Program, device, system and method for estimating risk of evaluation target

Abstract

To provide a risk estimation program for determining a risk index for an evaluation target with respect to a predetermined risk event.SOLUTION: A program causes a computer to function as: frequency information determination means which determines stay areas for which information on the stay of a target satisfies a predetermined condition, out of multiple areas included in a predetermined area, based on position information or the target, and determines stay frequency information related to frequency that the target stays in the stay area, for each of the determined stay areas; area risk determination means which determines a degree of exposure to a risk event in the stay area, based on data related to occurrence of the risk event and / or determines a degree of robustness for the risk event in the stay area, based on data related to measures, handling, preparation or recognition for risks in the stay area; and risk index determination means which determines a risk index of the target using the stay frequency information of the stay area, the degree of exposure in the stay area, and / or the degree of robustness of the stay area.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technique for implementing measures against hazardous events such as natural disasters. [Background technology]

[0002] How to deal with natural disasters has been a major issue for some time now, and there has been a strong demand for understanding the risks of natural disasters in advance and taking more appropriate disaster prevention measures.

[0003] As an example of such risk assessment, Patent Document 1 discloses a technology for calculating a natural disaster comprehensive risk level index for a target area. Specifically, this technology uses a forecast index, a pressure index, a state index of the city or town affected by the disaster, and a response index for a specified natural disaster to calculate the weight of the index at each level through hierarchical analysis, then obtains the index value of the lowest level index from the acquired natural disaster data for the target area, and calculates a natural disaster comprehensive risk level index for the target area using this index value and the weight of the index at each level.

[0004] Furthermore, for example, Patent Document 2 discloses a technology for quantitatively assessing risk based on qualitative data on natural disasters. Specifically, this technology involves obtaining expert comments in step 1, expressing a cloud model and correcting the comments in step 2, calculating comprehensive cloud numerical characteristics in step 3, generating a risk cloud atlas and each membership function in step 4, constructing a Bayesian network based on expert knowledge in step 5, setting conditional probabilities in the Bayesian network in step 6, and estimating the risk of natural disasters using the Bayesian network in step 7.

[0005] Furthermore, Non-Patent Document 1 discloses the "risk of disasters resulting from natural phenomena" in 181 countries around the world, and calculates the risk for each country using exposure and vulnerability to disasters. Also, Non-Patent Document 2, while referring to the disclosures in Non-Patent Document 1, establishes its own index for the risk of disasters in Japan, taking into account the disaster circumstances in Japan, and calculates a risk index for disasters in Japan using exposure and vulnerability for each disaster. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Chinese Patent Application Publication No. 114254963 [Patent Document 2] Chinese Patent Application Publication No. 109978373 [Non-patent literature]

[0007] [Non-Patent Document 1] “WeltRisikoBericht - WeltRisikoIndex (World Risk Index)”, [online], [Retrieved October 3, 2022], Internet <URL: https: / / weltrisikobericht.de / weltrisikobericht-2021-e / #:~:text=The%20WorldRiskIndex%20states%20the%20risk%20of%20disaster%20in,to%20earthquakes%2C%20storms%2C%20floods%2C%20droughts%20and%20sea-level%20rise.> [Non-patent document 2] Kazuya Ito, Tomofumi Koyama, Osamu Kikumoto, "Development of Municipal GNS (GNS-Ver.2.0) for the Gross National Safety for Natural Disasters (GNS) Index: A Case Study of Eastern Japan," Construction Machinery Construction, Vol.72, No.10, 2020 Summary of the Invention [Problem to be solved by the invention]

[0008] As such, various risk indicators for natural disasters have been proposed in the past, with assessment units set at nations, local governments, etc. However, it is thought that the risk of natural disasters varies greatly depending on the individual who belongs to a nation or local government.

[0009] For example, an individual's risk of natural disasters will naturally vary depending on the area in which they stay during a given period, such as a single day. It is also thought that the risk of natural disasters will vary greatly depending on an individual's level of awareness of disaster prevention and mitigation on a daily basis. However, no individual-based risk index for natural disasters has been proposed to date, including in the aforementioned Patent Documents 1 and 2 and Non-Patent Documents 1 and 2.

[0010] Therefore, an object of the present invention is to provide a risk estimation program, device, system, and method that can determine a risk index for an object of evaluation for a predetermined risk event. [Means for solving the problem]

[0011] According to the present invention, there is provided a risk estimation program for estimating a risk index for a predetermined risk event, comprising: a frequency information determination means for determining, from among a plurality of zones included in a predetermined region, a zone of stay for which information relating to the stay of the subject satisfies a predetermined condition based on the acquired information relating to the location of the subject, and for each of the determined zones of stay, determining stay frequency information relating to the frequency at which the subject stays in the zone of stay; Determine the degree of exposure to the hazardous event in the area based on the acquired data on the occurrence of the hazardous event; and / or Published by the national government, local government, or designated organization, In the acquired area of ​​stay The index data danger phenomenon measures, responses, preparations or awareness regarding For at least one category, a numerical indicator is provided to show the level of the relevant measures, response, preparation, or awareness. Based on the data, Using the numerical values, an area risk determination means for determining a degree of robustness of the stay area against the hazardous event; a risk index determination means for determining the risk index of the subject using the stay frequency information of the stay area, the degree of exposure of the stay area, and / or the degree of robustness of the stay area; A risk estimation program is provided that causes a computer to function as follows.

[0012] As one embodiment of the risk estimation program according to the present invention, the risk estimation program further causes the computer to function as object risk determination means that determines the degree of robustness of the object against the risk event, based on the acquired data related to matters that may work in a direction to reduce the degree of risk to the object, It is also preferable that the risk index determining means determines the risk index of the subject by also using the degree of robustness of the subject.

[0013] In the above embodiment, the area risk determination means determines the degree of exposure of the stay area and the degree of robustness of the stay area, The risk index determination means determines a risk index of the subject using the stay frequency information and the degree of exposure for each determined stay area, the stay frequency information and the degree of robustness for each determined stay area, and the degree of robustness of the subject; It is also preferable that this risk estimation program further functions as display control means for dividing the risk index of the target into an exposure index, which is an index portion determined using the stay frequency information of the stay area and the degree of exposure, and a robustness index, which is an index portion determined using the stay frequency information of the stay area and the degree of robustness, and the degree of robustness of the target, and for generating display data for displaying the risk index of the target so that the magnitude of the exposure index and the magnitude of the robustness index can be distinguished.

[0014] Furthermore, it is also preferable that the display control means generates the display data for displaying the size of the exposure index divided into the size of the index portion related to each determined area of ​​stay, and for displaying the size of the robustness index divided into the size of the index portion related to each determined area of ​​stay and the size of the index portion related to the degree of robustness of the subject.

[0015] Furthermore, it is also preferable that the display control means generates display data for displaying the risk index and the tolerance range of the target in a graph of the target's risk index having an axis for the exposure index and an axis for the robustness index, so that the tolerance range of the set risk index is clearly indicated.

[0016] In addition, in the embodiment including the above-mentioned target risk determination means, the area risk determination means determines the degree of exposure of the stay area and the degree of robustness of the stay area, The risk index determination means determines a risk index of the subject using the stay frequency information and the degree of exposure for each determined stay area, the stay frequency information and the degree of robustness for each determined stay area, and the degree of robustness of the subject; The risk index of the target is divided into an exposure index, which is an index portion determined using the stay frequency information of the stay area and the degree of exposure of the stay area, and a robustness index, which is an index portion determined using the stay frequency information of the stay area and the degree of robustness of the stay area, and the degree of robustness of the target, It is also preferable that the risk estimation program further causes the computer to function as proposal information generation means that (a) generates proposal information for reducing the size of the index portion of the exposure index, when the size of the index portion relating to the area of ​​stay associated with stay frequency information associated with a frequency high enough to satisfy a specified condition is large enough to satisfy the specified condition; (b) generates proposal information for increasing the size of the index portion of the robustness index, when the size of the index portion relating to the area of ​​stay associated with stay frequency information associated with a frequency high enough to satisfy the specified condition is small enough to satisfy the specified condition; and / or (c) generates proposal information for increasing the size of the index portion of the robustness index, when the size of the index portion relating to the degree of robustness of the subject is small enough to satisfy the specified condition.

