JP7570911B2 - Risk information provision system and risk information provision method - Google Patents

Description

An embodiment of the present invention relates to a risk information providing system and a risk information providing method for providing information related to disaster prediction.

In recent years, there have been long-standing concerns about the increasing severity of disasters due to the effects of climate change and the expansion of the scale of damage caused by unexpected disasters. Providing disaster information plays an important role in ensuring the safety and security of society.

A method has been proposed to provide information on disaster phenomena that progress while gradually changing time and location, based on location information and time-series information (see, for example, Patent Document 1).

On the other hand, there are constant complaints that even if disaster warning information is widely disseminated through public mass media, it does not reach all victims.

One of the reasons for this is thought to be that potential victims do not necessarily perceive disaster information as something that concerns them personally (in behavioral economics and sociology, this is known as normalcy bias or status quo bias).

Therefore, methods of organizing information and providing evacuation support information that are tailored to the perspective of the recipients of various types of information during disaster response have been proposed (see, for example, Patent Documents 2 and 3).

JP 2015-032886 A JP 2015-210681 A JP 2016-224755 A

Previous proposals aimed to disseminate information about the likely occurrence of a disaster or damage caused by a disaster as early as possible. However, there has been an increase in cases where it is difficult to secure lead time for evacuation, such as in the case of extreme weather phenomena, and it has been pointed out that even if information is disseminated after an imminent crisis has occurred, it is difficult to avoid cases of damage during evacuation or problems with people being unable to escape in time.

For this reason, disaster prevention agencies have begun to shift to a policy of issuing disaster alerts "without fear of false alarms," and this is becoming more and more accepted by society.

However, there is a dilemma in that false alerts can lead to habituation and complacency on the part of those receiving the information. Furthermore, in 2020, the spread of COVID-19 has brought to light issues regarding overcrowding, close contact, and confined spaces in the operation of evacuation shelters, increasing the need for earlier and more dispersed evacuations.

The present invention has been developed in consideration of these circumstances, and aims to provide a risk information provision system and method that enable early and decentralized evacuation by inducing recipients of disaster prediction information to take "actions to protect property" in accordance with economic principles prior to "evacuation actions to protect lives."

The risk information providing system of the embodiment includes a server having a communication function with a user terminal,
The server,
A property location information acquisition means for acquiring location information of a user's property;
a cost value acquiring means for acquiring, as a cost value, a degree of burden felt by a user for measures taken by the user to prevent damage to the user's property;
a loss value acquiring means for acquiring, as a loss value, a degree of a user's sense of loss in regard to damage to the property caused by not taking the countermeasure or a disadvantage associated therewith;
A disaster prediction probability acquisition means for acquiring the probability of disaster prediction information;
a first risk information transmission means for transmitting risk information notifying the user terminal of the possibility of damage to the property when a location corresponding to the property location information acquired by the property location information acquisition means is related to the disaster prediction information and the property is likely to be damaged or the user may suffer a disadvantage related thereto, and when the probability of the disaster prediction information acquired by the disaster prediction probability acquisition means exceeds a cost loss ratio between the cost value acquired by the cost value acquisition means and the loss value acquired by the loss value acquisition means;
A risk preference value acquisition means for acquiring a risk preference value of the user;
a second risk information transmission means for transmitting risk information notifying the user terminal of the possibility of damage to the property when a location corresponding to the property location information acquired by the property location information acquisition means indicates a possibility that the property may be affected by a disaster related to the disaster prediction information or that the user may suffer a related disadvantage, and when the probability of the disaster prediction information acquired by the disaster prediction probability acquisition means is equal to or greater than the user's risk preference value acquired by the risk preference value acquisition means;
Equipped with.

FIG. 1 is a block diagram showing the overall functional configuration of a risk information providing system RIS according to an embodiment. A block diagram showing the hardware configuration common to the disaster prediction information providing server 10 included in the risk information providing system RIS, the disaster prevention server 21, the SNS server 22, the service server 23, and the SNS-ChatBot server 30 in the commercial facility server system 20. A diagram showing the general concept of the cost/loss model. FIG. 1 is a diagram showing an example of implementation of a risk information provision system RIS in a commercial facility for users who are the targets of information provision. FIG. 13 shows a questionnaire screen EG displayed on the user terminal 40 for asking the user to respond on a 5-point scale to the degree of burden of moving a vehicle as a cost value (C). FIG. 2 is a diagram showing, as a cost table TC, examples of cost values (C) corresponding to the degree of burden felt by a user, that is, how troublesome it is to move a vehicle. FIG. 2 is a diagram showing, as a loss table TL, examples of loss values (L) corresponding to the degree of loss perception of a user, that is, how bothersome it is to get wet in the rain. FIG. 13 is a diagram showing an example of user management information (user management information DB 24c) reflecting cost values (C) and loss values (L) and their cost-loss ratios (C/L) obtained by a questionnaire to users. 11 is a flowchart showing a parking location notification process of the risk information provision system RIS. 11 is a flowchart showing a rainfall risk information provision process of the risk information provision system RIS. 11 is a flowchart showing a flood risk information provision process of the risk information provision system RIS.

