operation of generating the second value for each infectious disease by using the symptoms received from the plurality of doctor terminals matching the first area is performed by using the symptoms received from the plurality of doctor terminals matching the first area. generating the number of occurrences and generating a second input vector having each symptom count as a dimensional value; and inputting the second input vector as an input value to the pre-learned second artificial neural network, and receiving a second value for each infectious disease from the second artificial neural network.

In addition, the operation of determining the first risk level of the infectious disease requiring confirmation by using the symptoms requiring confirmation received from the plurality of doctor terminals matching the first area is received from the plurality of doctor terminals matching the first area. generating the number of occurrences for each symptom requiring confirmation using the identified symptom to be confirmed, and generating a third input vector having each of the number of occurrences for each symptom to be confirmed as a dimension value; and inputting the third input vector as an input value to the pre-learned third artificial neural network, and receiving the first outbreak risk of the infectious disease requiring confirmation from the third artificial neural network.

The at least one operation may include selecting a plurality of second regions adjacent to the first region where the first onset risk is greater than a preset first reference risk; providing a symptom requiring verification to a plurality of pseudo terminals matching each of the plurality of second regions; determining a second risk of developing an infectious disease requiring confirmation in the second area by using symptoms requiring confirmation received from a plurality of doctor terminals matched with the second area; determining a risk of proliferation in the second region based on the number of populations in the second region, the number of floating populations between the second region and the first region, a first risk of outbreak, and a second risk of outbreak; and determining a correspondence order corresponding to each of the plurality of second regions in order of highest diffusion risk.

In addition, the operation of determining the second risk of outbreak of an infectious disease requiring confirmation in the second area by using symptoms requiring confirmation received from a plurality of doctor terminals matching the second area, generating a frequency of occurrence for each symptom requiring confirmation using symptoms to be confirmed received from a plurality of doctor terminals, and generating a fourth input vector having each of the number of occurrences for each symptom to be confirmed as a dimension value; and inputting the fourth input vector as an input value to the pre-learned third artificial neural network, and receiving the second risk level of the infectious disease requiring confirmation from the third artificial neural network.

In addition, according to an embodiment of the present invention, an operating method for providing a 3D modeling service of a seabed topography using big data is provided.

The operation method may include generating a first value for each infectious disease using symptoms received from a plurality of pharmacist terminals matched with a first area; generating a second value for each infectious disease by using symptoms received from a plurality of pseudo terminals matched with the first area; An operation of providing, to a plurality of doctor terminals matched with the first region, symptoms requiring confirmation that are matched with an infectious disease requiring confirmation, which is an infectious disease having a first value greater than a preset first reference value or a second value greater than a preset second reference value. ; and determining a first risk level of the infectious disease requiring confirmation by using symptoms requiring confirmation received from a plurality of doctor terminals matched with the first area.

In addition, according to an embodiment of the present invention, a non-transitory recording medium on which a program for executing the operating method is recorded and which can be read by a computer is provided.

In addition, according to an embodiment of the present invention, a computer program recorded on a non-transitory recording medium is provided in order to execute the operation method in a 3D modeling apparatus for a seafloor topography using big data.

In addition, according to an embodiment of the present invention, a system for providing a 3D modeling service for seafloor topography using big data is provided.

The system includes: a plurality of doctor terminals, which provide symptoms and symptoms to be confirmed to the device; a plurality of pharmacist terminals that provide symptoms and symptoms requiring confirmation to the device; and a device for determining an onset risk using the received symptoms and symptoms requiring confirmation.

In addition, the device, at least one processor (processor); and a memory for storing instructions instructing the at least one processor to perform at least one operation.

The at least one operation may include generating a first value for each infectious disease by using symptoms received from a plurality of pharmacist terminals matching the first area; generating a second value for each infectious disease by using symptoms received from a plurality of pseudo terminals matched with the first region; An operation of providing, to a plurality of doctor terminals matched with the first region, symptoms requiring confirmation that are matched with an infectious disease requiring confirmation, which is an infectious disease having a first value greater than a preset first reference value or a second value greater than a preset second reference value. ; and determining an onset risk of the contagious disease requiring confirmation by using symptoms requiring confirmation received from a plurality of doctor terminals matching the first area.

According to an embodiment of the present invention, necessary educational information may be provided to doctors and pharmacists using the disease spread prediction service.

According to an embodiment of the present invention, since the risk of occurrence and spread of an infectious disease can be provided, a response scenario can be established with reference to the provided risk of occurrence and spread.

1 is a schematic diagram of a system for providing a disease spread prediction service according to an embodiment of the present invention.
FIG. 2 is a block diagram showing functional modules of the service providing apparatus according to FIG. 1 by way of example.
3 is a diagram conceptually illustrating a process in which the service providing device according to FIG. 1 provides educational information.
FIG. 4 is a flowchart illustrating a process in which the service providing apparatus according to FIG. 1 determines the risk of an infectious disease occurring in a specific area.
FIG. 5 is a diagram conceptually illustrating that the service providing apparatus according to FIG. 1 specifies an area around an area where an infectious disease has occurred.
FIG. 6 is a flowchart illustrating a process in which the service providing apparatus according to FIG. 1 determines the order of correspondence of areas surrounding an area where an epidemic has occurred.
7 is a diagram showing the hardware configuration of the service providing apparatus according to FIG. 1 by way of example.
8 is a diagram illustrating a wireless communication system that can be applied in a communication process according to an embodiment of the present invention.
9 is a diagram illustrating a base station in the wireless communication system according to FIG. 8 .
10 is a diagram illustrating a terminal in the wireless communication system according to FIG. 8 .
11 is a diagram illustrating a communication interface in the wireless communication system according to FIG. 8 .

