WO2022259294A1 - Early damage prediction device, early damage prediction method, and early damage prediction program - Google Patents

Early damage prediction device, early damage prediction method, and early damage prediction program Download PDF

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Publication number
WO2022259294A1
WO2022259294A1 PCT/JP2021/021526 JP2021021526W WO2022259294A1 WO 2022259294 A1 WO2022259294 A1 WO 2022259294A1 JP 2021021526 W JP2021021526 W JP 2021021526W WO 2022259294 A1 WO2022259294 A1 WO 2022259294A1
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failure
damage prediction
degree
section
equipment
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PCT/JP2021/021526
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French (fr)
Japanese (ja)
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晃 小山
浩史 松原
尚子 小阪
恒子 倉
潤 加藤
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日本電信電話株式会社
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Priority to PCT/JP2021/021526 priority Critical patent/WO2022259294A1/en
Priority to JP2023527138A priority patent/JPWO2022259294A1/ja
Publication of WO2022259294A1 publication Critical patent/WO2022259294A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism

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  • the present invention relates to an early damage prediction device, an early damage prediction method, and an early damage prediction program.
  • Non-Patent Document 1 In order to prepare for typhoon damage, the Japan Meteorological Agency provides typhoon track and intensity forecasts five days in advance (see Non-Patent Document 1).
  • the present invention has been made in view of the above circumstances, and the purpose of the present invention is to provide a technology that can predict the damage caused by future typhoons from the failure status of equipment caused by past typhoons.
  • An early damage prediction device is an early damage prediction device that predicts damage caused by a future typhoon based on failure conditions of facilities caused by past typhoons, wherein the degrees of failure of facilities caused by past typhoons are displayed in fixed units on a map.
  • a calculation unit that calculates for each section and for each time, learns the relationship between the degree of failure of equipment for each section and for each time, and the intensity of the past typhoon
  • a generation unit that generates a damage prediction learning model capable of predicting the degree of equipment failure, and using the damage prediction learning model, predicts and calculates the degree of equipment failure for each section and time based on the path and intensity forecast of the generated typhoon. and a determination unit that determines the degree of damage caused by the generated typhoon in each section and at each time by using the degree of failure of the equipment for each section and each time and the area information for each section.
  • An early damage prediction method of one aspect of the present invention is an early damage prediction method for predicting damage caused by a future typhoon based on failure conditions of facilities caused by past typhoons, wherein an early damage prediction device estimates the degree of failure of facilities caused by past typhoons.
  • a step of calculating for each section of a fixed unit on the map and for each time, learning the relationship between the degree of failure of the equipment for each section and for each time and the strength of the past typhoon, and calculating the section from the typhoon course and strength forecast a step of generating a damage prediction learning model capable of predicting the degree of failure of equipment for each period and time; and determining the degree of damage caused by the generated typhoon in each section and at each time by using the failure rate of the equipment for each section and each time and the area information for each section. conduct.
  • the early damage prediction program of one aspect of the present invention causes a computer to function as the early damage prediction device.
  • FIG. 1 is a diagram showing the overall configuration of an early damage prediction system.
  • FIG. 2 is a diagram showing a processing flow of the early damage prediction device.
  • FIG. 3 is a diagram showing an example of a correlation graph.
  • FIG. 4 is a diagram showing a display example of the degree of damage.
  • FIG. 5 is a diagram showing the hardware configuration of the early damage prediction device.
  • the present invention discloses a technology capable of predicting damage caused by future typhoons based on failure conditions of facilities caused by past typhoons. Specifically, based on typhoon failure information on equipment possessed by local governments, infrastructure companies, general companies, etc., the degree of damage to equipment due to typhoons that will land in the future is predicted, and the degree of damage to each region is calculated from the degree of damage to the equipment. judge.
  • the failure rate of infrastructure such as communication lines and power lines due to past typhoons is accumulated in time series for each section of a certain unit on the map, and the degree of failure of infrastructure in each section, the strength of the typhoon, the number of power outages, and the amount of rainfall.
  • Generate a damage prediction learning model that has learned the relationship between Then, the course and intensity forecast of the generated typhoon are input to the damage prediction learning model to predict the degree of damage to the infrastructure, and the degree of damage in each area is determined in consideration of area information such as population. This will enable preemptive crisis response.
  • FIG. 1 is a diagram showing the overall configuration of an early damage prediction system.
  • the early damage prediction system 1 includes an early damage prediction device 10 for early prediction of typhoon damage, and a plurality of client terminals 20 for processing damage prediction results predicted by the early damage prediction device 10 .
  • the early damage prediction device 10 and the plurality of client terminals 20 are physically and electrically connected via a communication network 30 so as to be mutually communicable.
  • the client terminal 20 is a client device used by a user. Users are, for example, employees of municipalities, employees of infrastructure companies and general companies.
  • the client terminal 20 has a function of transmitting to the early damage prediction apparatus 10 various data necessary for creating a typhoon damage prediction result according to an input command input by a user.
  • the client terminal 20 also has a function of receiving damage prediction results predicted by the early damage prediction device 10, displaying the damage prediction results on a display device, printing them on a printing device, and transferring them to other devices.
  • the early damage prediction device 10 is a server device that predicts regional damage caused by future typhoons based on equipment failure conditions caused by past typhoons.
  • Facilities are, for example, communication facilities and power facilities.
  • the early damage prediction device 10 may function as one device of a Geographic Information System (GIS) that creates, stores, uses, manages, displays, and searches geographic information and additional information on a computer.
  • GIS Geographic Information System
  • the early damage prediction apparatus 10 includes a failure information storage unit 111, a map information storage unit 112, and a weather data storage unit as storage units for storing information or data for creating damage prediction results due to a typhoon.
  • An information storage unit 113 , a power failure information storage unit 114 , and an area information storage unit 115 are provided.
  • the failure information storage unit 111 has a function of storing facility failure information.
  • Equipment failure information is, for example, the failure rate of communication lines and the failure rate of power lines.
  • the map information storage unit 112 has a function of storing digital map data of all over Japan.
  • the weather information storage unit 113 has a function of storing weather data provided by the Japan Meteorological Agency, weather companies, and the like.
  • Meteorological data is, for example, the intensity (wind speed) of a typhoon that occurred at a given latitude and longitude on a given date and time, the magnitude (radius) of the typhoon, the speed of the typhoon, the path of the typhoon (latitude and longitude), and the amount of rainfall caused by the typhoon.
  • the power outage information storage unit 114 has a function of storing power outage data provided by the power company.
  • the power failure data is, for example, the date and time when the power failure occurred, the address, and the date and time when the power failure was restored, and the address.
  • the area information storage unit 115 has a function of storing area information.
