JP2018147495A - Prediction device, prediction method, prediction program, information processing device, information processing method, and information processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To appropriately predict the amount of damage relating to a disaster.SOLUTION: A prediction device includes an acquisition unit and a prediction unit. The acquisition unit obtains occurrence information about a disaster, target information about a target located in an area affected by the disaster, and a correspondence table in which the amount of damage is associated with a score. The prediction unit predicts the amount of damage relating to the disaster on the basis of both the score calculated from the occurrence information and target information which are obtained by the acquisition unit and the correspondence table.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a prediction device, a prediction method, a prediction program, an information processing device, an information processing method, and an information processing program.

  Conventionally, various techniques such as risk assessment in the occurrence of various disasters such as natural disasters have been provided. For example, disaster risk assessment technology is provided that enables decision making for disaster countermeasures based on an evaluation of an operator's economic viewpoint.

JP 2005-141334 A

  However, with the above-described conventional technology, it is not always possible to appropriately predict the amount of damage related to a disaster. For example, it is difficult to appropriately predict the amount of damage related to a disaster when an actual disaster occurs by simply formulating a disaster countermeasure plan and estimating a countermeasure cost.

  The present application has been made in view of the above, and an object thereof is to provide a prediction device, a prediction method, a prediction program, an information processing device, an information processing method, and an information processing program for appropriately predicting a damage amount related to a disaster. And

  The prediction device according to the present application includes an acquisition unit and a prediction unit. The acquisition unit acquires occurrence information related to a disaster, target information related to an object located in an area affected by the disaster, and a correspondence table in which the amount of damage is associated with a score. The prediction unit predicts the amount of damage related to the disaster based on the occurrence information acquired by the acquisition unit, the score calculated from the target information, and the correspondence table.

  According to the aspect of the embodiment, there is an effect that it is possible to appropriately predict the amount of damage related to the disaster.

FIG. 1 is a diagram illustrating an example of a prediction process according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of the prediction system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the prediction device according to the embodiment. FIG. 4 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of the asset information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of an insurance information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of the disaster history information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of a prediction model information storage unit according to the embodiment. FIG. 9 is a diagram illustrating an example of the occurrence information storage unit according to the embodiment. FIG. 10 is a flowchart illustrating an example of generation of a prediction model according to the embodiment. FIG. 11 is a flowchart illustrating an example of the prediction process according to the embodiment. FIG. 12 is a conceptual diagram illustrating an example of generation of a prediction model according to the embodiment. FIG. 13 is a conceptual diagram illustrating an example of a prediction process according to the embodiment. FIG. 14 is a diagram illustrating an example of a correspondence table used for predicting the amount of damage. FIG. 15 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the prediction device.

  Hereinafter, modes for carrying out a prediction apparatus, a prediction method, and a prediction program according to the present application (hereinafter referred to as “embodiments”) will be described in detail with reference to the drawings. Note that the prediction device, the prediction method, and the prediction program according to the present application are not limited to the embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

(Embodiment)
[1. (Prediction process)
First, an example of a prediction process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a prediction process according to the embodiment. In the example of FIG. 1, the prediction apparatus 100 shows a case where the amount of damage related to a disaster that has occurred (hereinafter also simply referred to as “disaster”) is predicted. Specifically, in the example of FIG. 1, the prediction apparatus 100 has an insurance company when the disaster type is “Typhoon” and a typhoon Z that is a disaster identified by the occurrence disaster ID “OC11” occurs. The case where the insurance premium (payment amount) paid by the company A is predicted. In the example of FIG. 1, as shown in the map information MP1, the target area of the typhoon Z is the area A identified by the area ID “AR11” and the area B identified by the area ID “AR12”. Will be described as an example. In the example of FIG. 1, the prediction apparatus 100 illustrates a case where the payment amount of the company A is predicted using a model corresponding to a disaster that has occurred among a plurality of prediction models corresponding to each disaster type. The size of the area here may be set as appropriate according to the purpose of the processing. For example, areas of various sizes such as “country”, “region”, “prefecture”, “city”, “ward”, “town” may be set as appropriate. For example, the area may be the Kanto region, Tokyo, or the like.

[Configuration of prediction system]
Prior to the description of FIG. 1, the configuration of the prediction system 1 will be described with reference to FIG. FIG. 2 is a diagram illustrating a configuration example of the prediction system according to the embodiment. As shown in FIG. 2, the prediction system 1 includes a terminal device 10, an insurance service device 20, an open data providing device 50, and a prediction device 100. The terminal device 10, the insurance service device 20, the open data providing device 50, and the prediction device 100 are connected via a predetermined network N so as to be communicable by wire or wirelessly. The prediction system 1 shown in FIG. 2 includes a plurality of terminal devices 10, a plurality of insurance service devices 20, a plurality of open data providing devices 50, and a plurality of prediction devices 100. Also good.

  The terminal device 10 is an information processing device used by a user. The terminal device 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. FIG. 1 shows a case where the terminal device 10 is a smartphone. The terminal device 10 has a function such as a GPS (Global Positioning System) sensor, and can detect and acquire the position of the user. In addition, the terminal device 10 transmits various information and the like to the prediction device 100. For example, the terminal device 10 transmits position information regarding the user to the prediction device 100. Hereinafter, the terminal device 10 may be referred to as a user. That is, hereinafter, the user can be read as the terminal device 10.

  The insurance service device 20 is an information processing device used by an insurance company administrator or the like. For example, the insurance service device 20 transmits various types of information related to insurance as illustrated in FIGS. 6 and 12 to the prediction device 100. Further, for example, when the insurance service device 20 has the asset information of the user, the insurance service device 20 transmits various types of information regarding the asset of the user as illustrated in FIG.

  The open data providing device 50 is an information processing device that provides open data, which is so-called widely open data, to the prediction device 100. For example, the open data providing device 50 may be an information processing device that is managed by each local government and provides data held by the local government to the outside as open data. Further, for example, the open data providing device 50 may be various information processing devices such as a server managed by the Japan Meteorological Agency. The open data providing device 50 is not limited to the above as long as it is data (information) that is permitted to be used, and is open data related to traffic such as road information, open data related to weather such as weather and temperature, geospatial information, disaster prevention・ Various open data such as disaster mitigation information, procurement information, and statistical information may be provided.

  The prediction device 100 is an information processing device that predicts a damage amount related to a disaster based on occurrence information related to a disaster and target information related to an object located in an area affected by the disaster. In addition, the prediction device 100 predicts a damage amount related to a disaster using a prediction model corresponding to a disaster type to be predicted among a plurality of prediction models generated for each disaster type.

  From here, the flow of a prediction process is demonstrated using FIG. First, in the example of FIG. 1, as shown in the map information MP1, typhoon Z, which is a disaster identified by the occurrence disaster ID “OC11”, occurs (step S11). Here, the map information MP1 shown in FIG. 1 will be briefly described. The map information MP1 illustrated in FIG. 1 is a diagram schematically illustrating the position of a typhoon Z that is a disaster identified by the occurrence disaster ID “OC11”. For example, the map information MP1 shows a predicted course PT11 of typhoon Z, which indicates that the typhoon Z is expected to take a course through area A and area B.

  Therefore, in the example of FIG. 1, the prediction device 100 predicts the payment amount of the company A by setting the target areas of the typhoon Z as area A and area B. First, the prediction device 100 extracts users located in area A and area B, which are target areas of typhoon Z (step S12). For example, the prediction device 100 extracts users located in the area A and the area B based on the position information of each user stored in the user information storage unit 121.