Since the present invention can make various changes and have various embodiments, specific embodiments will be 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 should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. Like reference numerals have been used for like elements throughout the description of each figure.

Terms such as first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention. The terms and/or include any combination of a plurality of related recited items or any of a plurality of related recited items.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present 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 unless explicitly defined in the present application, they should not be interpreted in an ideal or excessively formal meaning. don't

Hereinafter, a preferred embodiment according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a schematic diagram of a system for providing a disease spread prediction service according to an embodiment of the present invention.

Referring to FIG. 1 , a system for providing a disease spread prediction service includes a service providing device 100 , a doctor terminal 200 and a pharmacist terminal 300 .

The doctor terminal 200 is a terminal of a doctor using the disease spread prediction service, and may provide authentication information to the service providing device 100 and receive functions of the disease spread prediction service.

The pharmacist terminal 300 is a terminal of a pharmacist using the disease spread prediction service, and may provide authentication information to the service providing device 100 and receive functions of the disease spread prediction service.

Examples of the doctor terminal 200 and the pharmacist terminal 300 include a communicable desktop computer, a laptop computer, a notebook, a smart phone, and a tablet PC. , mobile phone, smart watch, smart glass, e-book reader, PMP (portable multimedia player), portable game device, navigation device, digital camera, Digital multimedia broadcasting (DMB) player, digital audio recorder, digital audio player, digital video recorder, digital video player, personal digital assistant (PDA) etc.

FIG. 2 is a block diagram showing functional modules of the service providing apparatus 100 according to FIG. 1 by way of example.

Referring to FIG. 2 , the service providing device 100 includes a subscription unit 101 , an educational information providing unit 102 , an outbreak risk determination unit 103 and a proliferation risk determination unit 104 .

The subscription unit 101 compares the authentication information received from the doctor terminal 200 and the pharmacist terminal 300 with the information stored in the database, and matches the doctor terminal 200 and the pharmacist terminal 300 based on the comparison result. generate identification information.

When receiving a request for providing educational information from the doctor terminal 200 and the pharmacist terminal 300, the educational information providing unit 102 searches the database for educational information matching the received educational information providing request, and retrieves the educational information. may be provided to the doctor terminal 200 and the pharmacist terminal 300.

The outbreak risk determination unit 103 may determine the risk of developing an infectious disease using information received from the plurality of doctor terminals 200 and the plurality of pharmacist terminals 300 .

The spread risk determination unit 104 may determine the spread risk of an infectious disease using information received from the plurality of doctor terminals 200 and the plurality of pharmacist terminals 300 .

3 is a diagram conceptually illustrating a process in which the service providing apparatus 100 according to FIG. 1 provides educational information.

Referring to FIG. 3 , the subscription unit 101 receives authentication information from the doctor terminal 200 (S110) and receives authentication information from the pharmacist terminal 300 (S120). In one embodiment, the authentication information may include a license, work area, and the like. For example, the authentication information may include a doctor's license number, a pharmacist's license number, and the like.

The subscription unit 101 compares the information stored in the database with the information matching the received authentication information, and generates identification information if they match (S130). In one embodiment, the subscription unit 101 generates identification information matched with the doctor terminal 200 that provided the doctor's license number when information matching the received doctor's license number exists in the database. In one embodiment, the subscription unit 101 generates identification information matched with the pharmacist terminal 300 that provided the pharmacist license number when there is information identical to the received pharmacist's license number in the database.

When receiving a request for providing education information from the doctor terminal 200, the education information provider 102 may provide the education information to the doctor terminal 200 (S140). When receiving a request for providing educational information from the pharmacist terminal 300, the educational information providing unit 102 may provide educational information to the pharmacist terminal 300 (S150).

In one embodiment, educational information about patient education may be stored in the database. When a request for providing education information on patient education is received from the doctor terminal 200 and the pharmacist terminal 300, the education information providing unit 102 provides education on patient education to the doctor terminal 200 and the pharmacist terminal 300. can provide information.

In one embodiment, educational information about drugs may be stored in the database. When a request for providing information on drugs is received from the doctor terminal 200 and the pharmacist terminal 300, the education information providing unit 102 provides education information on drugs to the doctor terminal 200 and the pharmacist terminal 300. can do.

In one embodiment, education information on sales education may be stored in the database. When a request for providing education information on sales education is received, the education information providing unit 102 may provide education information on sales education to the doctor terminal 200 and the pharmacist terminal 300 .

FIG. 4 is a flowchart illustrating a process in which the service providing apparatus 100 according to FIG. 1 determines the risk of an infectious disease occurring in a specific area.

In the database, the entire region for which the risk of outbreak of an infectious disease is to be detected is divided into a plurality of regions and stored. In one embodiment, the entire area may be divided into a plurality of areas in a grid pattern. However, it is not limited thereto.

In the database, the doctor terminal 200 and the area are matched and stored, and the pharmacist terminal 300 and the area are matched and stored. For example, the identification numbers of the plurality of doctor terminals 200 belonging to a specific area A at work are matched with the area A and stored in the database. For example, the identification numbers of the plurality of pharmacist terminals 300 belonging to a specific area A at work are matched with the area A and stored in the database.

In the database, each of the plurality of regions and the number of population residing in each of the plurality of regions are matched and stored. For example, if the population of area A is 1 million, only area A and 1 million are matched and stored in the database.

In the database, the average floating population number between any one of the plurality of areas and another adjacent area is matched and stored. For example, when the average number of floating populations between areas A and B adjacent to each other is 500,000, 500,000 are matched with areas A and B and stored in the database.