  • the area information is, for example, the population in the area, the number of buildings (the number of houses, etc.) in the area, and the hazard map of the area.
  • the early damage prediction device 10 includes a failure information generation unit 116, an algorithm generation unit 117, and a damage prediction analysis unit 118 as control units for creating damage prediction results due to typhoons. , a map generation unit 119 and a damage degree determination unit 120 .
  • the failure information generation unit (calculation unit) 116 has a function of calculating the degree of failure of equipment due to past typhoons for each section of a fixed unit on the map and for each time.
  • the algorithm generation unit (generation unit) 117 learns the relationship between the degree of equipment failure for each section and for each time, the intensity of past typhoons, the number of power outages caused by past typhoons, and the amount of rainfall caused by past typhoons. It has a function to generate a damage prediction learning model that can predict the degree of equipment failure for each section and time from the course and intensity forecast.
  • the damage prediction analysis unit (analysis unit) 118 has a function of predicting and calculating the degree of equipment failure for each section and time from the course and strength forecast of the generated typhoon using the damage prediction learning model.
  • the map generation unit 119 has a function of displaying the predicted and calculated degree of failure of equipment in each section in an area within the section, and further displaying area information of each area.
  • the area information is, for example, the population in the area, the number of buildings (the number of houses, etc.) in the area, and the hazard map of the area.
  • the map generation unit 119 has a function of displaying each area by color-coding according to the degree of equipment failure and the degree of damage in the area.
  • a damage degree determination unit (determination unit) 120 determines the degree of damage caused by the generated typhoon in each section and at each time using the predicted and calculated equipment failure degree for each section and time and area information for each section. It has a function to (determine, calculate).
  • FIG. 2 is a diagram showing a processing flow of the early damage prediction device.
  • the early damage prediction device 10 performs learning processing for learning damage prediction of facilities, prediction processing for predicting the degree of failure of facilities from the course and intensity forecast of the generated typhoon, and determining the degree of damage to the area from the degree of failure of the facility. and run
  • Step S1; Step S1 is a learning process.
  • the failure information generation unit 116 uses communication line information in which the communication line ID, position, etc. are set, and communication line failure information in which the date and time of the communication line failure, the ID of the failed communication line, etc. are set. , Calculate the failure rate of communication lines caused by past typhoons in time series for each fixed unit block on the map. For example, the failure information generation unit 116 divides the number of failures of communication lines in each area, which is obtained by dividing the area on the map into meshes in units of 10 km, by the total number of communication lines in the same area. Calculate the failure rate of communication lines in the area. By calculating the failure rate of the communication line for each area of a certain distance, the secrecy of the communication line information and the communication line failure information, which are generally treated as private information, can be maintained.
  • Step S2; Step S2 is also a learning process.
  • the early damage prediction device 10 provides each area with information on power outages due to past typhoons (whether there were power outages, the number of power outages, etc.) as equipment failure information. In addition, the early damage prediction device 10 provides each area with the intensity of a typhoon and the amount of precipitation due to the typhoon as past typhoon information. After that, the algorithm generation unit 117 learns the relationship between the failure rate of the communication line, the strength of the typhoon, the number of power outages due to the typhoon, and the amount of precipitation due to the typhoon for each mesh area of 10 km units and for each time series.
  • the damage prediction learning model is based on "the failure rate of the communication line for each 10 km unit mesh area", “the past typhoon data for each 10 km unit mesh area”, and “the 10 km unit mesh area”. It is a learning model that multiplies past power outage data for each area.
  • the algorithm generation unit 117 creates teacher data from past typhoon wind speed data, rainfall data, power outage data, and communication line failure rates, and can predict communication line failure rates from the teacher data. Generate a damage prediction learning model. For example, the algorithm generation unit 117 creates CSV data having the date and time for each area, the maximum wind speed for each date and time, rainfall, power failure information, and communication line failure rate, and the communication line failure rate, power failure number, and rainfall rate for the maximum wind speed. Generate a correlation graph of An example of a correlation graph between maximum wind speed and communication line failure rate is shown in FIG. Note that the algorithm generation unit 117 may generate the correlation graph by deleting data that is not affected by the typhoon (maximum wind speed of 15 m/s or less, etc.) from the CSV data.
  • Step S3; Step S3 is a prediction process.
  • the damage prediction analysis unit 118 inputs the position data and wind speed data of the generated typhoon into the damage prediction learning model, and causes the damage prediction learning model to perform program processing, so that each area corresponding to the position and wind speed of the typhoon. Predict and calculate the failure rate of communication lines. For example, the damage prediction analysis unit 118 calculates the failure rate of the communication line corresponding to the maximum wind speed from the correlation graph shown in FIG.
  • Step S4; Step S4 is also prediction processing.
  • the map generation unit 119 displays the predicted and calculated failure rate of the communication line in each area, and displays each area by color-coding according to the failure rate of the communication line.
  • the map generator 119 also mashes up the population in the area, the house data in the area, the building data in the area, and the hazard map of the area into each area.
  • Step S5; Step S5 is also prediction processing.
  • the damage degree determination unit 120 uses the predicted and calculated failure degree of the communication line in each area, the population in the area, the house data in the area, the building data in the area, and the hazard map of the area to determine the degree of damage caused by the typhoon. Determine the degree of damage in each area in chronological order.
  • the degree of damage is defined as "predicted value of communication line failure rate for each mesh area of 10 km unit", “population data for each mesh area of 10 km unit", and “mesh area of 10 km unit”. "house data for each area”, “building data for each mesh area in units of 10 km”, and “hazard map for each mesh area in units of 10 km”.
  • the damage degree determination unit 120 can calculate the degree of damage using any information or data used in the previous calculations, either alone or in any combination. For example, the damage level determination unit 120 first determines the failure risk level f1 of the communication line within the range of level 1 to level 3 according to the failure rate of the communication line predicted and calculated in step S3. set for each Further, the damage degree determination unit 120 sets the power outage risk f2 within the range of level 1 to level 3 for each area according to the number of power outages of the typhoon predicted from the correlation graph between the maximum wind speed and the number of power outages. Then, the damage degree determination unit 120 incorporates the population data, house data, and hazard map of the national census into the map, and estimates human damage and house damage, thereby determining the degree of damage in each area.
  • the map generation unit 119 displays each area by color-coding according to the degree of damage.
  • FIG. 4 shows a display example of the degree of damage in each area.
  • the early damage prediction device 10 includes the failure information generation unit 116 that calculates the degree of failure of equipment due to past typhoons for each section of a fixed unit on the map and for each time, and for each section and each time.