[0017] Furthermore, in the risk estimation program according to the present invention, it is also preferable that the frequency information determination means determines the area of ​​stay based on information relating to the location of the target, obtained from a positioning means related to a terminal attached to the target or related to communication with the terminal. It is also preferable that the frequency information determination means determines the area of ​​stay based on the location information of the target, including a residential area related to the target's residence, a workplace area related to the target's workplace, and a frequent stay area where the target stays frequently enough to satisfy a predetermined condition.

[0018] In the risk estimation program according to the present invention, the predetermined risk event is set to be a risk event including at least one of earthquakes, tsunamis, storm surges, landslides, volcanic disasters, and floods; The area risk determination means determines, for each set hazardous event, a degree of exposure to the hazardous event in the stay area; It is also preferable that the risk index determining means determines the risk index of the subject using information on the frequency of stay in the area and the degree of exposure to each of the risk events in the area.

[0019] Furthermore, in the risk estimation program according to the present invention, indexThe data is classified into set categories including at least one of the following categories: buildings, lifelines, infrastructure, supplies and reserves, medical services, and ordinances and local government. For each hazardous event, Countermeasures, response, preparation or awareness An indicator with a numerical value showing the degree of It is data, The area risk determination means determines the degree of robustness of the stay area for each of the set classification items, It is also preferable that the risk index determining means determines a risk index of the target using information on the frequency of stay in the area and the degree of robustness of the area for each of the set classification items.

[0020] In an embodiment including the above-described target risk determination means, the items that can work to reduce the degree of the risk are set items including at least one of a means of transportation, a means of communication, an economic situation, a family structure, a health condition, insurance, and information collection, The target risk determination means determines the degree of robustness of the target for each of the set items, It is also preferable that the risk index determining means determines the risk index of the subject by using the degree of robustness of the subject for each of the set items.

[0021] According to the present invention, there is also provided a risk estimation device for estimating a risk index for a predetermined risk event, comprising: a frequency information determination means for determining, from among a plurality of zones included in a predetermined region, a zone of stay for which information relating to the stay of the subject satisfies a predetermined condition based on the acquired information relating to the location of the subject, and for each of the determined zones of stay, determining stay frequency information relating to the frequency at which the subject stays in the zone of stay; Based on the acquired data on the occurrence of the dangerous event, the area of ​​stay in Determine the degree of exposure to the hazard; and / or Published by the national government, local government, or designated organization, In the acquired area of ​​stay The index data danger phenomenon measures, responses, preparations or awareness regarding For at least one category, a numerical indicator is provided to show the level of the relevant measures, response, preparation, or awareness. Based on the data, Using the numerical values,an area risk determination means for determining a degree of robustness of the stay area against the hazardous event; a risk index determination means for determining the risk index of the subject using the stay frequency information of the stay area, the degree of exposure of the stay area, and / or the degree of robustness of the stay area; A risk estimation device is provided, comprising:

[0022] According to the present invention, there is further provided a risk estimation system for estimating a risk index for a predetermined risk event, the system comprising: a frequency information determination means for determining, from among a plurality of zones included in a predetermined region, a zone of stay for which information relating to the stay of the subject satisfies a predetermined condition based on the acquired information relating to the location of the subject, and for each of the determined zones of stay, determining stay frequency information relating to the frequency at which the subject stays in the zone of stay; Based on the acquired data on the occurrence of the dangerous event, the area of ​​stay in Determine the degree of exposure to the hazard; and / or Published by the national government, local government, or designated organization, In the acquired area of ​​stay The index data danger phenomenon measures, responses, preparations or awareness regarding For at least one category, a numerical indicator is provided to show the level of the relevant measures, response, preparation, or awareness. Based on the data, Using the numerical values, an area risk determination means for determining a degree of robustness of the stay area against the hazardous event; a risk index determination means for determining the risk index of the subject using the stay frequency information of the stay area, the degree of exposure of the stay area, and / or the degree of robustness of the stay area; A risk estimation system is provided having the following:

[0023] According to the present invention, there is further provided a risk estimation method for estimating a risk index for a predetermined risk event, comprising: determining, from among a plurality of zones included in a predetermined region, a zone of stay for which information related to the stay of the subject satisfies a predetermined condition based on the acquired information related to the location of the subject, and determining, for each determined zone of stay, stay frequency information related to the frequency with which the subject stays in the zone of stay; Based on the acquired data on the occurrence of the dangerous event, the area of ​​stay in Determine the degree of exposure to the hazard; and / or Published by the national government, local government, or designated organization, In the acquired area of ​​stay The index data danger phenomenon measures, responses, preparations or awareness regarding For at least one category, a numerical indicator is provided to show the level of the relevant measures, response, preparation, or awareness. Based on the data, Using the numerical values, determining a degree of robustness of the stay area against the hazardous event; determining the risk index of the subject using the frequency of stay information of the area of ​​stay, and the degree of exposure of the area of ​​stay and / or the degree of robustness of the area of ​​stay; A computer-implemented risk estimation method is provided, comprising: [Effects of the Invention]

[0024] According to the risk estimation program, device, system and method of the present invention, a risk index can be determined for an object of evaluation for a predetermined risk event. [Brief explanation of the drawings]

[0025] [Figure 1] 1 is a functional block diagram showing a functional configuration of an embodiment of a risk estimation device according to the present invention; [Figure 2] 10 is a table for explaining an embodiment of a process for calculating an area exposure degree according to the present invention. [Figure 3] 10 is a table for explaining an embodiment of a process for calculating a region robustness according to the present invention. [Figure 4] 10 is a table for explaining an embodiment of a process for calculating a target robustness according to the present invention. [Figure 5] 10 is a graph for explaining a specific example of a result in which the contents of the display data of the risk index according to the present invention are displayed. DETAILED DESCRIPTION OF THE INVENTION

[0026] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0027] [Hazard estimation device / system] FIG. 1 is a functional block diagram showing the functional configuration of an embodiment of a risk estimation device according to the present invention.

[0028] The risk estimation device 1 of this embodiment shown in Fig. 1 is a device that calculates and outputs a "risk index" of an evaluation target (a specific individual to be evaluated in this embodiment, hereinafter abbreviated as "target individual") for a predetermined risk event (a natural disaster in this embodiment). Furthermore, in this embodiment, the calculated "risk index" can be displayed on the display of the user interface (UI) unit 106 in the form of an easy-to-understand graph or table.

[0029] In order to realize the function of calculating the "risk index" for such a target individual, the risk estimation device 1 specifically: (A) a frequency information determination unit 111 that determines, based on the acquired "information related to the location of the target individual," a "stay area" (stay mesh) for which information related to the stay of the target individual satisfies a predetermined condition from among a plurality of areas (predetermined (area) meshes in this embodiment) included in a predetermined region (prefecture in this embodiment), and determines, for each determined "stay area," "stay frequency information" related to the frequency with which the target individual stays in the "stay area"; (B) (b1) an area risk determination unit (112, 113) that determines the degree of exposure to a hazardous event in the "stay area" (area exposure level) based on the acquired "data related to the occurrence of a hazardous event" and / or (b2) determines the degree of robustness to a hazardous event in the "stay area" (area robustness) based on the acquired "data related to measures, responses, preparations or awareness of danger" in the "stay area"; (C) a risk index determination unit 115 that determines the “risk index” of the target individual using the “stay frequency information” of the “stay area”, the area exposure (degree of exposure) of the “stay area”, and / or the area robustness (degree of robustness) of the “stay area”; It has the following characteristics.