The onset risk level determination unit 103 generates a first value for each type of infection using symptoms and medicines received from the plurality of pharmacist terminals 300 that match the first area (S210).

The onset risk level determining unit 103 receives symptoms and medicines from a plurality of pharmacist terminals 300 that match a first area, which is one of a plurality of areas. In one embodiment, the onset risk determination unit 103 provides a user interface through which symptoms and medicines can be input to the pharmacist terminal 300 . For example, when a patient visits a pharmacist due to a headache and the pharmacist prescribes drug A, the pharmacist may input the headache and drug A into the pharmacist terminal 300 . The pharmacist terminal 300 provides the input headache and medicine A to the service providing device 100 .

The onset risk determination unit 103 searches the database for a symptom matching the received drug, and when the searched symptom matches the symptom received from the pharmacist terminal 300, the symptom received from the pharmacist terminal 300 It is used to generate the first value for each epidemic. When the searched symptom and the symptom received from the pharmacist terminal 300 do not match, the onset risk determination unit 103 deletes the symptom received from the pharmacist terminal 300 .

The onset risk determination unit 103 generates the number of occurrences for each symptom using the symptoms received from the plurality of pharmacist terminals 300 matched with the first area during a preset period of time, and uses the number of occurrences for each symptom to generate a first input vector. generate For example, if the preset symptoms are A symptom, B symptom, C symptom, and D symptom, and 100 symptoms A, 200 symptoms B, 250 symptoms C, and 300 symptoms D are received during a preset period of time, In this case, the onset risk determination unit 103 may generate (100, 200, 250, 300) as the first input vector.

The outbreak risk determination unit 103 may input the first input vector as an input value to the pre-learned first artificial neural network and receive a preset first value for each infectious disease from the first artificial neural network. In one embodiment, the first value means an expected risk of developing an infectious disease. In one embodiment, the first artificial neural network may be generated through machine learning using learning data generated by labeling the first input vector for learning with expectations for each epidemic, and random forest, multiple regression analysis, Xgboost, etc. are used for learning. can be used For example, if the expected value range is a value between 0.000 and 1.000, and the preset epidemics are epidemic A, epidemic B, and epidemic C, the first input vector for training is a value between 0.000 and 1.000 that matches epidemic A, B A value between 0.000 and 1.000 matching the epidemic and a value between 0.000 and 1.000 matching the C epidemic may be labeled to generate learning data.

The outbreak risk determination unit 103 generates a second value for each infectious disease by using symptoms and diagnosis names received from the plurality of doctor terminals 200 matched with the first area (S220).

The onset risk level determining unit 103 receives symptoms and diagnosis names from a plurality of doctor terminals 200 that match a first area, which is one of a plurality of areas. In one embodiment, the onset risk determination unit 103 provides a user interface for inputting symptoms and diagnoses to the doctor terminal 200 . For example, when a doctor diagnoses a cold with headache and sore throat as symptoms, the doctor may input headache, sore throat and cold into the doctor terminal 200 . The doctor terminal 200 provides the input headache, sore throat, and cold to the service providing device 100 .

The onset risk determination unit 103 searches for a symptom matching the diagnosis received from the database, and when the searched symptom matches the symptom received from the doctor terminal 200, the symptom received from the doctor terminal 200 is selected. It is used to generate a second value for each epidemic. When the searched symptom and the symptom received from the doctor terminal 200 do not match, the onset risk determination unit 103 deletes the symptom received from the doctor terminal 200 .

The onset risk level determination unit 103 generates the number of occurrences for each symptom using the symptoms received from the plurality of doctor terminals 200 that match the first area during a preset period of time, and uses the number of occurrences for each symptom as a second input vector. generate For example, if the preset symptoms are A symptom, B symptom, C symptom, and D symptom, and 100 symptoms A, 200 symptoms B, 250 symptoms C, and 300 symptoms D are received during a preset period of time, In this case, the onset risk determination unit 103 may generate (100, 200, 250, 300) as the second input vector.

The outbreak risk determination unit 103 may input the second input vector as an input value to the pre-learned second artificial neural network and receive a preset second value for each infectious disease from the second artificial neural network. In one embodiment, the second value means an expected risk of developing an infectious disease. In one embodiment, the second artificial neural network may be generated through machine learning using learning data generated by labeling expectations for each epidemic in a second input vector for learning, and random forest, multiple regression analysis, Xgboost, etc. are used for learning. can be used For example, if the expected value range is a value between 0.000 and 1.000, and the preset epidemics are epidemic A, epidemic B, and epidemic C, the second input vector for learning is a value between 0.000 and 1.000 that matches epidemic A, B A value between 0.000 and 1.000 matching the epidemic and a value between 0.000 and 1.000 matching the C epidemic may be labeled to generate learning data.

The onset risk determination unit 103 determines symptoms requiring confirmation matching an infectious disease requiring confirmation, which is an infectious disease having a first value greater than a preset first reference value or a second value greater than a preset second reference value, which is matched with a first region. It is provided to a plurality of pseudo terminals 200 (S230).

The outbreak risk determination unit 103 determines an infectious disease having a first value greater than a preset first reference value as an infectious disease requiring confirmation, and searches a database for symptoms requiring confirmation that match the infectious disease requiring confirmation. In the database, infectious diseases, symptoms, and diagnoses are matched and stored. For example, COVID-19 may be matched with cough, sore throat, fever, and headache and stored in a database.

The onset risk level determining unit 103 provides the retrieved symptoms requiring confirmation to the plurality of doctor terminals 200 that match the first area. For example, if COVID-19 is an infectious disease requiring confirmation, the outbreak risk determination unit 103 determines cough, sore throat, fever, and headache that are matched with COVID-19 and stored in a plurality of doctor terminals (200) that are matched with the first area. ) is provided.