  • Algorithm generation unit that learns the relationship between the degree of equipment failure and the strength of the past typhoon, and generates a damage prediction learning model that can predict the degree of equipment failure for each section and time from the course and intensity forecast of the typhoon 117, a damage prediction analysis unit 118 that predicts and calculates the degree of failure of equipment for each section and for each time from the course and intensity forecast of the generated typhoon using the damage prediction learning model, and the equipment for each section and each time and a damage degree determination unit 120 that determines the degree of damage for each section and each time due to the generated typhoon using the failure degree and area information for each section.
  • the early damage prediction device 10 of the present embodiment described above includes, for example, a CPU 901, a memory 902, a storage 903, a communication device 904, an input device 905, and an output device 906, as shown in FIG. It can be realized using a general-purpose computer system equipped with.
  • Memory 902 and storage 903 are storage devices.
  • each function of the early damage prediction apparatus 10 is realized by executing a predetermined program loaded on the memory 902 by the CPU 901 .
  • the early damage prediction device 10 may be implemented by one computer.
  • the early damage prediction device 10 may be implemented by multiple computers.
  • the early damage prediction device 10 may be a virtual machine implemented on a computer.
  • a program for the early damage prediction device 10 can be stored in computer-readable recording media such as HDD, SSD, USB memory, CD, and DVD.
  • a program for the early damage prediction device 10 can also be distributed via a communication network.

Abstract

An early damage prediction device 10 is provided with: a failure information generation unit 116 which calculates the degrees of failure of equipment caused by past typhoons for each fixed unit parcel on a map and for each time point; an algorithm generation unit 117 which learns the relationship between the degrees of failure of equipment for each parcel and for each time point and the intensities of the past typhoons, and generates a damage prediction learning model that can predict a degree of failure of equipment for each parcel and for each time point from a forecasted path and intensity of a typhoon; a damage prediction analysis unit 118 which uses the damage prediction learning model to predict and calculate a degree of failure of equipment for each parcel and for each time point from a forecasted path and intensity of a typhoon that has occurred; and a damage degree determination unit 120 which determines, for each parcel and for each time point, the degree of damage caused by the typhoon that has occurred using the degree of failure of equipment for the parcel and for the time point, and area information for the parcel.

Description

早期被害予測装置、早期被害予測方法、及び、早期被害予測プログラムEarly damage prediction device, early damage prediction method, and early damage prediction program
　本発明は、早期被害予測装置、早期被害予測方法、及び、早期被害予測プログラムに関する。 The present invention relates to an early damage prediction device, an early damage prediction method, and an early damage prediction program.
　近年、台風による被害が増大している。気象庁は、台風による被害に備えるため、台風の進路及び強度予報を５日前から提供している（非特許文献１参照）。 In recent years, the damage caused by typhoons has increased. In order to prepare for typhoon damage, the Japan Meteorological Agency provides typhoon track and intensity forecasts five days in advance (see Non-Patent Document 1).
　しかしながら、従来は、台風の進路及び強度予報が提供されるにすぎないため、発生した台風による実被害の程度は不明である。自治体、インフラ企業、一般企業は、台風による設備の被害を上陸前に想定し、事前の対策を行うことで被害を軽減させる必要があるが、設備がどの程度の被害を受けるかが分からない。また、被害から早期復旧するために保守要員を配置しなくてはならないが、離島等の場合には移動が困難になる。その結果、台風の被害による対応が後手に回ってしまい、被害を拡大させてしまう懸念がある。 However, conventionally, only typhoon course and strength forecasts are provided, so the actual extent of damage caused by typhoons is unknown. Municipalities, infrastructure companies, and general companies need to anticipate damage to equipment from typhoons before they land and take preventive measures to mitigate damage, but they do not know how much damage will be done to equipment. In addition, maintenance personnel must be stationed in order to quickly recover from damage, but in the case of remote islands, it is difficult to move. As a result, there is concern that response to typhoon damage will be delayed and the damage will be exacerbated.
　本発明は、上記事情に鑑みてなされたものであり、本発明の目的は、過去の台風による設備の故障状況から将来の台風による被害を予測可能な技術を提供することである。 The present invention has been made in view of the above circumstances, and the purpose of the present invention is to provide a technology that can predict the damage caused by future typhoons from the failure status of equipment caused by past typhoons.
　本発明の一態様の早期被害予測装置は、過去の台風による設備の故障状況から将来の台風による被害を予測する早期被害予測装置において、過去の台風による設備の故障度を地図上の一定単位の区画毎及び時刻毎に計算する計算部と、前記区画毎及び時刻毎の設備の故障度と前記過去の台風の強度との関係を学習し、台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測可能な被害予測学習モデルを生成する生成部と、前記被害予測学習モデルを用いて、発生した台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測計算する解析部と、当該区画毎及び時刻毎の設備の故障度と区画毎のエリア情報とを用いて、前記発生した台風による各区画及び各時刻の被害度を判定する判定部と、を備える。 An early damage prediction device according to one aspect of the present invention is an early damage prediction device that predicts damage caused by a future typhoon based on failure conditions of facilities caused by past typhoons, wherein the degrees of failure of facilities caused by past typhoons are displayed in fixed units on a map. A calculation unit that calculates for each section and for each time, learns the relationship between the degree of failure of equipment for each section and for each time, and the intensity of the past typhoon, A generation unit that generates a damage prediction learning model capable of predicting the degree of equipment failure, and using the damage prediction learning model, predicts and calculates the degree of equipment failure for each section and time based on the path and intensity forecast of the generated typhoon. and a determination unit that determines the degree of damage caused by the generated typhoon in each section and at each time by using the degree of failure of the equipment for each section and each time and the area information for each section.
　本発明の一態様の早期被害予測方法は、過去の台風による設備の故障状況から将来の台風による被害を予測する早期被害予測方法において、早期被害予測装置が、過去の台風による設備の故障度を地図上の一定単位の区画毎及び時刻毎に計算するステップと、前記区画毎及び時刻毎の設備の故障度と前記過去の台風の強度との関係を学習し、台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測可能な被害予測学習モデルを生成するステップと、前記被害予測学習モデルを用いて、発生した台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測計算するステップと、当該区画毎及び時刻毎の設備の故障度と区画毎のエリア情報とを用いて、前記発生した台風による各区画及び各時刻の被害度を判定するステップと、を行う。 An early damage prediction method of one aspect of the present invention is an early damage prediction method for predicting damage caused by a future typhoon based on failure conditions of facilities caused by past typhoons, wherein an early damage prediction device estimates the degree of failure of facilities caused by past typhoons. A step of calculating for each section of a fixed unit on the map and for each time, learning the relationship between the degree of failure of the equipment for each section and for each time and the strength of the past typhoon, and calculating the section from the typhoon course and strength forecast a step of generating a damage prediction learning model capable of predicting the degree of failure of equipment for each period and time; and determining the degree of damage caused by the generated typhoon in each section and at each time by using the failure rate of the equipment for each section and each time and the area information for each section. conduct.