[0030] In this way, the risk estimation device 1 can determine a "risk index" that is specific to the evaluation target (target individual), unlike the risk assessment indexes that have been proposed in the past for specific regions (e.g., countries or prefectures).

[0031] Here, the "information related to the location of the target individual" in (A) above can be, for example, GPS (Global Positioning System) positioning information of terminal 2, with the target individual (evaluation subject) as the user of terminal 2 (who has authorized the use of positioning information). In this case, it is also preferable to adopt the quarter area mesh (an area of ​​approximately 250 m (meters) square) defined by the Statistics Bureau of the Ministry of Internal Affairs and Communications as the above-mentioned area (mesh). In any case, based on this "information related to the location of the target individual," it is possible to determine "stay frequency information" including, for example, the frequency (proportion) of the target individual being at home (as a stay area) or the frequency (proportion) of the target individual being at work (as a stay area).

[0032] Furthermore, the "data related to the occurrence of hazardous events" in (B)(b1) above, which will be explained in detail later using Figure 2, is data including the number of disasters occurring by disaster type in each prefecture (region). Based on such data, the area exposure (degree of exposure) to hazardous events in each "stay area" can be determined. Furthermore, the "data related to countermeasures, response, preparation, or awareness against danger" in (B)(b2) above, which will be explained in detail later using Figure 3, is data including numerical values ​​indicating the degree of countermeasures, response, preparation, or awareness against natural disasters in each "stay area." Based on such data, the area robustness (degree of robustness) against hazardous events in each "stay area" can be determined.

[0033] In this way, by determining the area exposure level and / or area robustness for each "stay area" specific to the target individual (evaluation subject), and further taking into account the "stay frequency information" relating to the stay frequency specific to the target individual (evaluation subject), it is possible to derive a "risk index" specific to the target individual (evaluation subject).

[0034] In this embodiment, the object for which the "risk index" is to be calculated is a human (individual target), and the following description will also refer to this object as a human (individual target), but the object according to the present invention is not limited to this. For example, it is possible to calculate the "risk index" for an automobile (such as a long-distance truck or a shared car) equipped with a GPS positioning device and a communication terminal, an autonomous mobile robot, or even a transport vehicle (such as a railroad car) for which "stay frequency information" can be determined.

[0035] Furthermore, in this embodiment (and in the following description), the predetermined risk event is a natural disaster, but the risk event according to the present invention is not limited to this. In other words, any event for which "data related to the occurrence of a risk event" or "data related to measures, responses, preparations, or awareness of risk" in each "stay area" can be obtained, such as various accidents or crimes, can also be set as the risk event according to the present invention.

[0036] Furthermore, although the risk estimation device 1 of this embodiment is an apparatus including all of the frequency information determination unit 111 described above (A), the area risk determination units (112, 113) described above (B), and the risk index determination unit 115 described above (C), the configuration of the present invention is not limited to this. That is, at least one of the frequency information determination unit 111, the (first) area risk determination unit 112, the (second) area risk determination unit 113, and the risk index determination unit 115 may be a functional component of another apparatus. For example, the frequency information determination unit 111, the (first) area risk determination unit 112, and the (second) area risk determination unit 113 may each be included in a separate server, and the risk index determination unit 115 may be included in a client terminal (e.g., terminal 2 owned by a target individual) that receives information from these servers. In any case, these apparatuses as a whole can be considered to be the risk estimation system of the present invention.

[0037] [Device functional configuration, risk estimation program and method] The following provides a more detailed explanation of the functional configuration of the risk estimation device 1 described above. Similarly, according to the functional block diagram of Fig. 1, in this embodiment, the risk estimation device 1 includes a communication interface unit 101, a user positioning information storage unit 102, a local government disaster information storage unit 103, a disaster index information storage unit 104, a target information storage unit 105, a user interface (UI) unit 106, and a processor / memory (a processing system with a memory function).

[0038] The processor memory stores an embodiment of a risk estimation program according to the present invention, has computer functions, and executes the risk estimation program to perform risk estimation processing. Therefore, the risk estimation device 1 may be a cloud server, a non-cloud server, or even a dedicated device for risk estimation, but it can also be, for example, a personal computer (PC), a notebook or tablet computer, or a smartphone equipped with the risk estimation program according to the present invention.

[0039] The processor memory further includes a frequency information determination unit 111, a first area risk determination unit 112, a second area risk determination unit 113, a target risk determination unit 114, a risk index determination unit 115, a display control unit 116, a proposal information generation unit 117, and a communication control unit 121. These functional components can be considered to be functions realized by executing a risk estimation program stored in the processor memory. The processing flow shown by arrows connecting the functional components of the risk estimation device 1 in FIG. 1 can also be understood as one embodiment of a risk estimation method according to the present invention.

[0040] In the embodiment described below, the "risk index" of a target individual is determined using both the "area exposure" and "area robustness" of each stay area described above, and also using the target individual's "target robustness," which will be described later. For example, the risk of a natural disaster for a certain individual will naturally vary depending on the area in which the individual stays over a given period of time, for example, one week. Taking this into consideration, in this embodiment, the "area exposure" and "area robustness" are used to determine the target individual's risk index. Furthermore, the risk of a natural disaster will vary greatly depending on the individual's level of daily disaster prevention and mitigation awareness. Taking this into consideration, in this embodiment, the "target robustness" is also used to determine the target individual's "risk index."

[0041] <Means for determining frequency information> Similarly, in the functional block diagram of FIG. 1, the frequency information determination unit 111 in this embodiment is (a) Based on the "GPS positioning information" or "location registration information" (connection sector information) about the target individual who is the user of the terminal 2, which is received (via the communication interface unit 101 and the communication control unit 121) from the positioning information management server 3 managed by the telecommunications carrier and stored and managed in the user positioning information storage unit 102, determine the stay mesh (stay area) in which the information about the stay of the target individual satisfies the "predetermined conditions" from among multiple meshes (areas) included in a predetermined area (each prefecture); (b) For each determined mesh of stay (area of ​​stay), frequency of stay information relating to the frequency with which the target individual stays in that mesh of stay (area of ​​stay) is determined.

[0042] As a modification, instead of the location information of the target individual obtained from the positioning means (GPS positioning means) associated with terminal 2 or the positioning means (base station) associated with the communication of terminal 2 as described above, the target individual's stay history information may be generated from information such as responses to a travel history survey, travel history declarations, and even check-in history on SNS (Social Networking Service), and the stay mesh may be determined based on this information.

[0043] The "predetermined condition" in (a) above may be, for example, "the mesh where the user stayed the longest (continuously) during a predetermined time period in one day" or "the mesh where the user's stay time during a predetermined period exceeds a predetermined threshold value." Specifically, in this embodiment, the frequency information determination unit 111 determines the following as the mesh where the user stayed: (a1) Estimated residential mesh: The mesh in which the subject individual stayed the longest (continuously) during the night (e.g., 18:00-3:00), (a2) Estimated workplace mesh: The mesh in which the target individual stayed the longest (continuously) during the daytime (e.g., 9:00-19:00), and (a3) Frequent visit mesh: A mesh where the target individual stays frequently enough to meet specified conditions, for example, a mesh where the number of visits in a specified period (e.g., one month) is equal to or exceeds a specified threshold (e.g., three times) and the average stay time per visit is equal to or exceeds a specified threshold (e.g., one hour), and is a mesh other than the above-mentioned estimated residential mesh and estimated workplace mesh. Here, a plurality of frequent visit meshes may be determined in (a3) ​​above.