Through this, doctors working in the first area can more closely diagnose whether or not a symptom matching an infectious disease requiring confirmation is occurring when treating a patient.

The onset risk determination unit 103 determines the first onset risk of an infectious disease requiring confirmation by using the symptom requiring confirmation and the diagnosis requiring confirmation received from the plurality of doctor terminals 200 matched with the first area (S240).

The onset risk determination unit 103 generates the number of occurrences for each symptom requiring confirmation using the symptoms requiring confirmation received from the plurality of doctor terminals 200 that match the first area during a preset period of time, and determines the number of occurrences for each symptom requiring confirmation. Create a third input vector using . For example, if the symptoms to be confirmed are symptoms A, symptoms B, symptoms C, and symptoms D, and 100 symptoms A, 200 symptoms B, 250 symptoms C, and 300 symptoms D were received during a preset period of time, In this case, the onset risk determination unit 103 may generate (100, 200, 250, 300) as a third input vector.

The outbreak risk determination unit 103 may input the third input vector as an input value to the pre-learned third artificial neural network, and receive a first outbreak risk matched with an infectious disease requiring confirmation from the third artificial neural network. In one embodiment, the first outbreak risk means an expected risk of an infectious disease requiring confirmation. In one embodiment, the third artificial neural network may be generated through machine learning using learning data generated by labeling an expectation matching an infectious disease requiring confirmation in a third input vector for learning, and may be generated through random forest, multiple regression analysis, and Xgboost. etc. can be used for learning. For example, the expected value range is a value between 0.000 and 1.000, and a value between 0.000 and 1.000 matching an infectious disease requiring confirmation is labeled as a third input vector for learning, and learning data may be generated.

The outbreak risk determining unit 103 determines a quarantine scenario matching the first outbreak risk when the first outbreak risk is greater than the first preset reference risk (S250).

In the database, epidemics and quarantine scenario tables are matched and stored. In the quarantine scenario table, each of a plurality of quarantine scenarios and risk ranges are matched and stored.

The outbreak risk determination unit 103 searches the database for a quarantine scenario table matching an infectious disease requiring confirmation, and selects a quarantine scenario matching a risk range to which the first outbreak risk belongs. In one embodiment, the quarantine scenario may include information on response to an infectious disease requiring confirmation.

FIG. 5 is a diagram conceptually illustrating that the service providing apparatus 100 according to FIG. 1 specifies an area around an area where an infectious disease has occurred.

The spread risk determination unit 104 determines a plurality of second regions B adjacent to the first region A when the first risk of outbreak of an infectious disease requiring confirmation in the first region A is greater than the first reference risk. choose

FIG. 6 is a flowchart illustrating a process in which the service providing apparatus 100 according to FIG. 1 determines the order of correspondence of areas surrounding an area where an epidemic has occurred.

The diffusion risk determination unit 104 selects a plurality of second regions adjacent to the first region in which the first onset risk is greater than the preset first reference risk (S310).

The diffusion risk determination unit 104 provides symptoms requiring confirmation to the plurality of pseudo terminals 200 matching the plurality of second regions (S320).

The proliferation risk determination unit 104 provides the plurality of doctor terminals 200 matching the second area with symptoms requiring confirmation that are matched with infectious diseases requiring confirmation. For example, if COVID-19 is an infectious disease requiring confirmation, the spread risk determination unit 104 determines cough, sore throat, fever, and headache that are matched with COVID-19 and stored in a plurality of doctor terminals (200) that are matched with the second area. ) is provided.

The spread risk determining unit 104 determines a second risk of developing an infectious disease requiring confirmation using the symptoms requiring confirmation received from the plurality of doctor terminals 200 matched with the second area (S330).

The diffusion risk determination unit 104 generates the number of occurrences for each symptom requiring confirmation using the symptoms requiring confirmation received from the plurality of doctor terminals 200 that match the second area during a preset period of time, and generates the number of occurrences for each symptom requiring confirmation. A fourth input vector is generated using For example, if the symptoms to be confirmed are symptoms A, symptoms B, symptoms C, and symptoms D, and 100 symptoms A, 200 symptoms B, 250 symptoms C, and 300 symptoms D were received during a preset period of time, In this case, the onset risk determination unit 103 may generate (100, 200, 250, 300) as a fourth input vector.

The outbreak risk determination unit 103 may input the fourth input vector as an input value to the pre-trained third artificial neural network, and receive a second outbreak risk matched with an infectious disease requiring confirmation from the third artificial neural network.

The diffusion risk determination unit 104 determines the diffusion risk of each of the plurality of second regions based on the number of population in the second region, the number of floating populations between the second region and the first region, the first risk of outbreak, and the second risk of outbreak. Do (S340).

In one embodiment, the diffusion risk determination unit 104 may determine the diffusion risk as a relatively large value as the number of population in the second region increases. The diffusion risk determination unit 104 may determine the diffusion risk as a relatively small value as the number of population in the second region decreases.

In one embodiment, the diffusion risk determining unit 104 may determine the diffusion risk as a relatively large value as the number of floating populations between the second region and the first region increases. The diffusion risk determination unit 104 may determine the diffusion risk as a relatively small value as the number of floating populations between the second region and the first region decreases.

In one embodiment, the diffusion risk determination unit 104 may determine a relatively large value for the diffusion risk as the first risk of onset increases. The diffusion risk determining unit 104 may determine the diffusion risk as a relatively small value as the first risk of onset decreases.