　本発明の一態様の早期被害予測プログラムは、上記早期被害予測装置として、コンピュータを機能させる。 The early damage prediction program of one aspect of the present invention causes a computer to function as the early damage prediction device.
　本発明によれば、過去の台風による設備の故障状況から将来の台風による被害を予測可能な技術を提供できる。 According to the present invention, it is possible to provide a technology that can predict damage caused by future typhoons based on equipment failures caused by past typhoons.
図１は、早期被害予測システムの全体構成を示す図である。FIG. 1 is a diagram showing the overall configuration of an early damage prediction system. 図２は、早期被害予測装置の処理フローを示す図である。FIG. 2 is a diagram showing a processing flow of the early damage prediction device. 図３は、相関グラフの例を示す図である。FIG. 3 is a diagram showing an example of a correlation graph. 図４は、被害度の表示例を示す図である。FIG. 4 is a diagram showing a display example of the degree of damage. 図５は、早期被害予測装置のハードウェア構成を示す図である。FIG. 5 is a diagram showing the hardware configuration of the early damage prediction device.
　以下、図面を参照して、本発明の実施形態を説明する。図面の記載において同一部分には同一符号を付し説明を省略する。 Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the description of the drawings, the same parts are denoted by the same reference numerals, and the description thereof is omitted.
　［発明の概要］
　本発明は、過去の台風による設備の故障状況から将来の台風による被害を予測可能な技術を開示する。具体的には、自治体、インフラ企業、一般企業等が有する設備の台風による故障情報を基に、これから上陸する台風による設備の被害度を予測し、当該設備の被害度から各地域の被害度を判定する。 [Summary of Invention]
The present invention discloses a technology capable of predicting damage caused by future typhoons based on failure conditions of facilities caused by past typhoons. Specifically, based on typhoon failure information on equipment possessed by local governments, infrastructure companies, general companies, etc., the degree of damage to equipment due to typhoons that will land in the future is predicted, and the degree of damage to each region is calculated from the degree of damage to the equipment. judge.
　例えば、過去の台風による通信回線や電力線等のインフラの故障度を地図上の一定単位の区画毎に時系列で蓄積し、各区画におけるインフラの故障度と、台風の強度、停電数、降水量との関係を学習した被害予測学習モデルを生成しておく。そして、発生した台風の進路及び強度予報を被害予測学習モデルに入力してインフラの被害度を予測し、人口等のエリア情報を考慮して各地域の被害度を判定する。これにより、先手をうった危機対応が可能になる。 For example, the failure rate of infrastructure such as communication lines and power lines due to past typhoons is accumulated in time series for each section of a certain unit on the map, and the degree of failure of infrastructure in each section, the strength of the typhoon, the number of power outages, and the amount of rainfall. Generate a damage prediction learning model that has learned the relationship between Then, the course and intensity forecast of the generated typhoon are input to the damage prediction learning model to predict the degree of damage to the infrastructure, and the degree of damage in each area is determined in consideration of area information such as population. This will enable preemptive crisis response.
　［早期被害予測システムの全体構成］
　図１は、早期被害予測システムの全体構成を示す図である。早期被害予測システム１は、台風による被害を早期に予測する早期被害予測装置１０と、早期被害予測装置１０で予測された被害予測結果を処理する複数のクライアント端末２０と、を備える。早期被害予測装置１０と複数のクライアント端末２０とは、通信ネットワーク３０を介して、相互通信可能に物理的及び電気的に接続されている。 [Overall configuration of early damage prediction system]
FIG. 1 is a diagram showing the overall configuration of an early damage prediction system. The early damage prediction system 1 includes an early damage prediction device 10 for early prediction of typhoon damage, and a plurality of client terminals 20 for processing damage prediction results predicted by the early damage prediction device 10 . The early damage prediction device 10 and the plurality of client terminals 20 are physically and electrically connected via a communication network 30 so as to be mutually communicable.
　［クライアント端末の機能］
　クライアント端末２０は、ユーザが使用するクライアント装置である。ユーザとは、例えば、自治体の職員、インフラ企業及び一般企業の従業員である。クライアント端末２０は、ユーザが入力した入力命令に従い、台風による被害予測結果の作成に必要な諸データを早期被害予測装置１０へ送信する機能を備える。また、クライアント端末２０は、早期被害予測装置１０で予測された被害予測結果を受信し、当該被害予測結果を表示装置に表示し、印刷装置に印刷し、他の装置へ転送する機能を備える。 [Functions of client terminal]
The client terminal 20 is a client device used by a user. Users are, for example, employees of municipalities, employees of infrastructure companies and general companies. The client terminal 20 has a function of transmitting to the early damage prediction apparatus 10 various data necessary for creating a typhoon damage prediction result according to an input command input by a user. The client terminal 20 also has a function of receiving damage prediction results predicted by the early damage prediction device 10, displaying the damage prediction results on a display device, printing them on a printing device, and transferring them to other devices.
　［早期被害予測装置の機能］
　早期被害予測装置１０は、過去の台風による設備の故障状況から将来の台風による地域の被害を予測するサーバ装置である。設備とは、例えば、通信設備、電力設備である。早期被害予測装置１０は、地理情報及び付加情報をコンピュータ上で作成、保存、利用、管理、表示、検索する地理情報システム（GIS：Geographic Information System）の一装置として機能してもよい。 [Function of early damage prediction device]
The early damage prediction device 10 is a server device that predicts regional damage caused by future typhoons based on equipment failure conditions caused by past typhoons. Facilities are, for example, communication facilities and power facilities. The early damage prediction device 10 may function as one device of a Geographic Information System (GIS) that creates, stores, uses, manages, displays, and searches geographic information and additional information on a computer.
　図１に示したように、早期被害予測装置１０は、台風による被害予測結果を作成するための情報又はデータを記憶する記憶ユニットとして、故障情報記憶部１１１と、地図情報記憶部１１２と、気象情報記憶部１１３と、停電情報記憶部１１４と、エリア情報記憶部１１５と、を備える。 As shown in FIG. 1, the early damage prediction apparatus 10 includes a failure information storage unit 111, a map information storage unit 112, and a weather data storage unit as storage units for storing information or data for creating damage prediction results due to a typhoon. An information storage unit 113 , a power failure information storage unit 114 , and an area information storage unit 115 are provided.
　故障情報記憶部１１１は、設備の故障情報を記憶する機能を備える。設備の故障情報とは、例えば、通信回線の故障率、電力線の故障率である。 The failure information storage unit 111 has a function of storing facility failure information. Equipment failure information is, for example, the failure rate of communication lines and the failure rate of power lines.