[0044] Furthermore, regarding the frequency of stay information in (b) above, (b1) Determine the stay time in each of the estimated residence mesh, estimated workplace mesh, and frequent visit mesh for a predetermined period (for example, one month); (b2) Calculate the proportion of each determined stay time (to the total stay time), and use this proportion as the "frequency coefficient" W for each of the estimated residence mesh, estimated workplace mesh, and frequent stay mesh. j (j=1, 2, . . . , NP, where NP is the total number of grids visited in (a1) to (a3) ​​above), (b3) These "frequency coefficients" W j can be the stay frequency information for the estimated residence mesh, the estimated workplace mesh, and the frequent stay mesh, respectively.

[0045] For example, the frequency coefficient W1 of the estimated residence mesh is 0.432 (W1 = 0.432), the frequency coefficient W2 of the estimated workplace mesh is 0.411 (W2 = 0.411), the frequency coefficient W3 of the first frequent visit mesh is 0.102 (W3 = 0.102), and the frequency coefficient W4 of the second frequent visit mesh is 0.055 (W4 = 0.055). In this specific example, NP = 4, and the sum of W1 to W4 is 1.000. In this way, the frequency coefficients (W1, W2, W3, W4) act as weights when calculating the risk index later.

[0046] The "stay time" in a certain mesh mentioned above may be the length of a time interval in which the location is continuously within the certain mesh in, for example, GPS positioning information (or location registration information), which is time-series information of the location. Alternatively, it may be the length of a time interval formed by consecutive stay times at which it is determined that "a person is staying within this certain mesh" by a known stay determination technology using GPS positioning information (or location registration information). Here, as an example of such stay determination technology, see, for example, non-patent literature: Ishii Ryoji, Suenari Koji, Ochi Kengo, Seki Nobuo, Otsuka Kenta, Sakai Yukiteru, Aida Yuma, Minamikawa Atsunobu, "Verification of Reliability of Mobile Phone GPS Big Data for Use in the Urban Transportation Field," Civil Engineering Planning Research and Lecture Collection, Vol. 58, CD-ROM,<https: / / jglobal.jst.go.jp / en / detail?JGLOBAL_ID=201902291149555895> The technology described in 2018 can be adopted.

[0047] <First Zone Risk Determination Method> Also in the functional block diagram of FIG. 1, in this embodiment, the first area risk determination unit 112 determines the area exposure degree WF to natural disasters (hazardous events) in the determined stay mesh j (j=1, 2, . . . , NP) based on statistical data (data related to the occurrence of hazardous events) on natural disasters that have occurred in each prefecture that are published by the country, local governments, predetermined organizations, etc. jIn this embodiment, the statistical data used is the statistical data received from the local government disaster information management server 4 (via the communication interface unit 101 and the communication control unit 121) and stored and managed in the local government disaster information storage unit 103. Hereinafter, the area exposure degree WF in this embodiment will be described with reference to FIG. 2. j The calculation of is explained below.

[0048] FIG. 2 is a table for explaining an embodiment of the area exposure degree calculation process according to the present invention.

[0049] As shown in FIG. 2, in this embodiment, first, six types of natural disasters (hazardous events) are set as disaster types i: "earthquake," "tsunami," "storm surge," "landslide," "volcanic disaster," and "flood." For each of these disaster types i (i=1, 2, . . . , 6), (a) Exposure variable B for each stay mesh j (j=1, 2, , NP) i,j , and (b) Number of disasters N occurring in a given period in the past for each stay mesh j (j = 1, 2, . . . , NP) i,j Calculate.

[0050] Here, if the disaster type i is "earthquake", and the stay mesh j is included in the "area that may be exposed to an earthquake of seismic intensity 6 or more within 30 years" on the seismic hazard map (for example, J-SHIS probabilistic seismic hazard map), the exposure variable B i,j The value of is set to 1, and if not included, it is set to 0. In addition, the number of earthquakes with a seismic intensity of 6 or more that occurred in a specified period in the past (for example, the past 30 years) in the prefecture that includes the stay mesh j is used as the disaster occurrence count N i,j The value is

[0051] In addition, if the disaster type i is "tsunami", and the stay mesh j is included in the "area below 3m above sea level" in the national land digital information, the exposure variable B i,jThe value of is set to 1, and if not included, it is set to 0. In addition, the number of tsunamis that occurred in a prefecture that includes the stay mesh j during a specified period in the past (for example, the past 30 years) is calculated as the number of disaster occurrences N i,j The value is

[0052] Furthermore, if the disaster type i is "storm surge," and the stay mesh j is included in the "area below 3m above sea level" in the National Land Numerical Information, the exposure variable B i,j If the value of is 1, otherwise it is 0. In addition, the number of storm surges that occurred in a prefecture that includes the stay mesh j during a specified period in the past (for example, the past 30 years) is calculated as the number of disaster occurrences N i,j The value is

[0053] In addition, if the disaster type i is "landslide", and the stay mesh j is included in the "landslide warning area" in the national land digital information, the exposure variable B i,j If the value of is 1, if it is not included, it is 0. In addition, the number of landslides that occurred in a prefecture that includes the stay mesh j during a specified period in the past (for example, the past 30 years) is calculated as the number of disaster occurrences N i,j The value is

[0054] Furthermore, if the disaster type i is a "volcanic disaster," and the mesh j is included in a "volcanic area" (set by the country or a designated organization), the exposure variable B i,j If the value of is 1, if it is not included, it is 0. In addition, the number of volcanic disasters that occurred in the prefecture that includes the stay mesh j during a specified period in the past (for example, the past 30 years) is calculated as the number of disaster occurrences N i,j The value is

[0055] In addition, if the disaster type i is "flood", and the stay mesh j is included in the "expected flood area" in the national land digital information, the exposure variable B i,j If the value of is 1, if it is not included, it is 0. In addition, the number of floods that occurred in a prefecture that includes the stay mesh j during a specified period in the past (for example, the past 30 years) is calculated as the number of disaster occurrences N i,j The value is

[0056] Next, the first area risk determination unit 112 calculates the determined number of disaster occurrences N i,j (i=1, 2, , 6, j=1, 2, , NP), the frequency of occurrence of disaster of disaster type i in stay mesh j is calculated as the disaster occurrence frequency F i,j to the following equation (1) F i,j =1-exp(-N i,j / avN i ) where avN i is the average number of disasters of disaster type i that occurred in each prefecture over a specified period of time (for example, the past 30 years). By adopting the form of the above formula (1), the disaster occurrence frequency F i,j can be a quantity that takes on a value between 0 and 1.

[0057] Furthermore, the first area risk determination unit 112 calculates the calculated disaster occurrence frequency F i,j and the determined exposure variable B i,j Using this, the area exposure degree WF j to the following equation (2) WF j =W j Σ i B i,j ×F i,j Here, Σ i is the sum for disaster type i (i = 1, 2, . . . , 6).

[0058] In this way, the first area risk determination unit 112 determines the (W j , B i,j or F i,j (depending on the area exposure WF j Naturally, the setting of disaster type i is not limited to the above. For example, if only "earthquake" and "tsunami" are set, the area exposure level WF j Furthermore, it is also possible to adopt disaster types other than the six mentioned above.

[0059] <Second Zone Risk Determination Method> Returning to the functional block diagram of FIG. 1, in this embodiment, the second area risk determination unit 113 determines the area robustness G (Gross National Safety for natural disasters) against natural disasters (hazardous events) in the determined stay mesh j (j=1, 2, . . . , NP) based on disaster response data related to measures, responses, preparations, or awareness of risks published by the country, local governments, or predetermined organizations, for example, the safety index GNS (Gross National Safety for natural disasters) data (by prefecture or by city, town, or village) developed by the Kanto branch of the Geotechnical Society of Japan. j Determine.

[0060] In this embodiment, the GNS data received from the disaster indicator information management server 5 (via the communication interface unit 101 and the communication control unit 121) and stored and managed in the disaster indicator information storage unit 104 is used as the disaster response data. j The calculation of is explained below.