In one embodiment, the diffusion risk determination unit 104 may determine the diffusion risk as a relatively large value as the second outbreak risk increases. The diffusion risk determination unit 104 may determine the diffusion risk as a relatively small value as the second onset risk decreases.

In one embodiment, the diffusion risk determination unit 104 may determine the diffusion risk based on Equation 1 below.

In Equation 1, V means the diffusion risk, P1 means the population of the first region, P2 means the population of the second region, Pm means the average of the population of a plurality of regions, P _{1 -2} means the floating population between the first area and the second area.

The diffusion risk determination unit 104 determines a correspondence order corresponding to each of a plurality of second regions based on the diffusion risk (S350).

The diffusion risk determination unit 104 determines a correspondence order corresponding to each of the plurality of second regions in order of diffusion risk. For example, the diffusion risk determination unit 104 may determine the corresponding order of the second region having the highest diffusion risk as 1.

Since the correspondence order is provided, it is possible to determine a response scenario by referring to the correspondence order.

FIG. 7 is a diagram showing the hardware configuration of the service providing apparatus 100 according to FIG. 1 by way of example.

Referring to FIG. 7 , the service providing apparatus 100 stores at least one processor 110 and instructions instructing the at least one processor 110 to perform at least one operation. It may include a memory (memory) to.

The at least one operation may include the components 101 to 104 of the above-described service providing apparatus 100 or other functions or operation methods.

Here, the at least one processor 110 may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor for performing methods according to embodiments of the present invention. there is. Each of the memory 120 and the storage device 160 may include at least one of a volatile storage medium and a non-volatile storage medium.

For example, the memory 120 may be one of a read only memory (ROM) and a random access memory (RAM), and the storage device 160 may be a flash-memory. , a hard disk drive (HDD), a solid state drive (SSD), or various memory cards (eg, a micro SD card).

In addition, the device 100 may include a transceiver 130 that performs communication through a wireless network. In addition, the device 100 may further include an input interface device 140 , an output interface device 150 , a storage device 160 , and the like. Each component included in the device 100 may be connected by a bus 170 to communicate with each other.

For example, the device 100 may include a communicable desktop computer, a laptop computer, a notebook, a smart phone, a tablet PC, and a mobile phone. , smart watch, smart glass, e-book reader, PMP (portable multimedia player), portable game device, navigation device, digital camera, DMB (digital multimedia broadcasting) player , a digital audio recorder, a digital audio player, a digital video recorder, a digital video player, a personal digital assistant (PDA), and the like.

8 is a diagram illustrating a wireless communication system that can be applied in a communication process according to an embodiment of the present invention. 9 is a diagram illustrating a base station in the wireless communication system according to FIG. 8 . 10 is a diagram illustrating a terminal in the wireless communication system according to FIG. 8 . 11 is a diagram illustrating a communication interface in the wireless communication system according to FIG. 8 .

Hereinafter, an example of a wireless communication network system supporting communication between the service providing device 100, the terminals 200 and 300, and the base station will be described in detail as an example, and the service providing device 100 and the terminals 200 and 300 ) may be interchangeably referred to as a node or a terminal for convenience of description. In the following description, a first node (device) may be an anchor/donor node or a centralized unit (CU) of an anchor/donor node, and a second node (device) may be an anchor/donor node or a distributed unit (DU) of a relay node can be

A base station (BS), terminal, server, etc. may be included as a part of a node using a radio channel in a wireless communication system.

A base station is a network infrastructure that provides wireless access to terminals. A base station has a coverage defined as a predetermined geographic area according to a distance over which a signal can be transmitted.

A base station, like a "base station," is referred to as "access point (AP)", "enodeb (eNB)", "5th generation (5G) node", "wireless point", " It may be referred to as a transmission/reception point (TRP).

A base station and a terminal may transmit and receive wireless signals in a millimeter wave (mmWave) band (eg, 28 GHz, 30 GHz, 38 GHz, and 60 GHz). At this time, the base station and the terminal may perform beamforming to improve channel gain. Beamforming may include transmit beamforming and receive beamforming. That is, the base station and the terminal can give directivity to the transmitted signal and the received signal. To this end, the base station and the terminal may select a serving beam through a beam search procedure or a beam management procedure. After that, communication may be performed using a resource carrying a serving beam and a resource having a quasi co-located relationship.

A first antenna port and a second antenna port are considered quasi-colocated if the large-scale properties of the channel through which the symbol of the first antenna port carries can be inferred from the channel through which the symbol of the second antenna port carries. The large-scale properties may include one or more of delay spread, Doppler spread, Doppler shift, average gain, average delay, and spatial Rx parameters.

Hereinafter, a base station in the wireless communication system described above is exemplified. The terms "-module", "-unit" or "-er" used below may mean a unit that processes at least one function or operation, and may include hardware, software, or both hardware and software. It can be implemented as a combination of

The base station may include a wireless communication interface, a backhaul communication interface, a storage unit and a controller.

The wireless communication interface performs a function of transmitting and receiving signals through a wireless channel. For example, the wireless communication interface may perform a conversion function between a baseband signal and a bit stream according to a physical layer standard of a system. For example, in data transmission, a radio communication interface encodes and modulates a transmitted bit stream to generate composite symbols. Also, upon receiving data, the wireless communication interface demodulates and decodes the baseband signal to reconstruct the received bit stream.

The wireless communication interface performs a function of transmitting and receiving signals through a wireless channel. For example, the wireless communication interface may perform a conversion function between a baseband signal and a bit stream according to a system physical layer standard. For example, in data transmission, a radio communication interface encodes and modulates a transmitted bit stream to generate composite symbols. Also, upon receiving data, the wireless communication interface demodulates and decodes the baseband signal to reconstruct the received bit stream.