　地図情報記憶部１１２は、日本全国のデジタル地図データを記憶する機能を備える。 The map information storage unit 112 has a function of storing digital map data of all over Japan.
　気象情報記憶部１１３は、気象庁や気象会社等から提供される気象データを記憶する機能を備える。気象データとは、例えば、所定緯度経度で所定日時に発生した台風の強度（風速）、台風の大きさ（半径）、台風の速度、台風の経路（緯度経度）、台風による降水量である。 The weather information storage unit 113 has a function of storing weather data provided by the Japan Meteorological Agency, weather companies, and the like. Meteorological data is, for example, the intensity (wind speed) of a typhoon that occurred at a given latitude and longitude on a given date and time, the magnitude (radius) of the typhoon, the speed of the typhoon, the path of the typhoon (latitude and longitude), and the amount of rainfall caused by the typhoon.
　停電情報記憶部１１４は、電力会社から提供される停電データを記憶する機能を備える。停電データとは、例えば、停電が発生した日時、住所、停電が復旧した日時、住所である。 The power outage information storage unit 114 has a function of storing power outage data provided by the power company. The power failure data is, for example, the date and time when the power failure occurred, the address, and the date and time when the power failure was restored, and the address.
　エリア情報記憶部１１５は、エリア情報を記憶する機能を備える。エリア情報とは、例えば、エリア内の人口、エリア内の建物数（家屋数等）、エリアのハザードマップである。 The area information storage unit 115 has a function of storing area information. The area information is, for example, the population in the area, the number of buildings (the number of houses, etc.) in the area, and the hazard map of the area.
　また、図１に示したように、早期被害予測装置１０は、台風による被害予測結果を作成するための制御ユニットとして、故障情報生成部１１６と、アルゴリズム生成部１１７と、被害予測解析部１１８と、地図生成部１１９と、被害度判定部１２０と、を備える。 Further, as shown in FIG. 1, the early damage prediction device 10 includes a failure information generation unit 116, an algorithm generation unit 117, and a damage prediction analysis unit 118 as control units for creating damage prediction results due to typhoons. , a map generation unit 119 and a damage degree determination unit 120 .
　故障情報生成部（計算部）１１６は、過去の台風による設備の故障度を地図上の一定単位の区画毎及び時刻毎に計算する機能を備える。 The failure information generation unit (calculation unit) 116 has a function of calculating the degree of failure of equipment due to past typhoons for each section of a fixed unit on the map and for each time.
　アルゴリズム生成部（生成部）１１７は、区画毎及び時刻毎の設備の故障度と、過去の台風の強度、過去の台風による停電数、過去の台風による降水量との関係を学習し、台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測可能な被害予測学習モデルを生成する機能を備える。 The algorithm generation unit (generation unit) 117 learns the relationship between the degree of equipment failure for each section and for each time, the intensity of past typhoons, the number of power outages caused by past typhoons, and the amount of rainfall caused by past typhoons. It has a function to generate a damage prediction learning model that can predict the degree of equipment failure for each section and time from the course and intensity forecast.
　被害予測解析部（解析部）１１８は、上記被害予測学習モデルを用いて、発生した台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測計算する機能を備える。 The damage prediction analysis unit (analysis unit) 118 has a function of predicting and calculating the degree of equipment failure for each section and time from the course and strength forecast of the generated typhoon using the damage prediction learning model.
　地図生成部１１９は、予測計算された各区画の設備の故障度を区画内のエリアに表示し、各エリアのエリア情報を更に表示する機能を備える。エリア情報とは、例えば、エリア内の人口、エリア内の建物数（家屋数等）、エリアのハザードマップである。また、地図生成部１１９は、設備の故障度やエリアの被害度に応じて各エリアを色分けして表示する機能を備える。 The map generation unit 119 has a function of displaying the predicted and calculated degree of failure of equipment in each section in an area within the section, and further displaying area information of each area. The area information is, for example, the population in the area, the number of buildings (the number of houses, etc.) in the area, and the hazard map of the area. In addition, the map generation unit 119 has a function of displaying each area by color-coding according to the degree of equipment failure and the degree of damage in the area.
　被害度判定部（判定部）１２０は、予測計算された区画毎及び時刻毎の設備の故障度と区画毎のエリア情報とを用いて、発生した台風による各区画及び各時刻の被害度を判定（決定、計算）する機能を備える。 A damage degree determination unit (determination unit) 120 determines the degree of damage caused by the generated typhoon in each section and at each time using the predicted and calculated equipment failure degree for each section and time and area information for each section. It has a function to (determine, calculate).
　［早期被害予測装置の動作］
　図２は、早期被害予測装置の処理フローを示す図である。早期被害予測装置１０は、設備の被害予測を学習する学習処理と、発生した台風の進路及び強度予報から設備の故障度を予測し、当該設備の故障度からエリアの被害度を判定する予測処理と、を実行する。 [Operation of early damage prediction device]
FIG. 2 is a diagram showing a processing flow of the early damage prediction device. The early damage prediction device 10 performs learning processing for learning damage prediction of facilities, prediction processing for predicting the degree of failure of facilities from the course and intensity forecast of the generated typhoon, and determining the degree of damage to the area from the degree of failure of the facility. and run
　ステップＳ１；
　ステップＳ１は、学習処理である。 Step S1;
Step S1 is a learning process.
　故障情報生成部１１６は、通信回線のＩＤや位置等が設定された通信回線情報と、通信回線の故障日時や故障した通信回線のＩＤ等が設定された通信回線の故障情報と、を用いて、過去の台風による通信回線の故障率を地図上の一定単位の区画毎に時系列に計算する。例えば、故障情報生成部１１６は、地図上のエリアを１０ｋｍ単位でメッシュ状に区分けした各エリアについて、エリア内の通信回線の故障数を同じエリア内の通信回線の総数で除算することで、各エリアの通信回線の故障率を計算する。通信回線の故障率を一定距離のエリア毎に計算することで、一般に非公開情報として取り扱われる通信回線情報及び通信回線の故障情報の秘匿性を維持できる。 The failure information generation unit 116 uses communication line information in which the communication line ID, position, etc. are set, and communication line failure information in which the date and time of the communication line failure, the ID of the failed communication line, etc. are set. , Calculate the failure rate of communication lines caused by past typhoons in time series for each fixed unit block on the map. For example, the failure information generation unit 116 divides the number of failures of communication lines in each area, which is obtained by dividing the area on the map into meshes in units of 10 km, by the total number of communication lines in the same area. Calculate the failure rate of communication lines in the area. By calculating the failure rate of the communication line for each area of a certain distance, the secrecy of the communication line information and the communication line failure information, which are generally treated as private information, can be maintained.