  Here, “user ID” shown in the user information storage unit 121 in FIG. 1 indicates identification information for identifying a user. Hereinafter, the user identified by the user ID “U1” may be referred to as “user U1”. Thus, hereinafter, when “user U * (* is an arbitrary numerical value)” is described, this indicates that the user is a user identified by the user ID “U *”. For example, when “user U2” is described, the user is a user identified by the user ID “U2”.

  Further, “latest position” shown in the user information storage unit 121 in FIG. 1 indicates the position information acquired last for the corresponding user. Further, “acquisition date / time” shown in the user information storage unit 121 in FIG. 1 indicates the date / time when the position information was last acquired.

  For example, in the example of FIG. 1, the user U1 indicates that the latest position is the position LC11 in the area A. For example, in the example of FIG. 1, the user U2 indicates that the latest position is the position LC21 in the area B. For example, in the example of FIG. 1, for the user U3, the latest position is the position LC31 in the area C. For example, in the example of FIG. 1, for the user U4, the latest position is the position LC25 in the area B. For example, in the example of FIG. 1, for the user U5, the latest position is the position LC51 in the area E.

  In the example of FIG. 1, the prediction device 100 extracts the user U1, the user U2, the user U4, and the like who are users located in the area A or the area B. For example, the prediction device 100 does not extract the user U3, the user U5, and the like that are located in an area other than the area A or the area B.

  And the prediction apparatus 100 extracts the asset located in the area A and the area B which are the target areas of typhoon Z (step S13). For example, the prediction apparatus 100 extracts assets located in area A and area B based on the location of each asset stored in the asset information storage unit 122.

  Here, the “asset information” shown in the asset information storage unit 122 in FIG. 1 indicates information related to assets owned by each user. “Asset ID” shown in the asset information storage unit 122 in FIG. 1 indicates identification information for identifying an asset owned by the user. Also, “asset type” shown in the asset information storage unit 122 in FIG. 1 indicates a specific name of the type of asset owned by the user. Further, “location” shown in the asset information storage unit 122 in FIG. 1 indicates the location value of the asset. Further, “value” shown in the asset information storage unit 122 in FIG. 1 indicates the monetary value of the asset.

  For example, in the example of FIG. 1, the user U1 has the asset “house” identified by the asset ID “AS1-1”, the asset “automobile” identified by the asset ID “AS1-2”, and the asset ID “AS1-”. 5 ”indicates that an asset such as“ house ”identified by“ 5 ”is owned. Hereinafter, the asset identified by the asset ID “AS1-1” may be referred to as “asset AS1-1”. Thus, in the following, when “asset AS * (* is an arbitrary numerical value)” is described, it indicates that the asset is an asset identified by the asset ID “AS *”. For example, when “asset AS2-1” is described, the asset is an asset identified by the asset ID “AS2-1”.

  For example, in the example of FIG. 1, the house that is the asset AS <b> 1-1 owned by the user U <b> 1 is located at the position LC <b> 12 in the area A, and the monetary value is “A million yen”. Symbols such as “A” to “Z” immediately before the character string “10,000 yen” or “100 million yen” indicating the amount correspond to a predetermined number of 1 or more. For example, when “A” in “A million yen” corresponds to the number “7000”, “A million yen” means “70 million yen”. Further, for example, in the example of FIG. 1, the automobile that is the asset AS1-2 owned by the user U1 is located at the position LC13 in the area A, and the monetary value is “B million yen”. Note that the location relating to the movable property such as an automobile may be acquired by a function such as GPS installed in the automobile and updated as needed.

  In the example of FIG. 1, the prediction device 100 extracts the asset AS1-1, the asset AS1-2, the asset AS1-2, the asset AS2-1 of the user U2, etc., which are assets located in the area A or the area B. For example, the prediction device 100 does not extract the user U3, the user U5, and the like that are located in an area other than the area A or the area B. For example, the prediction device 100 does not extract the asset AS1-5 of the user U1 and the asset AS4-1 of the user U4 that are located in an area other than the area A or the area B.

  And the prediction apparatus 100 extracts the insurance information used as the object of the insurance payment in Typhoon Z out of the insurance information of A company which is a target company which estimates payment amount (step S14). For example, the prediction apparatus 100 extracts insurance information in which the user or asset to be insured is located in the areas A and B from the insurance information of the company A stored in the insurance information storage unit 123.

  Here, “subscription information” shown in the insurance information storage unit 123 in FIG. 1 indicates information on insurance provided (contracted) by each insurance company. The “user ID” shown in the insurance information storage unit 123 in FIG. 1 indicates identification information for identifying a user who has contracted with an insurance company. The “insurance type ID” shown in the insurance information storage unit 123 in FIG. 1 indicates identification information for identifying the type of insurance that the user has joined. Further, “insurance type” shown in the insurance information storage unit 123 in FIG. 1 indicates a specific name of the type of insurance that the user has joined. In addition, “target” shown in the insurance information storage unit 123 in FIG. 1 indicates the insurance target to which the user has joined. Further, the “limit amount” shown in the insurance information storage unit 123 in FIG. 1 indicates the limit amount of insurance money paid by the user.

  In the example of FIG. 1, the user U <b> 1 indicates that he / she has subscribed to home insurance or disability insurance provided by Company A. For example, the user U1 indicates that the target is the asset AS1-1 or asset AS1-5 in the housing insurance of the company A. For example, the user U1 indicates that the target is the user U1 himself / herself in the accident insurance of the company A.

  In the example of FIG. 1, the prediction device 100 extracts insurance information indicating that the asset AS1-1 of the user U1 located in the area A and the user U1 themselves are insured. In the example of FIG. 1, the prediction device 100 extracts insurance information indicating that the user U <b> 2 himself / herself located in the area B is insured.

  And the prediction apparatus 100 determines the prediction model used for prediction of the payment amount of A company (step S15). For example, the prediction device 100 determines a prediction model used for predicting the payment amount of the company A from a plurality of prediction models M1 to M3 stored in the prediction model information storage unit 125. In the example of FIG. 1, it is assumed that the prediction models M1 to M3 corresponding to each disaster type as shown in the prediction model information storage unit 125 have been generated. In the example of FIG. 1, the prediction models M1 to M3 are prediction models that generate payment information of the target company at the time of disaster for each disaster type as correct information, as shown in FIG. It is assumed that the prediction model corresponds to the type.

  The prediction models M1 to M3 and the like shown in the prediction model information storage unit 125 in FIG. 1 store the disaster types identified by the disaster type IDs in association with each other. Further, each weight (value) such as “feature 1” to “feature 3” is stored in association with each of the prediction models M1 to M3 shown in the prediction model information storage unit 125 in FIG.

  For example, in the example of FIG. 1, the prediction model information related to the prediction model M1 indicates that the prediction model corresponds to the disaster type “typhoon” identified by the disaster type ID “DS11”. For example, in the example of FIG. 1, the prediction model information regarding the prediction model M2 indicates that the prediction model corresponds to the disease “earthquake” identified by the disaster type ID “DS12”.

  For example, in the example of FIG. 1, the prediction model information related to the prediction model M1 includes feature 1 weight “0.5”, feature 2 weight “0.4”, feature 3 weight “0.2”, and the like. Indicates that In the example of FIG. 1, the prediction apparatus 100 predicts the amount of damage caused by a disaster based on the prediction models M1 to M3 and the like stored in the prediction model information storage unit 125 in this way. Each model may be generated using various conventional techniques relating to machine learning as appropriate. For example, the model generation may be performed using a technique related to machine learning of supervised learning such as SVM (Support Vector Machine). Further, for example, the model generation may be performed using a technique related to machine learning of unsupervised learning. For example, the generation of the model may be performed using a deep learning technique. For example, the model generation may be performed by appropriately using various deep learning techniques such as DNN (Deep Neural Network), RNN (Recurrent Neural Network), and CNN (Convolutional Neural Network). The description relating to the generation of the model is merely an example, and the generation of the model may be performed by a learning method appropriately selected according to information that can be acquired.