In addition, the wireless communication interface up-converts a baseband signal into a radio frequency (RF) band signal, transmits the converted signal through an antenna, and down-converts the RF band signal received through the antenna into a baseband signal. To this end, the wireless communication interface includes a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, a digital-to-analog converter (DAC), An analog-to-digital converter (ADC) and the like may be included. Also, the wireless communication interface may include a plurality of transmit/receive paths. Additionally, the wireless communication interface may include at least one antenna array comprising a plurality of antenna elements.

In terms of hardware, the wireless communication interface may include a digital unit and an analog unit, and the analog unit may include a plurality of sub-units according to operating power, operating frequency, and the like. A digital unit may be implemented with at least one processor (eg, a digital signal processor (DSP)).

The wireless communication interface transmits and receives signals as described above. Accordingly, a wireless communication interface may be referred to as a “transmitter”, “receiver” or “transceiver”. In addition, in the following description, transmission and reception performed through a wireless channel may be used as a meaning including processing performed in a wireless communication interface as described above.

The backhaul communication interface provides an interface for communicating with other nodes in the network. That is, the backhaul communication interface converts the bit stream transmitted to other nodes, for example, other access nodes, other base stations, upper nodes or core networks from base stations into physical signals, and the physical signals received from other nodes into bits. convert to stream

The storage unit stores data such as basic programs, applications, and setting information for operation of the base station. The storage unit may include volatile memory, non-volatile memory, or a combination of volatile and non-volatile memory.

The controller controls the overall operation of the base station. For example, the controller transmits and receives signals through a wireless communication interface or a backhaul communication interface. Also, the controller writes data to the storage unit and reads the recorded data. The controller can perform protocol stack functions required by communication standards. According to another implementation, a protocol stack may be included in a wireless communication interface. To this end, the controller may include at least one processor.

According to an embodiment, the controller may control the base station to perform an operation according to an embodiment of the present invention.

According to various embodiments, a donor node of a wireless communication system includes at least one processor, includes a transceiver operably coupled to the at least one processor, and includes a plurality of radio bearers for a terminal accessing the relay node. configured to transmit to a relay node a first message including first information related to the donor node about; receive a second message including second information related to the relay node regarding a plurality of radio bearers for the terminal from the relay node; Data for the terminal may be transmitted to the relay node. Data may be transmitted to the terminal through a plurality of radio bearers based on the first information and the second information.

According to various embodiments, a radio bearer among a plurality of radio bearers may integrate a plurality of radio bearers. the at least one processor is also configured to determine a radio bearer for a terminal accessing the relay node and multiple radio bearers aggregated by the radio bearer; Alternatively, a radio bearer for a UE accessing a relay node may be determined.

According to various embodiments, the first message may include one or more of: identification of a terminal accessing the relay node; display information indicating the type of terminal accessing the relay node; information about a radio bearer of a terminal accessing a relay node; information about a radio bearer transmitted by a terminal accessing a relay node; information about a tunnel established for a radio bearer between the donor node and the relay node; information about integrated multiple radio bearers; radio bearer mapping information; information about the address on the side of the donor node; information about the address of the relay node side; indication information corresponding to a radio bearer of a terminal accessing the relay node; indication information indicating the relay node to allocate a new address to a radio bearer for a terminal accessing the relay node; a list of address information that cannot be used by a relay node that transmits radio bearer data of a terminal accessing the relay node; and information related to security configuration.

According to various embodiments, the second message may include one or more of: identification of a terminal accessing the relay node; information about the radio bearer granted by the relay node; information about radio bearers not acknowledged by the relay node; information about radio bearers partially granted by the relay node; radio bearer mapping information; Configuration information of a terminal accessing the relay node created by the relay node; information about the address of the relay node side; and information related to security configuration.

According to various embodiments, the second message may further include information on integrated multiple radio bearers.

According to various embodiments, the donor node may include a central unit of the donor node, and the relay node may include a distribution unit of the donor node.

According to various embodiments, a relay node of a wireless communication system includes at least one processor, includes a transceiver operably coupled to the at least one processor, and provides a plurality of information from a donor node to a terminal accessing the relay node. configured to receive a first message including first information related to a donor node for a radio bearer of; transmit to the donor node a second message including second information related to the relay node for the plurality of radio bearers for the terminal; Data on the terminal may be received from the donor node. Data may be transmitted to the terminal through a plurality of radio bearers based on the first information and the second information.

According to various embodiments, a radio bearer among a plurality of radio bearers may integrate a plurality of radio bearers. the at least one processor is also configured to determine a radio bearer for a terminal accessing the relay node and multiple radio bearers aggregated by the radio bearer; Alternatively, multiple radio bearers integrated by radio bearer may be determined.

According to various embodiments, the first message may include one or more of: identification of a terminal accessing the relay node; display information indicating the type of terminal accessing the relay node; information about a radio bearer of a terminal accessing a relay node; information about a radio bearer transmitted by a terminal accessing a relay node; information about a tunnel established for a radio bearer between the donor node and the relay node; information about integrated multiple radio bearers; radio bearer mapping information; information about the address of the side of the donor node; information about the address of the relay node side; indication information corresponding to a radio bearer of a terminal accessing the relay node; indication information indicating the relay node to allocate a new address to a radio bearer for a terminal accessing the relay node; a list of address information that cannot be used by a relay node that transmits radio bearer data of a terminal accessing the relay node; and information related to security configuration.