The following describes the embodiment of the risk information provision system and risk information provision method with reference to the drawings.

First Embodiment
FIG. 1 is a block diagram showing the overall functional configuration of a risk information providing system RIS according to an embodiment.

In this embodiment, it is assumed that information is provided to customers who visit a commercial facility by car (vehicle), and that voluntary evacuation behavior is induced in each customer according to their individual disaster-related values and risk tolerance, thereby accelerating and dispersing evacuation.

The risk information system RIS comprises a disaster prediction information server 10 provided by, for example, a local government or a company that collects and provides various types of hydrological information, a commercial facility server system 20 provided by a commercial facility installer, and a SNS (social networking site)-ChatBot server 30 that exchanges messages with user terminals 40 of customers who use the commercial facility.

The disaster prediction information providing server 10 and the commercial facility server system 20, the commercial facility server system 20 and the SNS-ChatBot server 30, and the SNS-ChatBot server 30 and the user terminal 40 are all connected for communication at any time via a communication network NW such as the Internet.

The disaster prediction information server 10 includes a Web API (application programming interface) 11, a Push API 12, a hydrological information DB (database) 13a, an AI learning model DB 13b, and a user management information DB 13c.

The hydrological information DB 13a stores observation data of various hydrological information such as river water levels, sewer water levels, etc., and rainfall collected via the communication network NW or dedicated communication lines, as well as disaster-related forecast data such as the probability of precipitation and the probability of inland and outside water flooding of rivers, sewers, etc.

The AI learning model DB13b, for example, calculates the probability distribution of predicted data corresponding to the observation data based on the past time series observation data and predicted data stored in the hydrological information DB13a, and stores a learning model for obtaining more accurate (reliable) predicted data for the current observation data.

User management information DB13c will be described later in the second embodiment.

The Web API 11, for example, transmits and receives messages (including observation data and prediction data) with external communication devices (including servers) via the communication network NW, and includes an API processing unit 11a, a message transmission/reception unit 11b, a message analysis unit 11c, and a message creation unit 11d.

In the Web API 11, the API processing unit 11a performs communication connection processing with the disaster prevention server 21 of the commercial facility server system 20. It also performs communication connection processing with external hydrological monitoring and control devices and weather information servers.

The message transmission/reception unit 11b also transmits and receives messages between the disaster prevention server 21 of the commercial facility server system 20, which is connected by the API processing unit 11a, or an external monitoring control device or weather information server.

In addition, the message analysis unit 11c analyzes the contents of the message received by the message transmission/reception unit 11b.

The message creation unit 11d also creates, for example, a response message based on the contents of the message from the commercial facility server system 20 or the contents of the message from an external monitoring control device or weather information server analyzed by the message analysis unit 11c, and provides the message to the message transmission/reception unit 11b.

The Push API 12 transmits, for example, a preset type of observation data or prediction data as a message at preset time intervals (variable) to the disaster prevention server 21 of the commercial facility server system 20, and includes an API processing unit 12a, a message sending unit 12b, a trigger timer 12c, and a message creation unit 12d.

In the Push API 12, the API processing unit 12a performs communication connection processing with the disaster prevention server 21 of the commercial facility server system 20.

The trigger timer 12c also times a time interval (variable) for transmitting a preset type of observation data or prediction data at each preset time interval (variable), for example, to the disaster prevention server 21 of the commercial facility server system 20.

In addition, the message creation unit 12d acquires observation data and forecast data to be sent to the disaster prevention server 21 from the hydrological information DB 13a at each time interval (variable) measured by the trigger timer 12c, and creates a message for the acquired observation data and forecast data.

The message sending unit 12b also sends the message created by the message creation unit 12d to the disaster prevention server 21 of the commercial facility server system 20 via the API processing unit 12a.