  In the example of FIG. 1, the prediction device 100 determines a prediction model that uses the prediction model M1 corresponding to the disaster type “typhoon” identified by the disaster type ID “DS11” that is the disaster type of typhoon Z.

  And the prediction apparatus 100 estimates the payment amount of A company by typhoon Z using the prediction model M1 (step S16). For example, the prediction device 100 predicts the payment amount of company A by typhoon Z by inputting information related to the user, information related to assets, and information related to insurance extracted in steps S12 to S14 to the prediction model M1. For example, the prediction apparatus 100 predicts the score output by the prediction model M1 as the payment amount of the company A by the typhoon Z by inputting information regarding the user, information regarding assets, and information regarding insurance into the prediction model M1. In the example of FIG. 1, the prediction device 100 predicts the payment amount of Company A in Typhoon Z as “P billion yen” as shown in the prediction information PL11.

  Thus, the prediction device 100 can appropriately predict the amount of damage related to the disaster. In addition, in order to simplify description in FIG. 1, the example which estimates the payment amount of A company combining the area A and the area B was shown, However, The prediction apparatus 100 separates the area A and the area B separately. The amount of payment may be predicted.

[1-1. Score〕
In the example described above, the case where the score output by the prediction model is used as the amount of damage (payment amount) is shown. However, the prediction device 100 uses the score output by the prediction model as an index indicating the level of the amount of damage (payment amount). It may be used. For example, the prediction apparatus 100 may predict the amount of damage (payment amount) using a score output from the prediction model and a correspondence table. This point will be described with reference to FIG. FIG. 14 is a diagram illustrating an example of a correspondence table used for predicting the amount of damage.

  In the example of FIG. 14, the prediction apparatus 100 predicts the amount of damage (payment amount) using the correspondence table TB11. “Rank ID” shown in the correspondence table TB11 in FIG. 14 indicates identification information for identifying the rank of each score. Further, “score” shown in the correspondence table TB11 in FIG. 14 indicates a score output by the prediction model. Further, “damage amount (yen)” shown in the correspondence table TB11 in FIG. 14 indicates the amount of damage corresponding to each score.

  In the example of FIG. 14, for example, when the prediction model used by the prediction device 100 to predict the damage amount of a predetermined disaster outputs a score “3”, the score “3” is ranked ID “RN1” in the correspondence table TB11. Corresponds to the rank identified by. Therefore, the prediction apparatus 100 predicts the damage amount “200 to 400 million yen” corresponding to the rank identified by the rank ID “RN1” in the correspondence table TB11 as the damage amount of the predetermined disaster.

  In the example of FIG. 14, for example, when the prediction model used by the prediction device 100 to predict the damage amount of a predetermined disaster outputs a score “7”, the score “7” is ranked in the rank ID “RN2” in the correspondence table TB11. Corresponds to the rank identified by. Therefore, the prediction device 100 predicts the damage amount “5 to 1 billion yen” corresponding to the rank identified by the rank ID “RN2” in the correspondence table TB11 as the damage amount of the predetermined disaster.

  In the example of FIG. 14, for example, when the prediction model used by the prediction device 100 to predict the damage amount of a predetermined disaster outputs a score “14”, the score “14” is ranked ID “RN3” in the correspondence table TB11. Corresponds to the rank identified by. Therefore, the prediction apparatus 100 predicts the damage amount “1.5 to 3 billion yen” corresponding to the rank identified by the rank ID “RN3” in the correspondence table TB11 as the damage amount of the predetermined disaster.

  In addition, the prediction process mentioned above is an example, and the prediction apparatus 100 may predict the amount of damage using various information as appropriate.

[2. Configuration of prediction device]
Next, the configuration of the prediction apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the prediction device 100 according to the embodiment. As illustrated in FIG. 3, the prediction device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The prediction device 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator of the prediction device 100, and a display unit (for example, a liquid crystal display) for displaying various types of information. May be.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network by wire or wireless, and transmits / receives information to / from the terminal device 10.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 3, the storage unit 120 according to the embodiment includes a user information storage unit 121, an asset information storage unit 122, an insurance information storage unit 123, a disaster history information storage unit 124, and a prediction model information storage unit. 125 and a generation information storage unit 126. In addition, the storage unit 120 may store open data OD1, service data SD1, insurance data AD1, open data OD2, service data SD2, insurance data AD2, and the like as shown in FIG.

(User information storage unit 121)
The user information storage unit 121 according to the embodiment stores various types of information regarding the user. For example, the user information storage unit 121 stores various types of information regarding user attributes. FIG. 4 is a diagram illustrating an example of a user information storage unit according to the embodiment. The user information storage unit 121 illustrated in FIG. 4 includes items such as “user ID”, “age”, “gender”, and “position information”. The “position information” includes items such as “latest position” and “acquisition date”.

  “User ID” indicates identification information for identifying a user. “Age” indicates the age of the user identified by the user ID. The “age” may be a specific age of the user identified by the user ID, such as 35 years old. “Gender” indicates the gender of the user identified by the user ID.

  The “latest position” in the “position information” indicates the position information acquired last for the corresponding user. In the example of FIG. 4, “latest position” shows information indicating an abstract code and range such as “LC11 (area A)”, but information indicating latitude and longitude, It may be information indicating an address such as “”. In addition, “acquisition date / time” in “position information” indicates the date / time when the position information was last acquired. In the example of FIG. 4, “TM1” or the like is illustrated, but “acquisition date / time” may store a specific date / time such as “February 15, 2017 19:38:56”. .

  For example, in the example of FIG. 4, the age of the user (user U1) identified by the user ID “U1” is “20s”, and the gender is “male”. Further, for example, for the user U1, the position information indicating that the user U1 is located at the position LC11 in the area A acquired at the date and time TM1 is the position information acquired last, that is, the latest position information.

  The user information storage unit 121 is not limited to the above, and may store various types of information according to the purpose. For example, the user information storage unit 121 may store various information such as demographic attribute information and psychographic attribute information. For example, the user information storage unit 121 may store information such as name, family structure, and income.

(Asset information storage unit 122)
The asset information storage unit 122 according to the embodiment stores information (asset information) related to user assets. FIG. 5 is a diagram illustrating an example of the asset information storage unit according to the embodiment. For example, the asset information storage unit 122 stores information on various assets such as real estate and movable property owned by the user. The asset information storage unit 122 illustrated in FIG. 5 includes items such as “user ID” and “asset information”. The “asset information” includes items such as “asset ID”, “asset type”, “location”, and “value”.

  “User ID” indicates identification information for identifying a user. “Asset ID” in “Asset Information” indicates identification information for identifying an asset owned by the user. In addition, “asset type” in “asset information” indicates a specific name of the type of asset owned by the user. The “location” in the “asset information” indicates the location of the asset owned by the user. In addition, “location” shows information indicating an abstract code and range such as “LC12 (area A)”, but information indicating latitude and longitude, an address such as “Y city Z town” of X prefecture, etc. It may be the information shown. Further, “value” in “asset information” indicates the monetary value of the asset owned by the user.

  For example, in the example of FIG. 5, the user (user U1) identified by the user ID “U1” is identified by the asset “house” identified by the asset ID “AS1-1” and the asset ID “AS1-2”. The asset “car” and the asset “house” identified by the asset ID “AS1-5”. For example, in the example of FIG. 5, the asset “house” identified by the asset ID “AS1-1” owned by the user U1 is located at the position LC12 in the area A, and the monetary value thereof is “A10,000”. "Yen". Further, for example, in the example of FIG. 5, the asset “car” identified by the asset ID “AS1-2” owned by the user U1 is located at the position LC13 in the area A, and its monetary value is “B0000”. "Yen". Note that the location relating to the movable property such as an automobile may be acquired by a function such as GPS installed in the automobile and updated as needed.