　ステップＳ２；
　ステップＳ２も学習処理である。 Step S2;
Step S2 is also a learning process.
　早期被害予測装置１０は、通信回線の故障率の他、設備の故障情報として、過去の台風による停電情報（停電の有無、停電数等）を各エリアに付与する。また、早期被害予測装置１０は、過去の台風情報として、台風の強度及び台風による降水量を各エリアに付与する。その後、アルゴリズム生成部１１７は、１０ｋｍ単位のメッシュ状のエリア毎に、かつ、時系列毎に、通信回線の故障率と、台風の強度、台風による停電数、台風による降水量との関係を学習した被害予測学習モデルを生成する。すなわち、被害予測学習モデルとは、「１０ｋｍ単位のメッシュ状のエリア毎の通信回線の故障率」に対し、「１０ｋｍ単位のメッシュ状のエリア毎の過去の台風データ」と、「１０ｋｍ単位のメッシュ状のエリア毎の過去の停電データ」と、を掛け合わせた学習モデルとなる。 In addition to the communication line failure rate, the early damage prediction device 10 provides each area with information on power outages due to past typhoons (whether there were power outages, the number of power outages, etc.) as equipment failure information. In addition, the early damage prediction device 10 provides each area with the intensity of a typhoon and the amount of precipitation due to the typhoon as past typhoon information. After that, the algorithm generation unit 117 learns the relationship between the failure rate of the communication line, the strength of the typhoon, the number of power outages due to the typhoon, and the amount of precipitation due to the typhoon for each mesh area of 10 km units and for each time series. Generate a damage prediction learning model based on In other words, the damage prediction learning model is based on "the failure rate of the communication line for each 10 km unit mesh area", "the past typhoon data for each 10 km unit mesh area", and "the 10 km unit mesh area". It is a learning model that multiplies past power outage data for each area.
　具体的には、アルゴリズム生成部１１７は、過去の台風の風速データ、雨量データ、停電データと通信回線の故障率とから教師データを作成し、当該教師データから通信回線の故障率を予測可能な被害予測学習モデルを生成する。例えば、アルゴリズム生成部１１７は、エリア毎の日時、各日時の最大風速、雨量、停電情報、通信回線の故障率を有するＣＳＶデータを作成し、最大風速に対する通信回線の故障率、停電数、雨量の相関グラフを生成する。最大風速と通信回線の故障率との相関グラフの例を図３に示す。なお、アルゴリズム生成部１１７は、ＣＳＶデータから台風の影響によらないデータ（最大風速が１５ｍ／ｓ以下等）を削除して、相関グラフを生成してもよい。 Specifically, the algorithm generation unit 117 creates teacher data from past typhoon wind speed data, rainfall data, power outage data, and communication line failure rates, and can predict communication line failure rates from the teacher data. Generate a damage prediction learning model. For example, the algorithm generation unit 117 creates CSV data having the date and time for each area, the maximum wind speed for each date and time, rainfall, power failure information, and communication line failure rate, and the communication line failure rate, power failure number, and rainfall rate for the maximum wind speed. Generate a correlation graph of An example of a correlation graph between maximum wind speed and communication line failure rate is shown in FIG. Note that the algorithm generation unit 117 may generate the correlation graph by deleting data that is not affected by the typhoon (maximum wind speed of 15 m/s or less, etc.) from the CSV data.
　ステップＳ３；
　ステップＳ３は、予測処理である。 Step S3;
Step S3 is a prediction process.
　被害予測解析部１１８は、発生した台風の位置データ及び風速データを上記被害予測学習モデルに入力し、当該被害予測学習モデルでプログラム処理させることで、当該台風の位置及び風速に対応する各エリアの通信回線の故障率を予測計算する。例えば、被害予測解析部１１８は、図３に示した相関グラフより、最大風速に対応する通信回線の故障率を計算する。 The damage prediction analysis unit 118 inputs the position data and wind speed data of the generated typhoon into the damage prediction learning model, and causes the damage prediction learning model to perform program processing, so that each area corresponding to the position and wind speed of the typhoon. Predict and calculate the failure rate of communication lines. For example, the damage prediction analysis unit 118 calculates the failure rate of the communication line corresponding to the maximum wind speed from the correlation graph shown in FIG.
　ステップＳ４；
　ステップＳ４も予測処理である。 Step S4;
Step S4 is also prediction processing.
　地図生成部１１９は、予測計算された各エリアの通信回線の故障率を各エリアに表示し、通信回線の故障率に応じて各エリアを色分けして表示する。また、地図生成部１１９は、エリア内の人口、エリア内の家屋データ、エリア内の建物データ、エリアのハザードマップも各エリアにマッシュアップする。 The map generation unit 119 displays the predicted and calculated failure rate of the communication line in each area, and displays each area by color-coding according to the failure rate of the communication line. The map generator 119 also mashes up the population in the area, the house data in the area, the building data in the area, and the hazard map of the area into each area.
　ステップＳ５；
　ステップＳ５も予測処理である。 Step S5;
Step S5 is also prediction processing.
　被害度判定部１２０は、予測計算された各エリアの通信回線の故障度と、エリア内の人口、エリア内の家屋データ、エリア内の建物データ、エリアのハザードマップとを用いて、発生した台風による各エリアの被害度を時系列に判定する。被害度とは、「１０ｋｍ単位のメッシュ状のエリア毎の通信回線の故障率の予測値」に対し、「１０ｋｍ単位のメッシュ状のエリア毎の人口データ」と、「１０ｋｍ単位のメッシュ状のエリア毎の家屋データ」と、「１０ｋｍ単位のメッシュ状のエリア毎の建物データ」と、「１０ｋｍ単位のメッシュ状のエリア毎のハザードマップ」と、を掛け合わせた値となる。 The damage degree determination unit 120 uses the predicted and calculated failure degree of the communication line in each area, the population in the area, the house data in the area, the building data in the area, and the hazard map of the area to determine the degree of damage caused by the typhoon. Determine the degree of damage in each area in chronological order. The degree of damage is defined as "predicted value of communication line failure rate for each mesh area of 10 km unit", "population data for each mesh area of 10 km unit", and "mesh area of 10 km unit". "house data for each area", "building data for each mesh area in units of 10 km", and "hazard map for each mesh area in units of 10 km".