[0061] FIG. 3 is a table for explaining an embodiment of the process for calculating the area robustness according to the present invention.

[0062] As shown in FIG. 3, in this embodiment, six types of sub-indicators (classification items) of the area robustness are set first: "Buildings," "Lifelines," "Infrastructure," "Supplies and Stockpiles," "Medical Services," and "Regulations and Autonomy." For each type of these sub-indicators (classification items), (a) Sub-index G for each stay mesh j (j = 1, 2, , NP) k,j Here, k is an identifier and subscript for the sub-indicator items such as "** index" and "** number" adopted for each type of sub-indicator. This k takes an integer value from 1 to the total number of "** index" and "** number" adopted (total number of sub-indicator items), K (k = 1, 2, ..., K).

[0063] If the type of sub-indicator is "buildings," the earthquake resistance rate of buildings (obtained by referring to GNS data) in the prefecture (or municipality) that includes stay mesh j is used as the sub-indicator G k,j In addition, if the type of sub-indicator is "lifeline," the earthquake resistance rate of water supply pipelines, water purification facilities, water reservoirs, etc. (obtained by referring to GNS data) in the prefecture (or municipality) that includes stay mesh j is used as the sub-indicator G k,j The value is

[0064] Furthermore, if the type of sub-indicator is "infrastructure," at least one of the road index and the number of bridge repairs (obtained by referring to GNS data) in the prefecture (or municipality) containing the stay mesh j is used as the sub-indicator G k,j In addition, if the type of sub-indicator is "supplies and stockpiles," at least one of the following (obtained by referring to GNS data) in the prefecture (or municipality) that includes stay mesh j: the number of food stockpiles (5 items), the number of drinking water stockpiles, the number of blanket stockpiles, the supermarket index, and the convenience store index is used as the sub-indicator G k,j The value is

[0065] Furthermore, if the type of sub-indicator is "medical services," at least one of the number of doctors per 100,000 people and the number of hospital beds per 100,000 people (obtained by referring to GNS data) in the prefecture (or municipality) that includes mesh j is used as the sub-indicator G. k,j In addition, if the type of sub-indicator is "ordinance / autonomy," at least one of the following (obtained by referring to GNS data) in the prefecture (or municipality) that includes stay mesh j, the rate of landslide disaster warning area designation, the rate of hazard map publication, and the rate of voluntary disaster prevention organization coverage is used as the sub-indicator G k,j The value is

[0066] Next, the second area risk determination unit 113 determines the determined sub-index G k,j (k=1, 2, , K), and calculate the area robustness G j to the following equation (3) G j =W j Σ k ((Gk,j -avG k ) / VG k ) Here, Σ k is the sum for sub-index item k (k=1, 2, . . . , K). k and VG k are the average value and unbiased variance across prefectures (or municipalities) for the value of the sub-indicator k (such as the above index or number). k,j -avG k ) / VG k ) is the sub-indicator G k,j The Z score indicates the degree of deviation from the average of the sub-indicator G, which can take on various scales. k,j , the regional robustness G j It has been standardized, so to speak, to appropriately reflect the above.

[0067] In this way, the second area risk determination unit 113 determines the (W j and G k,j (depending on the area robustness G j It is possible to determine the sub-index G k,j The content of the sub-index item k is, of course, not limited to those mentioned above. For example, it is also possible to adopt the earthquake resistance rate of gas piping for "lifelines." In any case, the sub-index G k,j is set so that the larger the value, the higher the degree of robustness.

[0068] <Method for determining target risk> Returning to the functional block diagram of Figure 1, the target risk determination unit 114 determines the target robustness A of the target individual against natural disasters (hazardous events) based on data related to matters that may work to reduce the degree of risk to the target individual, which in this embodiment is based on the results of a questionnaire survey of the target individual and the user registration information of the target individual.

[0069] In this embodiment, the above-mentioned questionnaire survey results and user registration information are the results of a questionnaire survey conducted on the terminal 2 owned by the target individual, and the questionnaire survey results and user registration information received (via the communication interface unit 101 and the communication control unit 121) from the questionnaire and registration information management server 6 that manages the user registration information of the target individual and stored and managed in the target information storage unit 105. The calculation of the target robustness A in this embodiment will be described below with reference to Figure 4.

[0070] FIG. 4 is a table for explaining an embodiment of the calculation process of the object robustness according to the present invention.

[0071] As shown in FIG. 4, in this embodiment, first, seven types of sub-indices (classification items) of the subject robustness are set: "Means of transportation," "Means of communication," "Economic situation," "Family composition," "Health condition," "Insurance," and "Information gathering." For each type of these sub-indices (classification items), Sub-indicator A for target individuals m Here, m is an identifier and subscript for the sub-indicator items, such as "presence or absence of **" or "number of **" adopted for each type of sub-indicator. This m takes an integer value from 1 to M, the total number of "presence or absence of **" or "number of **" adopted (total number of sub-indicator items) (m=1, 2, . . . , M). Naturally, the types of sub-indicators (classification items) are not limited to those mentioned above; for example, it is also possible to adopt "stockpiling status" or "disaster prevention drill participation status," etc.

[0072] Here, if the type of sub-indicator is "Means of transportation," the presence or absence of a private car (obtained from the questionnaire survey results) of the target individual (for example, 1 if yes, 0 if no) is used as sub-indicator A. m In addition, if the type of sub-indicator is "communication means," the presence or absence of a designated communication means (mobile phone, optical fiber line, Wi-Fi (registered trademark), disaster prevention radio, etc.) of the target individual (obtained from the questionnaire survey results), or the number of designated communication means owned, will be the value of sub-indicator A. m The value is

[0073] Furthermore, if the type of sub-indicator is "economic situation," the annual household income (obtained from the questionnaire survey results) of the target individual (for example, a numerical value assigned to each predetermined annual income category) is used as sub-indicator A. m If the sub-indicator type is "family structure," the number of people in the household (obtained from the questionnaire survey results) of the target individual is used as the value of sub-indicator A. m Furthermore, if the sub-indicator type is "health status," the health checkup results (obtained from the questionnaire survey results) of the target individual (for example, evaluations A to E are set to values ​​of 5 to 1, respectively) are used as the sub-indicator A. m The value is

[0074] In addition, if the type of sub-indicator is "insurance," the presence or absence of specified insurance (earthquake insurance, flood insurance, fire insurance, etc.) of the target individual (obtained from the questionnaire survey results), or the number of specified insurance policies they have, is used as sub-indicator A. m Furthermore, if the sub-indicator type is "information gathering," the frequency of viewing disaster prevention information (obtained from the questionnaire survey results) by the target individual (for example, the total frequency of viewing through designated information gathering means such as television, websites, and official SNS) is used as the value of sub-indicator A. m The value is

[0075] Next, the target risk determination unit 114 determines the determined sub-index A m (m=1, 2, , M), the target robustness A of the target individual is calculated using the following formula: (4) A=Σ m ((A m -avA m ) / VA m ) Here, Σ m is the sum of the sub-index items m (m=1, 2, . . . , M). m and VA m are the values ​​of the sub-indicator items m (such as the above "presence or absence of **" and "number of **") and the sub-indicator A (obtained by a questionnaire survey of many individuals). m The mean and unbiased variance of ((A m -avA m ) / VAm ) is sub-indicator A m The Z score indicates the degree of deviation from the average of the A m is standardized so as to appropriately reflect the target robustness A.

[0076] In this way, the target risk determination unit 114 determines whether the target individual is unique (A m The target robustness index A (which depends on the sub-index A) can be determined. m The content of the sub-indicator item m is of course not limited to those mentioned above. For example, it is possible to use the size of the private car (for example, the number of people that can ride and the amount of luggage that can be carried) for "means of transportation." In any case, the sub-indicator A m is an index that depends only on the target individual and is not dependent on the place of stay, and is set as an index whose degree of robustness increases as its value increases.