  The asset information storage unit 122 is not limited to the above, and may store various types of information according to the purpose.

(Insurance information storage unit 123)
The insurance information storage unit 123 according to the embodiment stores information (insurance information) related to user insurance. FIG. 6 is a diagram illustrating an example of an insurance information storage unit according to the embodiment. For example, the insurance information storage unit 123 stores various types of information related to the insurance that each user subscribes as insurance information. The insurance information storage unit 123 illustrated in FIG. 6 includes items such as “insurance company ID”, “name”, and “subscription information”. The “subscription information” includes items such as “user ID”, “insurance type ID”, “insurance type”, “target”, and “limit”.

  “Insurance company ID” indicates identification information for identifying an insurance company. “Name” indicates a specific name of the insurance company.

  “User ID” in “subscription information” indicates identification information for identifying a user. For example, “user ID” in “subscription information” indicates identification information for identifying a user who has made an insurance contract. “Insurance type ID” in “subscription information” indicates identification information for identifying the type of insurance that the user has subscribed to. The “insurance type” in the “subscription information” indicates a specific name of the type of insurance that the user has joined. The “subject” in the “subscription information” indicates the insurance subject to which the user has subscribed. Further, the “limit” in the “subscription information” indicates the limit of the insurance premium paid by the user.

  For example, in the example of FIG. 6, the insurance company identified by the insurance company ID “IS11” is the name “Company A”. Moreover, in the example of FIG. 6, it shows that the user U1 and the user U2 have joined the insurance which A company provides. In the example of FIG. 6, the user U <b> 1 indicates that he / she has a home insurance identified by the insurance type ID “TP11” provided by Company A and a disability insurance identified by the insurance type ID “TP12”. .

  Further, for example, in the example of FIG. 6, the user U1 indicates that the target is subscribed to the home insurance identified by the insurance type ID “TP11” provided by the company A as the asset AS1-1. Further, in the example of FIG. 6, it is shown that the limit amount of the home insurance of the company A that has joined the target as the asset AS1-1 is D 10,000 yen.

  Further, in the example of FIG. 6, the user U1 indicates that the target is the user U1 in the failure insurance identified by the insurance type ID “TP12” provided by the company A. Further, in the example of FIG. 6, it is shown that the limit of the disability insurance of the company A who has subscribed as the user U1 is E 10,000 yen.

  Further, for example, in the example of FIG. 6, the user U1 indicates that the target is subscribed to the home insurance identified by the insurance type ID “TP11” provided by the company A as the asset AS1-5. Further, in the example of FIG. 6, it is shown that the limit amount of the home insurance of the company A that has joined the target as the asset AS1-5 is F million yen.

  The insurance information storage unit 123 is not limited to the above, and may store various information according to the purpose.

(Disaster history information storage unit 124)
The disaster history information storage unit 124 according to the embodiment stores information related to the disaster history. FIG. 7 is a diagram illustrating an example of the disaster history information storage unit according to the embodiment. For example, the disaster history information storage unit 124 stores various information related to disasters that have occurred in the past. The disaster history information storage unit 124 shown in FIG. 7 includes items such as “history ID”, “disaster type ID”, “name”, “target area”, “disaster intensity”, “damage scale”, and “damage amount”. included.

  “History ID” indicates identification information for identifying each disaster history. “Disaster type ID” indicates identification information for identifying a disaster type. “Name” indicates a specific name of the disaster. “Target area” indicates an area damaged by a disaster. “Disaster intensity” indicates the intensity of disaster in the corresponding area. In the example of FIG. 7, a symbol such as “LV11” is illustrated, but a specific numerical value or the like corresponding to each disaster may be stored in “disaster intensity”. For example, when the disaster is “typhoon”, specific numerical values such as “maximum wind speed of 54 meters” and “central pressure of 900 hectopascals” may be stored in “disaster intensity”. “Damage scale” indicates the scale of disaster damage in the corresponding area. In the example of FIG. 7, a symbol such as “DM11” is illustrated, but a specific numerical value or the like corresponding to each disaster may be stored in “disaster scale”. For example, in the “damage scale”, a specific numerical value such as “totally destroyed 5,000 buildings” or “half-destroyed 10,000 buildings” may be stored. “Damage amount” indicates the amount of damage caused by a disaster in the corresponding area.

  For example, the example of FIG. 7 indicates that the disaster type identified by the history ID “LG11” is “typhoon” identified by the disaster type ID “DS11”. The disaster name identified by the history ID “LG11” is Typhoon A. The disaster target area identified by the history ID “LG11” is area A or area B.

  For example, the disaster identified by the history ID “LG11” indicates that the disaster intensity in the area A is “LV11”. For example, a disaster identified by the history ID “LG11” indicates that the damage scale in the area A is “DM11”. For example, a disaster identified by the history ID “LG11” indicates that the amount of damage in area A is “X billion yen”.

  The disaster history information storage unit 124 is not limited to the above, and may store various information according to the purpose. For example, the disaster history information storage unit 124 may store information related to the date and time when the disaster occurred.

(Prediction model information storage unit 125)
The prediction model information storage unit 125 according to the embodiment stores information related to the prediction model. For example, the prediction model information storage unit 125 stores prediction model information generated by the generation process. FIG. 8 is a diagram illustrating an example of a prediction model information storage unit according to the embodiment. In the prediction model information storage unit 125 illustrated in FIG. 8, a prediction model corresponding to each disaster type is stored. The prediction model information storage unit 125 illustrated in FIG. 8 includes items such as “feature 1” to “feature 3” corresponding to the prediction models M1 to M3 as the prediction model information.

  For example, in the example of FIG. 8, the prediction model information regarding the prediction model M1 indicates that the prediction model corresponds to the disaster type “typhoon” identified by the disaster type ID “DS11”. For example, in the example of FIG. 8, the prediction model information regarding the prediction model M2 indicates that the prediction model corresponds to the disaster type “earthquake” identified by the disaster type ID “DS12”.

  For example, in the example of FIG. 8, the prediction model information related to the prediction model M1 includes feature 1 weight “0.5”, feature 2 weight “0.4”, feature 3 weight “0.2”, and the like. Indicates that For example, when the feature (feature value) of the model is expressed by an m-dimensional vector, the number of features is m, and the weights of the features 1 to m are stored. For example, in the example of FIG. 8, the feature of the prediction model may be a feature corresponding to asset information, insurance information, or user information.

  The prediction model information storage unit 125 is not limited to the above, and may store various prediction model information according to the purpose.

(Generation information storage unit 126)
The occurrence information storage unit 126 according to the embodiment stores information (occurrence information) related to a disaster that occurs. For example, the occurrence information storage unit 126 stores occurrence information related to a disaster that has occurred. FIG. 9 is a diagram illustrating an example of the occurrence information storage unit according to the embodiment. The occurrence information storage unit 126 illustrated in FIG. 9 includes items such as “occurrence disaster ID”, “name”, and “disaster target information”. The “disaster target information” includes items such as “target area” and “predicted strength”.

  The “occurrence disaster ID” indicates identification information for identifying the occurred disaster. “Name” indicates a specific name of the disaster that has occurred. The “target area” in the “disaster target information” indicates an area to be damaged by the disaster that has occurred. “Predicted intensity” in “disaster target information” indicates the intensity of a disaster predicted in the corresponding area. In the example of FIG. 9, a symbol such as “LV21” is illustrated, but a specific numerical value or the like corresponding to each disaster may be stored in the “predicted strength”. For example, when the disaster is “earthquake”, a specific numerical value such as “seismic intensity 5” may be stored in the “predicted strength”. Further, the “target area” and “predicted strength” in the “disaster target information” may be updated as needed. The “target area” and “predicted intensity” in the “disaster target information” may be updated as needed in accordance with the course change or scale change of the disaster “typhoon”.