　被害度判定部１２０は、これまでの計算に用いた任意の情報又はデータを単独で又は任意に組み合わせて用いて、被害度を算出可能である。例えば、被害度判定部１２０は、まず、設備の故障危険度について、ステップＳ３で予測計算した通信回線の故障率に応じ、通信回線の故障危険度ｆ１をレベル１～レベル３の範囲内でエリア毎に設定する。また、被害度判定部１２０は、最大風速と停電数との相関グラフから予測した台風の停電数に応じ、停電発生危険度ｆ２をレベル１～レベル３の範囲内でエリア毎に設定する。そして、被害度判定部１２０は、国勢調査の人口データ、家屋データ、ハザードマップを地図に取り込み、人的被害推計及び家屋被害推計を行うことで、各エリアの被害度を判定する。 The damage degree determination unit 120 can calculate the degree of damage using any information or data used in the previous calculations, either alone or in any combination. For example, the damage level determination unit 120 first determines the failure risk level f1 of the communication line within the range of level 1 to level 3 according to the failure rate of the communication line predicted and calculated in step S3. set for each Further, the damage degree determination unit 120 sets the power outage risk f2 within the range of level 1 to level 3 for each area according to the number of power outages of the typhoon predicted from the correlation graph between the maximum wind speed and the number of power outages. Then, the damage degree determination unit 120 incorporates the population data, house data, and hazard map of the national census into the map, and estimates human damage and house damage, thereby determining the degree of damage in each area.
　例えば、ｆ１＝レベル１、ｆ２＝レベル１、人口密度が中以下の場合、被害度のレベルを１（生活への影響可能性中かつ被害規模小）とする。ｆ１＝レベル２、ｆ２＝レベル２、ハザードマップ上で危険エリア、人口密度が中の場合、被害度のレベルを２（生活への影響可能性中かつ被害規模中）とする。ｆ１＝レベル３、ｆ２＝レベル３、ハザードマップ上で危険エリア、人口密度が大の場合、被害度のレベルを３（生活への影響可能性大かつ被害規模大）とする。 For example, if f1 = level 1, f2 = level 1, and the population density is medium or lower, the level of damage is set to 1 (intermediate impact on life and small scale of damage). If f1=level 2, f2=level 2, and the danger area on the hazard map and the population density is medium, the degree of damage is set to level 2 (intermediate possibility of impact on life and medium scale of damage). f1 = level 3, f2 = level 3. If the hazard map indicates a dangerous area and the population density is high, the degree of damage is set to level 3 (high possibility of impact on life and large scale of damage).
　その後、地図生成部１１９は、被害度に応じて各エリアを色分けして表示する。各エリアの被害度の表示例を図４に示す。 After that, the map generation unit 119 displays each area by color-coding according to the degree of damage. FIG. 4 shows a display example of the degree of damage in each area.
　［効果］
　本実施形態によれば、早期被害予測装置１０が、過去の台風による設備の故障度を地図上の一定単位の区画毎及び時刻毎に計算する故障情報生成部１１６と、前記区画毎及び時刻毎の設備の故障度と前記過去の台風の強度との関係を学習し、台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測可能な被害予測学習モデルを生成するアルゴリズム生成部１１７と、前記被害予測学習モデルを用いて、発生した台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測計算する被害予測解析部１１８と、当該区画毎及び時刻毎の設備の故障度と区画毎のエリア情報とを用いて、前記発生した台風による各区画及び各時刻の被害度を判定する被害度判定部１２０と、を備えるので、過去の台風による設備の故障状況から将来の台風による被害を予測可能な技術を提供できる。 [effect]
According to the present embodiment, the early damage prediction device 10 includes the failure information generation unit 116 that calculates the degree of failure of equipment due to past typhoons for each section of a fixed unit on the map and for each time, and for each section and each time. Algorithm generation unit that learns the relationship between the degree of equipment failure and the strength of the past typhoon, and generates a damage prediction learning model that can predict the degree of equipment failure for each section and time from the course and intensity forecast of the typhoon 117, a damage prediction analysis unit 118 that predicts and calculates the degree of failure of equipment for each section and for each time from the course and intensity forecast of the generated typhoon using the damage prediction learning model, and the equipment for each section and each time and a damage degree determination unit 120 that determines the degree of damage for each section and each time due to the generated typhoon using the failure degree and area information for each section. We can provide technology that can predict the damage caused by future typhoons.
　これにより、先手をうった危機対応が可能になる。例えば、台風上陸の３日～５日前に、任意の地点の被害を予測できる。インフラ企業は、事前に作業員を確保でき、所有するインフラに対して適切かつ早急な復旧が可能になる。自治体は、通信途絶や停電等の可能性から事前に人員の確保、機材の準備等が可能になる。一般企業は、社員の安全を図るとともに、事前の仕入れ等、ライフライン途絶に備えた対応が可能になる。 This will enable preemptive crisis response. For example, 3 to 5 days before a typhoon hits, it is possible to predict the damage at an arbitrary point. Infrastructure companies will be able to secure workers in advance, and will be able to restore their infrastructure appropriately and quickly. Local governments will be able to secure personnel and prepare equipment in advance in case of communication disruptions or power outages. General companies will be able to ensure the safety of their employees and prepare for lifeline disruptions, such as advance purchases.
　［その他］
　本発明は、上記実施形態に限定されない。本発明は、本発明の要旨の範囲内で数々の変形が可能である。 [others]
The invention is not limited to the above embodiments. The present invention can be modified in many ways within the scope of the gist of the present invention.
　上記説明した本実施形態の早期被害予測装置１０は、例えば、図５に示すように、ＣＰＵ９０１と、メモリ９０２と、ストレージ９０３と、通信装置９０４と、入力装置９０５と、出力装置９０６と、を備えた汎用的なコンピュータシステムを用いて実現できる。メモリ９０２及びストレージ９０３は、記憶装置である。当該コンピュータシステムにおいて、ＣＰＵ９０１がメモリ９０２上にロードされた所定のプログラムを実行することにより、早期被害予測装置１０の各機能が実現される。 The early damage prediction device 10 of the present embodiment described above includes, for example, a CPU 901, a memory 902, a storage 903, a communication device 904, an input device 905, and an output device 906, as shown in FIG. It can be realized using a general-purpose computer system equipped with. Memory 902 and storage 903 are storage devices. In the computer system, each function of the early damage prediction apparatus 10 is realized by executing a predetermined program loaded on the memory 902 by the CPU 901 .
　早期被害予測装置１０は、１つのコンピュータで実装されてもよい。早期被害予測装置１０は、複数のコンピュータで実装されてもよい。早期被害予測装置１０は、コンピュータに実装される仮想マシンであってもよい。早期被害予測装置１０用のプログラムは、ＨＤＤ、ＳＳＤ、ＵＳＢメモリ、ＣＤ、ＤＶＤ等のコンピュータ読取り可能な記録媒体に記憶できる。早期被害予測装置１０用のプログラムは、通信ネットワークを介して配信することもできる。 The early damage prediction device 10 may be implemented by one computer. The early damage prediction device 10 may be implemented by multiple computers. The early damage prediction device 10 may be a virtual machine implemented on a computer. A program for the early damage prediction device 10 can be stored in computer-readable recording media such as HDD, SSD, USB memory, CD, and DVD. A program for the early damage prediction device 10 can also be distributed via a communication network.