[0077] <Means for determining risk index> Returning to the functional block diagram of FIG. 1, in this embodiment, the risk index determination unit 115: (a) The frequency coefficient W determined for each stay mesh j (j = 1, 2, , NP) of the target individual j , area exposure degree WF j , and regional robustness G j and, (b) the target robustness A determined for the target individual; Using the above, the risk index R for the target individual is calculated using the following formula: (5) R=Σ j WF j -Σ j G j -A Here, Σ j is the sum over j.

[0078] In this way, the risk index determination unit 115 can determine a risk index R that is specialized for the target individual, unlike conventional risk assessment indices for specific regions, such as safety indices GNS that are determined by prefecture or city / town / village.

[0079] Here, in order to contribute to a suitable presentation (display) of the risk index R, which will be described later, the risk index determination unit 115 in this embodiment calculates the risk index R by the following formula: (6) V=Σ j G j +A It is also preferable to calculate a robustness index V for the target individual using the above formula. The robustness index V is a comprehensive evaluation of the target individual's level of robustness against natural disasters (hazardous events), and is a useful index that should be presented to the target individual. Using this robustness index V, the risk index R can be calculated using the following formula: (7) R=Σ j WF j -V It is also possible to express (calculate) it as follows. Here, as will be explained later, Σ j WF j is the "exposure index," the risk index R is the exposure index Σ j WF j It can be said that it is determined by the robustness index V.

[0080] As a modification of the risk index R, the risk index R can be calculated by the following equations (8) to (12): (8) R=Σ j WF j (9) R=-Σ j G j (10) R=Σ j WF j -Σ j G j =Σ j (WF j -G j ) (11) R=Σ j WF j -A (12) R=-Σ j G j -A=-V Any of the risk indices R is calculated by combining information for each stay mesh (stay area) j specific to the target individual, and is suitable as a risk assessment index for natural disasters (hazardous events) for the target individual.

[0081] As a further modification, the risk index R is different from the above formula (5), formula (7), and formulas (8) to (12), and is expressed as Σ j WF j and Σ j G j , A and V. For example, the following formula (13) R=Σ j WF j / (Σ j G j +A)=Σ j WF j / V Furthermore, the risk index R can be calculated by Σ j WF j is a monotonically increasing function with respect to Σ j G j Any other form may be used as long as it is a monotonically decreasing function of A and V. In any case, it can be seen that the larger the risk index R, the higher the risk (degree of risk) of the individual to a natural disaster (hazardous event).

[0082] <Display control means, proposal information generation means> Similarly, in the functional block diagram of FIG. 1, in this embodiment, the display control unit 116 converts the risk index R of the target individual determined by the risk index determination unit 115 into an exposure index Σ j WF j and robustness index V, and exposure index Σ j WF j and the robustness index V, display data for displaying the risk index R of the target individual is generated and output to the UI unit 106 equipped with a display.

[0083] Furthermore, in this embodiment, the proposed information generation unit 117 generates proposed information for further reducing the risk index R of the target individual determined by the risk index determination unit 115, and outputs this proposed information to the display control unit 116, which then adds this proposed information to the display data. A specific example of the display result of the generated display data will be described below with reference to FIG. 5.

[0084] FIG. 5 is a graph for explaining a specific example of the results of displaying the contents of the risk index display data according to the present invention.

[0085] In the specific example shown in FIG. 5(A), the horizontal axis represents the exposure index Σ j WF j (Area exposure level WF j ) and the vertical axis is the robustness index V (regional robustness G j and target robustness A), and a risk index graph showing the risk index R as coordinate point R. In this specific example, as will be shown later, the number of stay meshes NP is 3 (NP = 3).

[0086] In this risk index graph, the distance from the origin of the graph to coordinate point (risk index) R is as follows: (a) A vertical arrow corresponding to the magnitude of the target robustness A, (b) Diagonal arrows representing the magnitude of the area exposure WF1 and area robustness G1 in the estimated residential mesh (stay mesh (j=1)), and (c) Diagonal arrows representing the magnitude of the area exposure WF2 and area robustness G2 in the estimated workplace mesh (stay mesh (j=2)), and (d) Diagonal arrows representing the magnitude of the area exposure WF3 and area robustness G3 in the frequent stay mesh (stay mesh (j = 3)) The size of each "index part" of the risk index R is expressed in an easy-to-understand manner.

[0087] That is, in this specific example, the display control unit 116 (FIG. 1) j WF j(b) The size of the robustness index V is divided into the size of the index portion related to each stay mesh j (WF1, WF2, WF3 in Figure 5(A)) and displayed, and (b) the size of the robustness index V is divided into the size of the index portion related to each stay mesh j (G1, G2, G3 in Figure 5(A)) and the size of the index portion (A) related to the target robustness A.

[0088] By displaying (visualizing) the contents of such display data (risk index graph data), an individual can, for example, grasp at a glance the extent of their own risk to natural disasters and which of their own risk factors has the greatest impact on their own risk.

[0089] In this specific example, the proposal information generating unit 117 (FIG. 1) (a) Exposure index Σ j WF j The index part of the frequency coefficient W (WF1, WF2, WF3 in FIG. 5(A)) is large enough (for example, the largest) to satisfy a predetermined condition. j If the size of the indicator portion (WF2 in FIG. 5A) for the stay mesh j related to (W2 in this specific example) is large enough to satisfy a predetermined condition (for example, exceeds a predetermined threshold WF_th), proposal information for reducing the size of this indicator portion (WF2), (b) The index part of the robustness index V (G1, G2, G3 in Figure 5(A)) that is large enough (for example, the largest) to satisfy the predetermined condition, and the frequency coefficient W j If the size of the indicator portion (G2 in FIG. 5A) for the stay mesh j related to (W2 in this specific example) is small enough to satisfy a predetermined condition (for example, less than a predetermined threshold G_th), suggestion information for increasing the size of this indicator portion (G2), and (c) When the magnitude of the target robustness A, which is the index part of the robustness index V, is small enough to satisfy a predetermined condition (for example, less than a predetermined threshold A_th), proposal information for increasing the magnitude of this index part (A) In this specific example, at least one of the above is generated, and all of them are generated. Incidentally, it is also preferable that the proposal information generation unit 117 prepares in advance proposal information templates corresponding to the above proposal patterns (a) to (c), and generates proposal information by reading out the corresponding template as appropriate in accordance with the above conditions (a) to (c).

[0090] Here, the above-mentioned (a) suggestion information can be, for example, information recommending reducing unnecessary stays at the workplace (j=2) when the frequency coefficient W2 is the largest. Also, when the frequency coefficient W3 is the largest, it can be information recommending reducing the frequency of visits to a frequently visited facility (j=3) or the time spent at this facility. Furthermore, when the frequency coefficient W1 is the largest, it can be information recommending moving to an area with a lower disaster risk (for example, at a specified opportunity).

[0091] Furthermore, the above-mentioned (b) suggested information may be, for example, information to further promote stockpiling of supplies as disaster countermeasures at the workplace (j=2) in response to the fact that the frequency coefficient W2 is the largest. Furthermore, although the frequency coefficient W1 is the largest, it may also be information to promote earthquake resistance at the home (j=1). Furthermore, the above-mentioned (c) suggested information may be, for example, information to recommend taking out designated insurance (in response to the fact that the designated disaster insurance is not yet enrolled).

[0092] In this specific example, the suggested information (a) to (c) above is preferably displayed as, for example, a balloon text while highlighting the corresponding indicator portion in the risk index graph, which allows, for example, a target individual to understand at a glance what they should do to effectively reduce their own risk of natural disasters.

[0093] Furthermore, in this specific example, as shown in FIG. 5(A), the display control unit 116 (FIG. 1) can generate display data for displaying the risk index R of the target individual and its allowable range (region I in FIG. 5(A)) so that the allowable range (region I in FIG. 5(A)) of the set risk index R is clearly shown in the risk index graph, and display it on the UI unit 106.