  For example, in the example of FIG. 9, the disaster identified by the occurrence disaster ID “OC11” is Typhoon Z. For example, the disaster identified by the occurrence disaster ID “OC11” indicates that the target area includes area A and area B. Further, for example, a disaster identified by the occurrence disaster ID “OC11” indicates that the predicted intensity in the area A is “LV21”. Further, for example, a disaster identified by the occurrence disaster ID “OC11” indicates that the predicted intensity in the area B is “LV22”.

  In addition, the generation | occurrence | production information storage part 126 may memorize | store not only above but various information according to the objective.

(Control unit 130)
Returning to the description of FIG. 3, the control unit 130 is a controller and is stored in a storage device inside the prediction device 100 by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Various programs (corresponding to an example of a prediction program) are implemented by using the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 3, the control unit 130 includes an acquisition unit 131, an extraction unit 132, a generation unit 133, a prediction unit 134, and a provision unit 135, and functions and functions of information processing described below. Realize or execute. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 3, and may be another configuration as long as the information processing described below is performed. Further, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship illustrated in FIG. 3, and may be another connection relationship.

(Acquisition part 131)
The acquisition unit 131 acquires various information. The acquisition unit 131 acquires various types of information from an external device such as the terminal device 10. In addition, the acquisition unit 131 acquires various types of information from the storage unit 120. The acquisition unit 131 includes a user information storage unit 121, an asset information storage unit 122, an insurance information storage unit 123, a disaster history information storage unit 124, a prediction model information storage unit 125, an occurrence information storage unit 126, and the like. Get various information from.

  For example, the acquisition unit 131 acquires occurrence information related to a disaster and target information related to a target located in an area affected by the disaster. For example, the acquisition unit 131 acquires target information including asset information related to assets owned by the user and located in an area affected by the occurrence of a disaster. For example, the acquisition unit 131 acquires target information including position information related to a user in the occurrence of a disaster. For example, the acquisition unit 131 acquires occurrence information regarding a disaster that is one of an earthquake, a typhoon, or a traffic disaster. For example, the acquisition unit 131 acquires insurance information related to an insurance company. For example, the acquisition unit 131 acquires insurance information of each user who is a user who joins a predetermined insurance provided by an insurance company and is related to an object located in the area.

  For example, the acquisition unit 131 may acquire various types of information from the open data providing device 50. For example, the acquisition unit 131 may acquire various types of information from various external information processing devices such as a server managed by the Japan Meteorological Agency. For example, the acquisition unit 131 may acquire various types of information from the insurance service device 20. For example, the acquisition unit 131 may acquire various types of information from various external information processing apparatuses such as servers used by each insurance company. For example, the acquisition unit 131 may acquire various types of information from various external information processing apparatuses such as servers used by the service provider.

  For example, the acquisition unit 131 acquires various information from the open data OD1, the service data SD1, the insurance data AD1, and the open data OD2, the service data SD2, the insurance data AD2, and the like as shown in FIG. Also good.

(Extractor 132)
The extraction unit 132 extracts various information. For example, the extraction unit 132 extracts various information from the storage unit 120. The extraction unit 132 includes a user information storage unit 121, an asset information storage unit 122, an insurance information storage unit 123, a disaster history information storage unit 124, a prediction model information storage unit 125, an occurrence information storage unit 126, and the like. Extract various information from.

  In the example of FIG. 1, the extraction unit 132 extracts users located in area A and area B, which are target areas of typhoon Z. In the example of FIG. 1, the extraction unit 132 extracts users located in the areas A and B based on the position information of each user stored in the user information storage unit 121.

  In the example of FIG. 1, the extraction unit 132 extracts the user U1, the user U2, the user U4, and the like who are users located in the area A or the area B. In the example of FIG. 1, the extraction unit 132 does not extract the user U3, the user U5, and the like that are located in an area other than the area A or the area B.

  In the example of FIG. 1, the extraction unit 132 extracts assets located in areas A and B, which are target areas of typhoon Z. In the example of FIG. 1, the extraction unit 132 extracts assets located in the area A and the area B based on the location of each asset stored in the asset information storage unit 122.

  In the example of FIG. 1, the extraction unit 132 extracts the asset AS1-1, the asset AS1-2, the asset AS1-2, the asset AS2-1 of the user U2, and the like, which are assets located in the area A or the area B. In the example of FIG. 1, the extraction unit 132 does not extract the user U3, the user U5, and the like that are located in an area other than the area A or the area B. For example, the prediction device 100 does not extract the asset AS1-5 of the user U1 and the asset AS4-1 of the user U4 that are located in an area other than the area A or the area B.

  In the example of FIG. 1, the extraction unit 132 extracts insurance information that is a target of insurance payment in Typhoon Z from the insurance information of the company A that is a target company that predicts the payment amount. In the example of FIG. 1, the extraction unit 132 extracts, from the insurance information of company A stored in the insurance information storage unit 123, insurance information in which the user or asset to be insured is located in the area A and the area B. .

  In the example of FIG. 1, the extraction unit 132 extracts insurance information indicating that the asset AS1-1 of the user U1 located in the area A and the user U1 themselves are insured. In the example of FIG. 1, the extraction unit 132 extracts insurance information indicating that the user U2 himself / herself located in the area B is insured.

(Generator 133)
The generation unit 133 generates various information. For example, the generation unit 133 generates various models such as a prediction model. For example, the generation unit 133 generates prediction models M1 to M3 stored in the prediction model information storage unit 125 (see FIG. 8).

  For example, the generation unit 133 generates a prediction model based on the amount of money related to damage in a disaster that has occurred in the past (hereinafter, also referred to as “past disaster”) and target information about an object located in an area affected by the past disaster. Is generated. For example, the generation unit 133 generates a prediction model for each disaster type.

  For example, the generation unit 133 sets a prediction model for predicting the damage amount of the disaster type corresponding to the past disaster, with the damage amount in the past disaster as correct information, and information on the user and information on the asset at the time of the past disaster as input. It may be generated. For example, the generation unit 133 is data included in the open data OD1, the service data SD1, and the insurance data AD1 illustrated in FIG. 12, and the score corresponding to the damage in the past disaster is input using the data corresponding to the disaster in the past disaster as an input. So that the prediction model outputs a prediction model by learning the weight of each feature.

  Further, for example, the generation unit 133 includes the asset information stored in the asset information storage unit 122 (see FIG. 5), the insurance information stored in the insurance information storage unit 123 (see FIG. 6), and the user information storage unit 121. Of the user information stored in (see FIG. 4), the information corresponding to the disaster in the past disaster is input, and the weight of each feature is learned so that the prediction model outputs a score close to the damage amount in the past disaster. Thus, the prediction models M1 to M3 and the like stored in the prediction model information storage unit 125 (see FIG. 8) are generated.

  For example, the generation unit 133 sets the payment amount of the insurance company in the past disaster as correct information, inputs information on the user, information on assets, and information on insurance at the time of the past disaster, and insures about the disaster type corresponding to the past disaster. A prediction model for predicting the payment amount of the company may be generated. For example, the generation unit 133 is data included in the open data OD1, service data SD1, and insurance data AD1 shown in FIG. The prediction model is generated by learning the weight of each feature so that the prediction model outputs a score close to.