　１：早期被害予測システム
　１０：早期被害予測装置
　２０：クライアント端末
　３０：通信ネットワーク
　１１１：故障情報記憶部
　１１２：地図情報記憶部
　１１３：気象情報記憶部
　１１４：停電情報記憶部
　１１５：エリア情報記憶部
　１１６：故障情報生成部
　１１７：アルゴリズム生成部
　１１８：被害予測解析部
　１１９：地図生成部
　１２０：被害度判定部
　９０１：ＣＰＵ
　９０２：メモリ
　９０３：ストレージ
　９０４：通信装置
　９０５：入力装置
　９０６：出力装置 1: Early damage prediction system 10: Early damage prediction device 20: Client terminal 30: Communication network 111: Failure information storage unit 112: Map information storage unit 113: Weather information storage unit 114: Power outage information storage unit 115: Area information storage unit 116: Failure information generation unit 117: Algorithm generation unit 118: Damage prediction analysis unit 119: Map generation unit 120: Damage degree determination unit 901: CPU
902: Memory 903: Storage 904: Communication device 905: Input device 906: Output device

Claims (8)

  1. 　過去の台風による設備の故障状況から将来の台風による被害を予測する早期被害予測装置において、
    　過去の台風による設備の故障度を地図上の一定単位の区画毎及び時刻毎に計算する計算部と、
    　前記区画毎及び時刻毎の設備の故障度と前記過去の台風の強度との関係を学習し、台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測可能な被害予測学習モデルを生成する生成部と、
    　前記被害予測学習モデルを用いて、発生した台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測計算する解析部と、
    　当該区画毎及び時刻毎の設備の故障度と区画毎のエリア情報とを用いて、前記発生した台風による各区画及び各時刻の被害度を判定する判定部と、
    　を備える早期被害予測装置。     In an early damage prediction device that predicts damage caused by future typhoons from the failure status of facilities caused by past typhoons,
    a calculation unit that calculates the degree of failure of equipment due to past typhoons for each section of a certain unit on the map and for each time;
    A damage prediction learning model capable of learning the relationship between the degree of failure of equipment for each section and time and the strength of past typhoons, and predicting the degree of failure of equipment for each section and time from the course and intensity forecast of typhoons. a generator that generates
    an analysis unit that uses the damage prediction learning model to predict and calculate the degree of failure of equipment for each section and time from the course and intensity forecast of the generated typhoon;
    a determination unit that determines the degree of damage caused by the generated typhoon in each section and at each time by using the degree of failure of the equipment for each section and each time and the area information for each section;
    An early damage prediction device with
  2. 　前記計算部は、
    　前記区画毎及び時刻毎の設備の故障度として、地図上のエリアを一定距離でメッシュ状に区分けした各エリアの通信回線の故障率を時系列に計算する請求項１に記載の早期被害予測装置。     The calculation unit
    2. The early damage prediction device according to claim 1, which calculates, in chronological order, the failure rate of a communication line in each area of a map divided into a mesh at a constant distance as the equipment failure rate for each section and for each time. .
  3. 　前記生成部は、
    　前記被害予測学習モデルとして、前記区画毎及び時刻毎の設備の故障度と前記過去の台風の強度との相関を示す相関グラフを生成する請求項１又は２に記載の早期被害予測装置。     The generating unit
    3. The early damage prediction apparatus according to claim 1, wherein, as the damage prediction learning model, a correlation graph showing a correlation between the degree of equipment failure for each section and for each time and the intensity of the past typhoon is generated.
  4. 　前記生成部は、
    　前記過去の台風による停電数との関係を更に学習することで、前記被害予測学習モデルを生成する請求項１乃至３のいずれかに記載の早期被害予測装置。     The generating unit
    The early damage prediction device according to any one of claims 1 to 3, wherein the damage prediction learning model is generated by further learning a relationship with the number of power outages caused by past typhoons.
  5. 　前記生成部は、
    　前記過去の台風による降水量との関係を更に学習することで、前記被害予測学習モデルを生成する請求項１乃至４のいずれかに記載の早期被害予測装置。     The generating unit
    5. The early damage prediction device according to any one of claims 1 to 4, wherein the damage prediction learning model is generated by further learning a relationship with the amount of precipitation caused by the past typhoon.
  6. 　前記判定部は、
    　前記エリア情報として、エリア内の人口、エリア内の建物数、エリアのハザードマップのうち１つ以上を用いて、前記発生した台風による各区画及び各時刻の被害度を判定する請求項１乃至５のいずれかに記載の早期被害予測装置。     The determination unit is
    6. Using one or more of the population in the area, the number of buildings in the area, and the hazard map of the area as the area information, determining the degree of damage in each section and at each time due to the generated typhoon. The early damage prediction device according to any one of 1.
  7. 　過去の台風による設備の故障状況から将来の台風による被害を予測する早期被害予測方法において、
    　早期被害予測装置が、
    　過去の台風による設備の故障度を地図上の一定単位の区画毎及び時刻毎に計算するステップと、
    　前記区画毎及び時刻毎の設備の故障度と前記過去の台風の強度との関係を学習し、台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測可能な被害予測学習モデルを生成するステップと、
    　前記被害予測学習モデルを用いて、発生した台風の進路及び強度予報から区画毎及び時刻毎の設備の故障度を予測計算するステップと、
    　当該区画毎及び時刻毎の設備の故障度と区画毎のエリア情報とを用いて、前記発生した台風による各区画及び各時刻の被害度を判定するステップと、
    　を行う早期被害予測方法。     In the early damage prediction method for predicting damage from future typhoons based on equipment failures caused by past typhoons,
    Early damage prediction device
    a step of calculating the degree of failure of equipment due to past typhoons for each fixed unit block on the map and for each time;
    A damage prediction learning model capable of learning the relationship between the degree of failure of equipment for each section and time and the strength of past typhoons, and predicting the degree of failure of equipment for each section and time from the course and intensity forecast of typhoons. a step of generating
    using the damage prediction learning model to predict and calculate the degree of failure of equipment for each section and time from the course and strength forecast of the generated typhoon;
    a step of determining the damage degree of each section and each time by the generated typhoon using the failure degree of the equipment for each section and each time and the area information for each section;
    early damage prediction method.
  8. 　請求項１乃至６のいずれかに記載の早期被害予測装置としてコンピュータを機能させる早期被害予測プログラム。 An early damage prediction program that causes a computer to function as the early damage prediction device according to any one of claims 1 to 6.
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