[0094] More specifically, in FIG. 5(A), a graph of R = Ra (Ra is a preset allowable upper limit value) (the dashed line in FIG. 5(A)) is superimposed and displayed on the risk index graph, and the upper side (the side where V is larger) of the dashed line graph indicating R = Ra is displayed as region I (the allowable range where R < Ra). Conversely, the lower side of the dashed line graph is displayed as region II (the non-allowable range where R > Ra). Thereby, for example, the target individual can grasp at a glance whether his / her risk index R is within the set allowable range, if it is within the non-allowable range, to what extent it deviates from the allowable range, and furthermore, what the index part (the insufficient part as a risk countermeasure) that is strongly affected by the deviation is.

[0095] Furthermore, it is also preferable that the proposed information in (a) to (c) above includes specific proposals that can move the risk index R of the target individual from region II (non-allowable range) to region I (allowable range). Also in this case, for example, together with the proposed information, the position within region I (the allowable range where R < Ra) of the risk index R that would be realized if the proposed content is implemented may be clearly shown in the risk index graph.

[0096] Incidentally, the above Ra value may be, for example, an empirical value considered necessary to keep the damage caused by natural disasters within a predetermined range based on past disaster examples, or it may also be a set value determined based on the average value of the risk index R calculated for a large number of target individuals.

[0097] In the specific example shown in FIG. 5(B), the display data generated by the display control unit 116 is displayed on the UI unit 106 as a display result, and the risk index (WF j -G j )(or (WF j -G j -A) or (maybe A)) is also displayed. The 10 grids are arranged in order from A to J, with the frequency of stay W j has become a major factor.

[0098] In this example (Fig. 5(B)), for the top three stay meshes C, D, and A in terms of the "percentage" mentioned above, their stay frequency W j Regardless of the size of the risk index (WF j -G j ) is generated and presented. On the other hand, for the stay mesh B (the sixth one) which is not in the top three in terms of "ratio", its stay frequency W j Although the second largest, in this example, no suggestion information is generated (or suggestion information with less important content is generated). j -G j By generating and presenting proposal information that focuses on the magnitude of the risk index R of the target individual, it is possible to effectively reduce the risk index R of the target individual.

[0099] Furthermore, in the specific example shown in FIG. 5C, the robustness index V (V=Σ j G j +A) as sub-index G j,k (Sub-indicator items are set to one for each type) and sub-indicator A m A radar chart showing the values ​​for each type (sub-indicator items are set to one for each type) is displayed. k,j , A m ) for (a) "Target values" determined based on the values ​​of standard disaster indicators (such as the GNS index) and the results of nationwide questionnaire surveys, (b) The "calculated value" for the target individual determined by the second area risk determination unit 113 (FIG. 1) and the target risk determination unit 114 (FIG. 1) are shown overlapping each other for ease of comparison. In this example, the value of j is set to 1 (j=1). That is, the sub-index G k,j Both the "target value" and "calculated value" are values ​​for the home (estimated residential mesh).

[0100] In this specific example (Fig. 5(C)), it can be seen at a glance that the calculated value for "Supplies and Stockpiles" is the one that is most significantly below the target value. In response to this, it is also preferable to generate and present proposal information that recommends taking measures related to supplies and stockpiles at home. Furthermore, since the calculated values ​​for "Insurance" and "Information Gathering" are also significantly below the target values, it is also possible to generate and present proposal information that recommends reviewing insurance coverage or that displays a list of disaster websites set up by local governments. In this way, the calculated values ​​(G k,j , A m ) in a way that makes it easy to compare it with the target value, and by presenting suggested information generated based on the difference between the calculated value and the target value, it is also possible to effectively increase the robustness index V of the target individual.

[0101] Three specific examples (FIGS. 5(A) to (C)) of the display (visualization) of the contents of the display data (including the proposed information) generated by the display control unit 116 have been described above. The displays in these specific examples may be performed simultaneously on one screen, or may be switchable between them. Furthermore, in these specific examples, the risk index R (=Σ j WF j -Σ j G j -A=Σ j WF j-V). By continuously displaying monthly data in the same way, it is quite possible that the results of the target individual's behavioral change, which was encouraged by the previous month's display, will be significantly reflected in the display of this month's risk index R. In this case, the target individual can see at a glance the effect of their own behavioral change, and it is also possible to maintain and improve their motivation to take risk countermeasures.

[0102] The information on the risk index R determined by the risk index determination unit 115 and the display data (including the suggested information) generated by the display control unit 116 may be transmitted to an external information processing device, for example, a terminal 2 possessed by a target individual, via the communication control unit 121 and the communication interface unit 101, and may be used in that device. For example, a display similar to the above specific example (FIGS. 5(A) to (C)) may be displayed on the screen of this terminal 2.

[0103] As explained above in detail, according to the present invention, unlike the risk assessment indexes for risk events (e.g., natural disasters) for specific regions (e.g., countries or prefectures) that have been proposed in the past, it is possible to determine a risk index specialized for an evaluation target (e.g., a target individual). Furthermore, by using this determined risk index, it is possible to promote measures for the evaluation target (target individual) in addition to measures at the national or prefectural level, for example, to encourage behavioral changes in the evaluation target (target individual).

[0104] Furthermore, in order to maintain and develop the safe and healthy lives of each resident in a given area, the present invention can grasp the risk index for each resident for various risk events and use this risk index to implement individual countermeasures or provide individual advice. It is also possible to prioritize effective countermeasures for specific areas within a given area. In other words, the present invention can contribute to Goal 3 "Ensure healthy lives and promote well-being for all at all ages" and Goal 11 "Make cities inclusive, safe, resilient and sustainable" of the United Nations' Sustainable Development Goals (SDGs).

[0105] With respect to the various embodiments of the present invention described above, various changes, modifications, and omissions that fall within the scope of the technical spirit and aspects of the present invention may be easily made by those skilled in the art. The above description is merely illustrative and is not intended to be limiting in any way. The present invention is limited only by the scope of the claims and their equivalents. [Explanation of symbols]

[0106] 1. Risk estimation device 101 Communication interface unit 102 User positioning information storage unit 103 Local government disaster information storage department 104 Disaster Indicator Information Storage Department 105 Target information storage unit 106 User Interface (UI) Section 111 Frequency information determination unit 112 Area 1 Risk Determination Department 113 Area 2 Risk Determination Division 114 Target Risk Determination Department 115 Risk Index Determination Unit 116 Display control unit 117 Proposal information generation section 121 Communication control unit 2. Devices 3. Positioning information management server 4. Local government disaster information management server 5. Disaster indicator information management server 6. Survey and registration information management server

Claims

1. A risk estimation program for estimating a risk index for a predetermined risk event, a frequency information determination means for determining, from among a plurality of zones included in a predetermined region, a zone of stay for which information relating to the stay of the subject satisfies a predetermined condition based on the acquired information relating to the location of the subject, and for each of the determined zones of stay, determining stay frequency information relating to the frequency at which the subject stays in the zone of stay; an area risk determination means for determining the degree of exposure to the hazardous event in the area of ​​stay based on the acquired data on the occurrence of the hazardous event, and / or for determining the degree of robustness to the hazardous event in the area of ​​stay based on the acquired index data for the area of ​​stay published by the national government, a local government, or a specified organization, which index data shows a numerical value indicating the degree of countermeasures, responses, preparations, or awareness for at least one classification item related to countermeasures, responses, preparations, or awareness for the hazardous event, using the numerical value; a risk index determination means for determining the risk index of the subject using the stay frequency information of the stay area, the degree of exposure of the stay area, and / or the degree of robustness of the stay area; A risk estimation program characterized by causing a computer to function as follows.