  Further, for example, the generation unit 133 includes the asset information stored in the asset information storage unit 122 (see FIG. 5), the insurance information stored in the insurance information storage unit 123 (see FIG. 6), and the user information storage unit 121. The weight of each feature is set so that the prediction model outputs a score close to the amount paid by the insurance company in the past disaster, taking as input the information corresponding to the disaster in the past disaster among the user information stored in (see FIG. 4). To generate prediction models M1 to M3 and the like stored in the prediction model information storage unit 125 (see FIG. 8).

  Further, for example, the generation unit 133 stores the disaster history information among the asset information stored in the asset information storage unit 122 (see FIG. 5) and the user information stored in the user information storage unit 121 (see FIG. 4). The information corresponding to one past disaster stored in the unit 124 is input, and the weight of each feature is learned so that the prediction model outputs a score close to the amount of damage in the one past disaster. May be generated. For example, the generation unit 133 stores the asset information stored in the asset information storage unit 122 (see FIG. 5) corresponding to the occurrence of typhoon A in the disaster history information storage unit 124 or the user information storage unit 121 (see FIG. 4). The information corresponding to area A is input, and the weight of each feature is set so that the prediction model outputs a score close to “X billion yen” in area A in Typhoon A. A learning model may be generated by learning.

  In the example of FIG. 12, the generation unit 133 inputs various data (information) such as open data OD1, service data SD1, insurance data AD1, etc., and makes predetermined data correct information, thereby paying for disaster type X A prediction model PM for predicting the amount is generated.

  For example, when the prediction unit PM predicts the payment amount of one insurance company (hereinafter referred to as “target company”) in the disaster type X by the prediction model PM, the generation unit 133 of the target company at the time of the disaster of the past disaster type X A prediction model PM is generated using the payment amount as correct information. In this case, the extraction unit 132 extracts the payment amount of the target company at the time of the disaster of the past disaster type X from the insurance data AD1. Also, in this case, the generation unit 133 is data included in the open data OD1, the service data SD1, and the insurance data AD1. The predictive model PM is generated by learning the weight of each feature so that the predictive model PM outputs a score close to the payment amount.

  In addition, for example, when predicting the damage amount in the disaster type X using the prediction model, the generation unit 133 generates a prediction model using the damage amount of the local government (area) at the time of the disaster of the past disaster type X as correct answer information. . In this case, the extraction unit 132 extracts the damage amount of the local government (area) at the time of the disaster of the past disaster type X from the open data OD1. In this case, the generation unit 133 is data included in the open data OD1, the service data SD1, and the insurance data AD1, and receives the data corresponding to the disaster of the past disaster type X as the input, and the local government ( A prediction model is generated by learning the weight of each feature so that the prediction model outputs a score close to the amount of damage in (area).

  Note that when the prediction device 100 acquires the prediction models M1 to M3 and the like from an external information processing device, the prediction device 100 may not include the generation unit 133.

(Prediction unit 134)
The prediction unit 134 predicts various information. For example, the prediction unit 134 predicts various information using various models generated by the generation unit 133. For example, the prediction unit 134 predicts the amount of damage in a disaster using the prediction models M1 to M3 stored in the prediction model information storage unit 125.

  For example, the prediction unit 134 predicts the amount of damage related to the disaster based on the occurrence information acquired by the acquisition unit 131 and the target information. For example, the prediction unit 134 uses the prediction model generated based on the amount related to damage in the past disaster that has occurred in the past and the target information related to the object located in the area affected by the past disaster, Estimate the amount of damage. For example, the prediction unit 134 predicts a damage amount related to a disaster using a prediction model corresponding to a disaster type to be predicted among a plurality of prediction models generated for each disaster type.

  For example, the prediction unit 134 predicts the amount of payment paid by the insurance company in response to the disaster as the amount of damage based on the insurance information. For example, the prediction unit 134 predicts a payment amount paid by an insurance company due to a disaster as a damage amount by specifying a predetermined insurance target based on the insurance information.

  In the example of FIG. 1, the prediction unit 134 predicts the payment amount of the company A by setting the target areas of the typhoon Z as areas A and B. In the example of FIG. 1, the prediction unit 134 predicts the payment amount of the company A by the typhoon Z using the prediction model M1. In the example of FIG. 1, the prediction unit 134 predicts the payment amount of the company A by the typhoon Z by inputting information about the user, information about assets, and information about insurance extracted by the extraction unit 132 into the prediction model M1. To do. In the example of FIG. 1, the prediction unit 134 inputs the information about the user, the information about the asset, and the information about the insurance into the prediction model M1, and thereby the score output by the prediction model M1 is the amount paid by the company A by typhoon Z To predict. In the example of FIG. 1, the prediction unit 134 predicts the payment amount of the company A in the typhoon Z as “P billion yen” as shown in the prediction information PL11.

(Providing unit 135)
The providing unit 135 provides various types of information to an external device such as the terminal device 10. For example, the providing unit 135 may provide various types of information generated by the generating unit 133 to an external device. Further, the providing unit 135 may provide various types of prediction information predicted by the prediction unit 134 to an external device. For example, the providing unit 135 may provide information regarding the amount of damage predicted by the prediction unit 134 to an external information processing apparatus. For example, the providing unit 135 may provide the insurance service device 20 of the insurance company with the amount paid by the insurance company predicted by the prediction unit 134.

[3. (Prediction model generation process flow)
Next, the procedure of the prediction process by the prediction system 1 according to the embodiment will be described with reference to FIG. FIG. 10 is a flowchart illustrating an example of generation of a prediction model according to the embodiment. Specifically, FIG. 10 is a flowchart illustrating an example of generation of a prediction model related to generation of a prediction model.

  As illustrated in FIG. 10, the prediction device 100 acquires past disaster history information (step S101). And the prediction apparatus 100 produces | generates the prediction model corresponding to each disaster classification based on the disaster historical information for every disaster classification (step S102).

[4. (Prediction process flow)
Next, the procedure of the prediction process by the prediction system 1 according to the embodiment will be described with reference to FIG. FIG. 11 is a flowchart illustrating an example of the prediction process according to the embodiment. Specifically, FIG. 11 is a flowchart illustrating an example of a prediction process using a prediction model.

  As illustrated in FIG. 11, when a predetermined disaster occurs (Step S201: Yes), the prediction device 100 extracts a target area affected by the disaster (Step S202). Moreover, the prediction apparatus 100 repeats the process of step S201, when the predetermined disaster has not occurred (step S201: No).

  Then, the prediction device 100 extracts users and assets located in the target area (step S203). Moreover, the prediction apparatus 100 extracts the insurance for which the user and the asset located in the target area are contract targets from the insurance of the insurance company to be predicted (step S204).

  Moreover, the prediction apparatus 100 determines the prediction model used according to a disaster (step S205). And the prediction apparatus 100 estimates the payment amount of the insurance company of a prediction object using a prediction model (step S206).

[5. (Model generation, damage amount prediction)
Here, data used for generation of a prediction model and prediction of a damage amount in the prediction system 1 will be described with reference to FIGS. 12 and 13. FIG. 12 is a conceptual diagram illustrating an example of generation of a prediction model according to the embodiment. FIG. 13 is a conceptual diagram illustrating an example of a prediction process according to the embodiment.

[5-1. (Model generation)
First, generation of a prediction model by the prediction device 100 will be described with reference to FIG. In FIG. 12, generation of a prediction model PM that predicts the damage amount (payment amount) of the disaster type X is shown as an example. For example, the disaster type X may be the disaster type “earthquake” or the disaster type “train accident”, or the disaster type “whole”, that is, all disasters.

  As illustrated in FIG. 12, the prediction device 100 generates a prediction model PM that predicts the payment amount of the disaster type X using various data (information) such as open data OD1, service data SD1, and insurance data AD1. (Step S51).