According to various embodiments, the second message may include one or more of: identification of a terminal accessing the relay node; information about the radio bearer granted by the relay node; information about radio bearers not acknowledged by the relay node; information about radio bearers partially granted by the relay node; radio bearer mapping information; Configuration information of a terminal accessing the relay node created by the relay node; information about the address of the relay node side; and information related to security configuration.

According to various embodiments, the second message may further include information on integrated multiple radio bearers.

According to various embodiments, the donor node may include a central unit of the donor node, and the relay node may include a distribution unit of the donor node.

Hereinafter, components of a terminal in the wireless communication system described above are illustrated. Components of a terminal to be described below are components of a general-purpose terminal supported by a wireless communication system, and may be merged or integrated with components of a terminal according to the foregoing contents, and may overlap or conflict with the above drawings. It can be interpreted that the content described with reference takes precedence. The terms "-module", "-unit" or "-er" used below may mean a unit that processes at least one function.

The terminal includes a communication interface, a storage unit and a controller.

The communication interface performs a function of transmitting and receiving signals through a wireless channel. For example, the communication interface performs a conversion function between a baseband signal and a bit stream according to the physical layer standard of the system. For example, in data transmission, a communication interface encodes and modulates a transmission bit stream to generate composite symbols. Also, when receiving data, the communication interface demodulates and decodes the baseband signal to reconstruct the received bit stream. Further, the communication interface up-converts the baseband signal to an RF-band signal, transmits the converted signal through an antenna, and down-converts the RF-band signal received through the antenna into a baseband signal. For example, the communication interface includes a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, and a digital-to-analog converter (DAC). , an analog-to-digital converter (ADC), and the like.

Also, the communication interface may include a plurality of transmit/receive paths. Additionally, the communication interface may include at least one antenna array comprising a plurality of antenna elements. On the hardware side, the wireless communication interface may include a digital circuit and an analog circuit (eg, a radio frequency integrated circuit (RFIC)). A digital circuit may be implemented with at least one processor (eg, DSP). A communication interface may include multiple RF chains. The communication interface may perform beamforming.

The communication interface transmits and receives signals as described above. Accordingly, a communication interface may be referred to as a “transmitter”, “receiver” or “transceiver”. In addition, in the following description, transmission and reception performed through a radio channel may be used as a meaning including processing performed in a communication interface as described above.

The storage unit stores data such as basic programs for operation of the terminal, applications, and setting information. The storage unit may include volatile memory, non-volatile memory, or a combination of volatile and non-volatile memory. In addition, the storage unit provides stored data according to the request of the controller.

The controller controls the overall operation of the terminal. For example, the controller sends and receives signals through a communication interface. Also, the controller writes data to the storage unit and reads the recorded data. The controller can perform protocol stack functions required by communication standards. According to another implementation, a protocol stack may be included in the communication interface. To this end, the controller may include at least one processor or microprocessor or reproduce parts of a processor. Also, a part of the communication interface or controller may be referred to as a communication processor (CP).

According to an embodiment of the present invention, a controller may control a terminal to perform an operation according to an embodiment of the present invention.

Hereinafter, a communication interface in a wireless communication system is illustrated.

The communication interface includes encoding and modulation circuitry, digital beamforming circuitry, a plurality of transmission paths, and analog beamforming circuitry.

Encoding and modulation circuitry performs channel encoding. At least one of a low-density parity check (LDPC) code, a convolution code, and a polar code may be used for channel encoding. An encoding and modulation circuit generates modulation symbols by performing constellation mapping.

A digital beamforming circuit performs beamforming on a digital signal (eg, a modulation symbol). To this end, a digital beamforming circuit multiplexes modulation symbols by beamforming weights. Beamforming weights can be used to change the size and phrase of a signal, and can be referred to as a "precoding matrix" or "precoder". The digital beamforming circuit outputs digital beamformed modulation symbols to a plurality of transmission paths. In this case, according to a multiple input multiple output (MIMO) transmission method, modulation symbols may be multiplexed or the same modulation symbol may be provided to a plurality of transmission paths.

The plurality of transmission paths convert digital beamformed digital signals into analog signals. To this end, each of the plurality of transmission paths may include an inverse fast fourier transform (IFFT) computation unit, a cyclic prefix (CP) insertion unit, a DAC, and an up conversion unit. The CP insertion unit is for an orthogonal frequency division multiplexing (OFDM) method and may be omitted when another physical layer method (eg, a filter bank multi-carrier: FBMC) is applied. That is, the plurality of transmission paths provide independent signal processing processes for a plurality of streams generated through digital beamforming. However, depending on the implementation, some elements of the plurality of transmission paths may be commonly used.

An analog beamforming circuit performs beamforming on an analog signal. To this end, the digital beamforming circuit multiplexes analog signals by beamforming weighting values. The beamformed weights are used to change the amplitude and phrase of the signal. More specifically, the analog beamforming circuit may be configured in various ways according to a connection structure between a plurality of transmission paths and an antenna. For example, each of a plurality of transmission paths may be connected to one antenna array. In another example, multiple transmission paths may be coupled to one antenna array. In another example, multiple transmission paths may be adaptively coupled to one antenna array or may be coupled to two or more antenna arrays.

The methods according to the present invention may be implemented in the form of program instructions that can be executed by various computer means and recorded on a computer readable medium. Computer readable media may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on a computer readable medium may be specially designed and configured for the present invention or may be known and usable to those skilled in computer software.

Examples of computer readable media may include hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions may include not only machine language codes generated by a compiler but also high-level language codes that can be executed by a computer using an interpreter and the like. The hardware device described above may be configured to operate with at least one software module to perform the operations of the present invention, and vice versa.