2. further causing the computer to function as a target risk determination means for determining the degree of robustness of the target against the risk event based on the acquired data relating to matters that can work in the direction of reducing the degree of risk in the target; The risk index determining means determines the risk index of the object by also using the degree of robustness of the object.

2. The risk estimation program according to claim 1, wherein:

3. The area risk determination means determines the degree of exposure of the stay area and the degree of robustness of the stay area; The risk index determination means determines a risk index of the subject using the stay frequency information and the degree of exposure for each determined stay area, the stay frequency information and the degree of robustness for each determined stay area, and the degree of robustness of the subject; The computer further functions as a display control means for dividing the risk index of the target into an exposure index, which is an index portion determined using the stay frequency information of the stay area and the degree of exposure, and a robustness index, which is an index portion determined using the stay frequency information of the stay area and the degree of robustness, and the degree of robustness of the target, and for generating display data for displaying the risk index of the target so that the magnitudes of the exposure index and the robustness index can be distinguished.

3. The risk estimation program according to claim 2.

4. The risk estimation program according to claim 3, characterized in that the display control means generates the display data for displaying the size of the exposure index divided into the size of the index portion related to each determined area of ​​stay, and for displaying the size of the robustness index divided into the size of the index portion related to each determined area of ​​stay and the size of the index portion related to the degree of robustness of the subject.

5. The risk estimation program according to claim 3 or 4, characterized in that the display control means generates display data for displaying the risk index of the target and the tolerance range so that the tolerance range of the set risk index is clearly indicated in a graph of the risk index of the target having an axis of the exposure index and an axis of the robustness index.

6. The area risk determination means determines the degree of exposure of the stay area and the degree of robustness of the stay area; The risk index determination means determines a risk index of the subject using the stay frequency information and the degree of exposure for each determined stay area, the stay frequency information and the degree of robustness for each determined stay area, and the degree of robustness of the subject; The risk index of the target is divided into an exposure index, which is an index portion determined using the stay frequency information of the stay area and the degree of exposure of the stay area, and a robustness index, which is an index portion determined using the stay frequency information of the stay area and the degree of robustness of the stay area, and the degree of robustness of the target, (a) when the size of the index portion of the exposure index relating to the area of ​​stay associated with stay frequency information relating to a frequency high enough to satisfy a predetermined condition is large enough to satisfy the predetermined condition, generate proposal information for reducing the size of the index portion; (b) when the size of the index portion of the robustness index relating to the area of ​​stay associated with stay frequency information relating to a frequency high enough to satisfy the predetermined condition is small enough to satisfy the predetermined condition, generate proposal information for increasing the size of the index portion; and / or (c) when the size of the index portion relating to the degree of robustness of the subject in the robustness index is small enough to satisfy the predetermined condition, generate proposal information for increasing the size of the index portion.

5. The risk estimation program according to claim 2, wherein the risk estimation program is a program for estimating a risk of a malfunction.

7. The risk estimation program according to any one of claims 1 to 4, characterized in that the frequency information determination means determines the area of ​​stay of the object based on information relating to the location of the object obtained from a positioning means related to a terminal attached to the object or related to communication of the terminal.

8. The risk estimation program according to any one of claims 1 to 4, characterized in that the frequency information determination means determines the area of ​​stay of the subject, including the residential area of ​​the subject's residence, the workplace area of ​​the subject's workplace, and a frequent stay area where the subject stays frequently enough to satisfy specified conditions, based on the location information of the subject.

9. the predetermined hazardous event is set to be a hazardous event including at least one of an earthquake, a tsunami, a storm surge, a landslide, a volcanic disaster, and a flood; the area risk determination means determines, for each set risk event, a degree of exposure to the risk event in the stay area; The risk index determining means determines a risk index of the target using information on the frequency of stay in the area and the degree of exposure to each of the risk events in the area.

5. The risk estimation program according to claim 1, wherein the risk estimation program is a program for estimating a risk of a malfunction.

10. The index data is index data showing a numerical value indicating the degree of measures, responses, preparations or awareness regarding the hazardous event for each set classification item including at least one of the classification items of buildings, lifelines, infrastructure, supplies and stockpiles, medical services, and ordinances and local government, the area risk determination means determines a degree of robustness of the stay area for each of the set classification items; The risk index determination means determines a risk index of the target using information on the frequency of stay of the area and the degree of robustness of the area for each of the set classification items.

5. The risk estimation program according to claim 1, wherein the risk estimation program is a program for estimating a risk of a malfunction.

11. The items that can work to reduce the degree of risk are set items including at least one of means of transportation, means of communication, economic situation, family structure, health condition, insurance, and information gathering, The target risk determination means determines the degree of robustness of the target for each of the set items, The risk index determining means determines a risk index of the object by using the degree of robustness of the object for each of the set items.

5. The risk estimation program according to claim 2, wherein the risk estimation program is a program for estimating a risk of a malfunction.

12. A risk estimation device that estimates a risk index for a predetermined risk event, a frequency information determination means for determining, from among a plurality of zones included in a predetermined region, a zone of stay for which information relating to the stay of the subject satisfies a predetermined condition based on the acquired information relating to the location of the subject, and for each of the determined zones of stay, determining stay frequency information relating to the frequency at which the subject stays in the zone of stay; an area risk determination means for determining the degree of exposure to the hazardous event in the area of ​​stay based on the acquired data on the occurrence of the hazardous event, and / or for determining the degree of robustness to the hazardous event in the area of ​​stay based on the acquired index data for the area of ​​stay published by the national government, a local government, or a specified organization, which index data shows a numerical value indicating the degree of countermeasures, responses, preparations, or awareness for at least one classification item related to countermeasures, responses, preparations, or awareness for the hazardous event, using the numerical value; a risk index determination means for determining the risk index of the subject using the stay frequency information of the stay area, the degree of exposure of the stay area, and / or the degree of robustness of the stay area; A risk estimation device comprising:

13. A risk estimation system that estimates a risk index for a predetermined risk event, a frequency information determination means for determining, from among a plurality of zones included in a predetermined region, a zone of stay for which information relating to the stay of the subject satisfies a predetermined condition based on the acquired information relating to the location of the subject, and for each of the determined zones of stay, determining stay frequency information relating to the frequency at which the subject stays in the zone of stay; an area risk determination means for determining the degree of exposure to the hazardous event in the area of ​​stay based on the acquired data on the occurrence of the hazardous event, and / or for determining the degree of robustness to the hazardous event in the area of ​​stay based on the acquired index data for the area of ​​stay published by the national government, a local government, or a specified organization, which index data shows a numerical value indicating the degree of countermeasures, responses, preparations, or awareness for at least one classification item related to countermeasures, responses, preparations, or awareness for the hazardous event, using the numerical value; a risk index determination means for determining the risk index of the subject using the stay frequency information of the stay area, the degree of exposure of the stay area, and / or the degree of robustness of the stay area; A risk estimation system comprising:

14. A risk estimation method for estimating a risk index for a predetermined risk event, comprising: determining, from among a plurality of zones included in a predetermined region, a zone of stay for which information related to the stay of the subject satisfies a predetermined condition based on the acquired information related to the location of the subject, and determining, for each determined zone of stay, stay frequency information related to the frequency with which the subject stays in the zone of stay; determining the degree of exposure to the hazardous event in the area of ​​stay based on the acquired data on the occurrence of the hazardous event, and / or determining the degree of robustness to the hazardous event in the area of ​​stay based on the acquired index data for the area of ​​stay published by the national government, local government, or a specified organization, which index data shows a numerical value indicating the degree of countermeasures, responses, preparations, or awareness for at least one classification item related to countermeasures, responses, preparations, or awareness for the hazardous event, using the numerical value; determining the risk index of the subject using the frequency of stay information of the area of ​​stay, and the degree of exposure of the area of ​​stay and / or the degree of robustness of the area of ​​stay; A computer-implemented risk estimation method comprising:

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