  For example, the prediction device 100 acquires the open data OD1 that is data that is allowed to be used widely. For example, the prediction device 100 may use as the open data OD1, various information such as information on weather and disaster that can be acquired from the Japan Meteorological Agency, information on past disasters that can be acquired from local governments, information on points where accidents, etc. occurred get. For example, the prediction device 100 acquires the open data OD1 from various external information processing devices such as a server managed by the Japan Meteorological Agency (for example, the open data providing device 50 in FIG. 2). Note that the above is an example, and the prediction apparatus 100 may acquire any data as the open data OD1 as long as the data can be used for disaster prediction and can be acquired.

  For example, the prediction device 100 acquires service data SD1 that is data collected in a service provided by a predetermined service provider. For example, the prediction device 100 acquires various information such as a registration point of each user, a user's action log and position information at the time of past disaster, from the service provider that uses the position information as the service data SD1. For example, the prediction device 100 acquires the service data SD1 from various external information processing devices such as a server used by the service provider. Note that the above is an example, and the prediction apparatus 100 may acquire any data as the service data SD1 as long as the data can be used for disaster prediction and can be acquired.

  For example, the prediction apparatus 100 acquires insurance data AD1 that is data collected in an insurance service provided by each insurance company. For example, the prediction apparatus 100 acquires various information such as the payment amount in the past disaster and the contract contents in the past disaster as the insurance data AD1. For example, the prediction device 100 acquires the insurance data AD1 from various external information processing devices (for example, the insurance service device 20 in FIG. 2) such as a server used by each insurance company. Note that the above is an example, and the prediction apparatus 100 may acquire any data as insurance data AD1 as long as the data can be used for disaster prediction and can be acquired.

  In the example of FIG. 12, the prediction device 100 inputs various data (information) such as the open data OD1, the service data SD1, and the insurance data AD1 as described above, and makes the predetermined data correct information, thereby causing a disaster. A prediction model PM that predicts the payment amount of type X is generated.

  For example, when predicting the payment amount of one insurance company in the disaster type X (hereinafter referred to as “target company”) using the prediction model PM, the prediction apparatus 100 predicts the target company at the time of the disaster of the past disaster type X. A prediction model PM is generated using the payment amount as correct information. In this case, the prediction device 100 extracts the payment amount of the target company at the time of the disaster of the past disaster type X from the insurance data AD1. Also, in this case, the prediction device 100 is the target company that is the correct answer information that is data included in the open data OD1, the service data SD1, and the insurance data AD1 and that corresponds to the past disaster type X disaster. The predictive model PM is generated by learning the weight of each feature so that the predictive model PM outputs a score close to the payment amount.

  For example, when predicting the amount of damage in the disaster type X using the prediction model, the prediction device 100 generates a prediction model using the amount of damage in the local government (area) at the time of the disaster of the past disaster type X as correct answer information. . In this case, the prediction device 100 extracts the damage amount of the local government (area) at the time of the disaster of the past disaster type X from the open data OD1. Further, in this case, the prediction device 100 is data included in the open data OD1, the service data SD1, and the insurance data AD1, and receives the data corresponding to the disaster of the past disaster type X as input, and the local government ( A prediction model is generated by learning the weight of each feature so that the prediction model outputs a score close to the amount of damage in (area).

  Note that the above-described prediction model generation method is an example, and the prediction apparatus 100 may generate the prediction model by any method.

[5-2. (Payment forecast)
Next, the prediction of the amount of damage by the prediction device 100 will be described using FIG. In FIG. 13, the prediction of the damage amount (payment amount) of the disaster type X using the prediction model PM is shown as an example. FIG. 13 illustrates a case where the prediction device 100 predicts the damage amount (payment amount) of the disaster XXX using the prediction model PM when the disaster XXX that is the disaster type X occurs. Specifically, the case where the prediction apparatus 100 predicts the insurance premium (payment amount) paid by the company X, which is an insurance company, when the disaster XXX of the disaster type X occurs is shown. Therefore, as shown in FIG. 12, the prediction device 100 uses a prediction model PM in which the payment amount of the target company at the time of disaster of the past disaster type X is generated as correct answer information.

  As illustrated in FIG. 13, the prediction device 100 inputs various data (information) such as open data OD2, service data SD2, and insurance data AD2 to the prediction model PM (step S61). Thereby, the prediction apparatus 100 predicts the payment amount of the company X in the disaster XXX (step S61).

  For example, the prediction device 100 acquires open data OD2 related to the target area of the disaster XXX. For example, the prediction device 100 acquires various types of information such as weather and disaster information related to the target area of the disaster XXX that can be acquired from the Japan Meteorological Agency as the open data OD2. For example, the prediction device 100 acquires the open data OD2 from various external information processing devices such as a server managed by the Japan Meteorological Agency (for example, the open data providing device 50 in FIG. 2). Note that the above is an example, and the prediction device 100 may acquire any data as the open data OD2 as long as the data can be used to predict the damage amount of the disaster XXX and can be acquired.

  For example, the prediction device 100 acquires service data SD2 that is data collected in a service provided by a predetermined service provider. For example, the prediction device 100 acquires various information such as a registration point of each user at the time of occurrence of the disaster XXX and an action log and position information of the user at the time of occurrence of the disaster XXX as the service data SD2. For example, the prediction device 100 acquires the service data SD2 from various external information processing devices such as a server used by the service provider. Note that the above is an example, and the prediction apparatus 100 may acquire any data as the service data SD2 as long as the data can be used to predict the damage amount of the disaster XXX and can be acquired.

  For example, the prediction apparatus 100 acquires insurance data AD2 that is data collected in an insurance service provided by company X, which is an insurance company. For example, the prediction device 100 acquires various types of information such as contract details of the target area of the disaster XXX as the insurance data AD2. For example, the prediction apparatus 100 acquires various types of information such as information on users and assets that are targets of insurance provided by Company X and are located in the target area of the disaster XXX as insurance data AD2. For example, the prediction device 100 acquires the insurance data AD2 from various external information processing devices (for example, the insurance service device 20 in FIG. 2) such as a server used by each insurance company. Note that the above is an example, and the prediction apparatus 100 may acquire any data as insurance data AD2 as long as it can be used to predict the damage amount of the disaster XXX and can be acquired.

  In the example of FIG. 13, the prediction device 100 inputs various data (information) such as the above-described open data OD2, service data SD2, and insurance data AD2 to the prediction model PM, so that the company X in the disaster XXX Estimate the payment amount. In the example of FIG. 13, the prediction device 100 predicts the payment amount of the company X in the disaster XXX as “W billion yen” as shown in the prediction information PL61.

[6. effect〕
As described above, the prediction device 100 according to the embodiment includes the acquisition unit 131 and the prediction unit 134. The acquisition unit 131 acquires occurrence information related to a disaster and target information related to a target located in an area affected by the disaster. The prediction unit 134 predicts the amount of damage related to the disaster based on the occurrence information and target information acquired by the acquisition unit 131.

  Accordingly, the prediction apparatus 100 according to the embodiment appropriately predicts the damage amount related to the disaster by predicting the damage amount related to the disaster based on the occurrence information and the target information based on the asset information of the user. Can do.

  Further, in the prediction device 100 according to the embodiment, the acquisition unit 131 acquires target information including asset information relating to assets owned by the user and located in an area affected by the occurrence of a disaster.

  Thereby, the prediction apparatus 100 according to the embodiment acquires the target information including the asset information regarding the asset that is owned by the user and located in the area that is affected by the occurrence of the disaster, so that the damage amount related to the disaster Can be appropriately predicted.

  Moreover, in the prediction device 100 according to the embodiment, the acquisition unit 131 acquires target information including position information regarding a user in the occurrence of a disaster.