In addition, the above-described method or device may be implemented by combining all or some of its components or functions, or may be implemented separately.

Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art will variously modify and change the present invention within the scope not departing from the spirit and scope of the present invention described in the claims below. You will understand that it can be done.

Claims (10)

As a disease spread prediction service providing device using big data,
The device,
at least one processor; and
A memory for storing instructions instructing the at least one processor to perform at least one operation,
At least one operation,
generating a first value for each infectious disease by using symptoms received from a plurality of pharmacist terminals matched with the first area;
generating a second value for each infectious disease by using symptoms received from a plurality of pseudo terminals matched with the first area;
An operation of providing, to a plurality of doctor terminals matched with the first region, symptoms requiring confirmation that are matched with an infectious disease requiring confirmation, which is an infectious disease having a first value greater than a preset first reference value or a second value greater than a preset second reference value. ; and
Determining a first risk level of the infectious disease requiring confirmation using symptoms requiring confirmation received from a plurality of doctor terminals matching the first area,
Device. According to claim 1,
The operation of generating a first value for each infectious disease using symptoms received from a plurality of pharmacist terminals matching the first area,
generating a frequency of occurrence for each symptom using the symptoms received from a plurality of pharmacist terminals matched with the first region, and generating a first input vector having each symptom count as a dimensional value; and
Inputting the first input vector as an input value to the pre-learned first artificial neural network and receiving a first value for each infectious disease from the first artificial neural network,
Device. According to claim 2,
The operation of generating a second value for each infectious disease by using symptoms received from a plurality of pseudo terminals matching the first region,
generating counts of occurrence for each symptom by using symptoms received from a plurality of pseudo terminals matched with the first region, and generating a second input vector having each of the counts of occurrence for each symptom as a dimensional value; and
Inputting the second input vector as an input value to the pre-learned second artificial neural network, and receiving a second value for each infectious disease from the second artificial neural network
Device. According to claim 3,
The operation of determining the first risk of outbreak of the infectious disease requiring confirmation using the symptoms requiring confirmation received from a plurality of doctor terminals matching the first area,
generating the number of occurrences of each symptom requiring confirmation by using symptoms to be confirmed received from the plurality of pseudo terminals matching the first area, and generating a third input vector having each of the number of occurrences of each symptom requiring confirmation as a dimensional value; and
Inputting the third input vector as an input value to the pre-learned third artificial neural network, and receiving the first outbreak risk of the infectious disease requiring confirmation from the third artificial neural network,
Device. According to claim 4,
At least one operation,
selecting a plurality of second regions adjacent to the first region where the first onset risk is greater than a preset first reference risk;
providing a symptom requiring verification to a plurality of pseudo terminals matching each of the plurality of second regions;
determining a second risk of developing an infectious disease requiring confirmation in the second area by using symptoms requiring confirmation received from a plurality of doctor terminals matched with the second area;
determining a risk of proliferation in the second region based on the number of populations in the second region, the number of floating populations between the second region and the first region, a first risk of outbreak, and a second risk of outbreak; and
Including the operation of determining a correspondence order corresponding to each of the plurality of second regions in order of highest proliferation risk,
Device. According to claim 5,
The operation of determining the second risk of developing an infectious disease requiring confirmation in the second area by using the symptoms requiring confirmation received from a plurality of doctor terminals matched with the second area,
generating a frequency of occurrence for each symptom requiring confirmation using symptoms to be confirmed received from a plurality of pseudo terminals matched with the second region, and generating a fourth input vector having each of the number of occurrences for each symptom to be confirmed as a dimension value; and
Inputting the fourth input vector as an input value to the pre-learned third artificial neural network and receiving a second risk of outbreak of the infectious disease requiring confirmation from the third artificial neural network,
Device. As an operating method for providing a 3D modeling service for undersea topography using big data,
generating a first value for each infectious disease by using symptoms received from a plurality of pharmacist terminals matched with the first area;
generating a second value for each infectious disease by using symptoms received from a plurality of pseudo terminals matched with the first area;
An operation of providing, to a plurality of doctor terminals matched with the first region, symptoms requiring confirmation that are matched with an infectious disease requiring confirmation, which is an infectious disease having a first value greater than a preset first reference value or a second value greater than a preset second reference value. ; and
Determining a first risk level of the infectious disease requiring confirmation using symptoms requiring confirmation received from a plurality of doctor terminals matching the first area,
how it works. A non-transitory recording medium on which a program for executing the operating method according to claim 7 is recorded and can be read by a computer. A computer program recorded on a non-transitory recording medium in order to execute the operation method according to claim 7 in a 3D modeling device for seafloor terrain using big data. A system for providing a 3D modeling service for undersea topography using big data,
a plurality of doctor terminals, providing symptoms and symptoms to be confirmed to the device;
a plurality of pharmacist terminals that provide symptoms and symptoms requiring confirmation to the device; and
Including a device for determining the risk of onset using the received symptoms and symptoms requiring confirmation,
The device,
at least one processor; and
A memory for storing instructions instructing the at least one processor to perform at least one operation,
At least one operation,
generating a first value for each infectious disease by using symptoms received from a plurality of pharmacist terminals matched with the first area;
generating a second value for each infectious disease by using symptoms received from a plurality of pseudo terminals matched with the first region;
An operation of providing, to a plurality of doctor terminals matched with the first region, symptoms requiring confirmation that are matched with an infectious disease requiring confirmation, which is an infectious disease having a first value greater than a preset first reference value or a second value greater than a preset second reference value. ; and
Determining the risk of developing the infectious disease requiring confirmation using symptoms requiring confirmation received from a plurality of doctor terminals matching the first area,
system.

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