  Thereby, the prediction apparatus 100 which concerns on embodiment can predict the damage amount regarding a disaster appropriately by acquiring the target information containing the positional information regarding the user in the occurrence of a disaster.

  Further, in the prediction device 100 according to the embodiment, the prediction unit 134 is generated based on the amount related to the damage in the past disaster that occurred in the past and the target information about the target located in the area affected by the past disaster. Predict the amount of damage related to disasters.

  Thereby, the prediction apparatus 100 according to the embodiment generates a prediction model generated based on the amount related to damage in the past disaster that has occurred in the past and the target information related to the object located in the area affected by the past disaster. It is possible to appropriately predict the damage amount related to the disaster by predicting the damage amount related to the disaster.

  In the prediction device 100 according to the embodiment, the prediction unit 134 uses a prediction model corresponding to a disaster type to be predicted among a plurality of prediction models generated for each disaster type, and relates to a disaster. Estimate the amount of damage.

  Thereby, the prediction apparatus 100 according to the embodiment predicts a damage amount related to a disaster using a prediction model corresponding to a disaster type to be predicted among a plurality of prediction models generated for each type of disaster. By doing so, it is possible to appropriately predict the amount of damage related to the disaster.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires occurrence information related to a disaster that is one of an earthquake, a typhoon, or a traffic disaster.

  Thereby, the prediction apparatus 100 which concerns on embodiment can predict the damage amount regarding a disaster appropriately by acquiring the occurrence information regarding the disaster which is either an earthquake, a typhoon, or a traffic disaster.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires insurance information related to an insurance company. Based on the insurance information, the prediction unit 134 predicts a payment amount paid by the insurance company as a damage amount in response to a disaster.

  Thereby, the prediction apparatus 100 which concerns on embodiment can predict the damage amount regarding a disaster appropriately by estimating the payment amount which an insurance company pays according to a disaster as a damage amount based on insurance information.

  Moreover, in the prediction device 100 according to the embodiment, the acquisition unit 131 acquires insurance information of each user who is a user who joins a predetermined insurance provided by an insurance company and is related to an object located in the area. The prediction unit 134 predicts a payment amount paid by an insurance company due to a disaster as a damage amount by specifying a predetermined insurance target based on the insurance information.

  Thereby, the prediction device 100 according to the embodiment predicts a payment amount paid by an insurance company due to a disaster as a damage amount by specifying a predetermined insurance target based on the insurance information, thereby calculating a damage amount related to the disaster. Can be predicted appropriately.

[7. Hardware configuration)
The prediction apparatus 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in FIG. 15, for example. FIG. 15 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the prediction device. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD (Hard Disk Drive) 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from other devices via the network N and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the network N.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. In addition, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the prediction device 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them. However, as another example, these programs may be acquired from other devices via the network N.

  As described above, some of the embodiments and modifications of the present application have been described in detail with reference to the drawings. It is possible to carry out the present invention in other forms that have been modified and improved.

[8. Others]
In addition, among the processes described in the above-described embodiments and modifications, all or a part of the processes described as being automatically performed can be manually performed, or are described as being performed manually. All or part of the processing can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  In addition, the above-described embodiments and modifications can be combined as appropriate within a range that does not contradict processing contents.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

DESCRIPTION OF SYMBOLS 1 Prediction system 100 Prediction apparatus 121 User information storage part 122 Asset information storage part 123 Insurance information storage part 124 Disaster history information storage part 125 Prediction model information storage part 126 Occurrence information storage part 130 Control part 131 Acquisition part 132 Extraction part 133 Generation part 134 prediction unit 135 providing unit 10 terminal device 20 insurance service device 50 open data providing device N network

Claims (15)

An acquisition unit that acquires occurrence information related to a disaster, target information related to an object located in an area affected by the disaster, and a correspondence table in which a damage amount is associated with a score;
Based on the occurrence information acquired by the acquisition unit, the score calculated by the target information, and the correspondence table, a prediction unit that predicts a damage amount related to the disaster;
A prediction apparatus comprising: The acquisition unit
Obtaining the target information including information on the user corresponding to the area, information on assets, and information on insurance, and a prediction model;
The prediction unit
Based on the occurrence information and the target information acquired by the acquisition unit, the prediction model outputs information about the user, information about the asset, and information about the insurance into the prediction model. The prediction apparatus according to claim 1, wherein the damage amount related to the disaster is predicted using a score and a correspondence table in which the damage amount is associated with the score. The acquisition unit
The prediction apparatus according to claim 1, wherein the target information including asset information relating to assets owned by a user and located in an area affected by the occurrence of the disaster is acquired. . The acquisition unit
The prediction apparatus according to claim 1, wherein the target information including position information related to a user in the occurrence of the disaster is acquired. The prediction unit
Predict the amount of damage related to the disaster using a prediction model that is generated based on the amount of damage related to the past disaster that occurred in the past and target information related to objects located in the area affected by the past disaster. The prediction device according to any one of claims 1 to 4, wherein: The prediction unit
6. The amount of damage related to the disaster is predicted using a prediction model corresponding to the type of disaster to be predicted among a plurality of prediction models generated for each type of disaster. Prediction device. The acquisition unit
The prediction apparatus according to any one of claims 1 to 6, wherein occurrence information related to the disaster that is one of an earthquake, a typhoon, or a traffic disaster is acquired. The acquisition unit
Get insurance information about insurance companies,
The prediction unit
The prediction device according to any one of claims 1 to 7, wherein a payment amount paid by the insurance company in accordance with the disaster is predicted as the amount of damage based on the insurance information. The acquisition unit
Each user who subscribes to a predetermined insurance provided by the insurance company, and obtains the insurance information of each user related to an object located in the area,
The prediction unit
The prediction device according to claim 8, wherein a payment amount paid by the insurance company due to the disaster is predicted as the damage amount by specifying a target of the predetermined insurance based on the insurance information. A prediction method performed by a computer,
An acquisition step of acquiring occurrence information related to a disaster, target information related to an object located in an area affected by the disaster, and a correspondence table in which a damage amount is associated with a score;
A prediction step of predicting a damage amount related to the disaster based on the occurrence information acquired by the acquisition step, the score calculated by the target information, and the correspondence table;
The prediction method characterized by including. An acquisition procedure for acquiring occurrence information related to a disaster, target information related to an object located in an area affected by the disaster, and a correspondence table in which a damage amount is associated with a score;
A prediction procedure for predicting a damage amount related to the disaster based on the occurrence information acquired by the acquisition procedure, the score calculated by the target information, and the correspondence table;
A prediction program that causes a computer to execute. An acquisition unit for acquiring occurrence information related to a disaster, target information related to an object located in an area affected by the disaster, and correct information indicating a damage amount in the disaster;
Based on the occurrence information acquired by the acquisition unit, the target information, and the correct answer information, a generation unit that generates a model for predicting a damage amount related to a disaster,
An information processing apparatus comprising: The acquisition unit
The information processing apparatus according to claim 12, wherein the target information includes information about a user corresponding to an area, information about an asset, and information about insurance. An information processing method executed by a computer,
An acquisition step of acquiring occurrence information related to a disaster, target information related to an object located in an area affected by the disaster, and correct information indicating a damage amount in the disaster,
Based on the occurrence information acquired by the acquisition step, the target information, and the correct answer information, a generation step of generating a model for predicting a damage amount related to a disaster,
An information processing method comprising: An acquisition procedure for acquiring occurrence information related to a disaster, target information related to an object located in an area affected by the disaster, and correct information indicating a damage amount in the disaster,
A generation procedure for generating a model for predicting a damage amount related to a disaster based on the occurrence information acquired by the acquisition procedure, the target information, and the correct answer information;
An information processing program for causing a computer to execute.

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