The commercial facility server system 20 includes a disaster prevention server 21, a SNS server 22, a service server 23, a store information DB 24a, a parking lot management information DB 24b, a user management information DB 24c, a message management DB 24d, and a disaster prediction information DB 24e.

The store information DB 24a stores, for example, overall map information including stores and parking lots of a commercial facility in which the commercial facility server system 20 is installed.

The parking lot management information DB24b stores, in association with the parking position number (for each vehicle) of parking lots in commercial facilities, information on whether the parking lot is outdoors or indoors (outdoor/indoor information), location information including the floor on which the parking lot is located (basement to rooftop), and vacancy status information for the parking lot.

The user management information DB24c (see FIG. 8) stores a store visit flag indicating whether the user has visited the store (left), the last store visit date and time, and the parking location number in association with the user ID (user identification information) of a user who is a customer using a commercial facility. In addition, as will be described in detail later, the database stores a cost value (C: indicating the sense of burden of actions taken for disaster countermeasures), a loss value (L: indicating the sense of loss that will be incurred if countermeasures are not taken), and the cost loss ratio (C/L) according to each user's disaster-related values obtained, for example, based on a user questionnaire (see FIG. 5 to FIG. 7), and a risk preference value (RS) indicating the risk tolerance obtained, for example, based on each user's purchasing trends.

When a parking lot code (two-dimensional code) QR including a parking location number photographed by the user terminal 40 upon a user's visit to the store is received by the commercial facility server system 20 from the user terminal 40 together with the user account, the message management DB 24d stores the received parking lot code QR in association with the user ID.

The disaster prediction information DB24e stores various observation data and prediction data related to hydrological information received from the disaster prediction information providing server 10.

The disaster prevention server 21 transmits and receives disaster prevention-related messages, including observation data and prediction data, between the disaster prediction information server 10 and the server, and includes an API processing unit 21a and a message transmission/reception unit 21b.

In the disaster prevention server 21, the API processing unit 21a performs communication connection processing with the Web API 11 and Push API 12 of the disaster prediction information providing server 10.

The message transmission/reception unit 21b also transmits and receives messages between the disaster prediction information providing server 10, which is connected via the API processing unit 21a, and the service server 23 in the commercial facility server system 20.

The SNS server 22 transmits and receives various messages, including parking lot code QR and disaster prevention prediction information, via the SNS-ChatBot server 30 to and from user terminals 40 that have user accounts corresponding to user IDs stored (registered) in the user management information DB 24c, and includes an SNS API processing unit 22a and a message transmission/reception unit 22b.

In the SNS server 22, the SNS API processing unit 22a performs communication connection processing with the SNS-ChatBot server 30.

The message transmission/reception unit 22b also transmits and receives messages between the user terminal 40 and the service server 23 in the commercial facility server system 20 via the SNS-ChatBot server 30 that is communicatively connected by the API processing unit 22a.

The service server 23 associates the parking lot code QR received from the user terminal 40 with the user ID and stores it in the message management DB 24d.

The service server 23 also generates user management information that associates the user ID with a store visit flag, the last store visit date and time, and the parking location number based on the parking lot code QR received from the user terminal 40, and stores the information in the user management information DB 24c. The service server 23 also stores, in addition to the user management information in the user management information DB 24c, the cost value (C), loss value (L), and cost loss ratio (C/L) according to each user's disaster-related values obtained based on the results of a survey of users received from the user terminal 40, and a risk preference value (RS) obtained based on each user's purchasing trends, for example.

In addition, when the service server 23 receives store exit information (including the current location) from the user terminal 40, it generates route guidance from the user's current location to the parking lot based on the user's current location, the user's parking location number stored in the user management information DB 24c, and the commercial facility map information stored in the store information DB 24a, and transmits the route guidance to the user terminal 40.

The service server 23 also determines users to whom risk information, including rainfall and flooding, should be provided based on the disaster prediction information (including probability of precipitation and flooding) received from the disaster prediction information providing server 10, the parking location number (parking location information) of the visiting user stored in the user management information DB 24c, and the user's cost-loss ratio (C/L) or risk preference value (RS), and generates parking lot vacancy information for covered parking lots that are not damaged by rainfall or upper-floor parking lots that are not damaged by flooding, together with rainfall risk information or flood risk information, and transmits this information to the corresponding user terminal 40.

The service server 23 includes a message transmission/reception unit 23a, a message analysis unit 23b, a disaster prediction information receiving unit 23c, and a message creation unit 23d.

In the service server 23, the message transmission/reception unit 23a transmits and receives messages with the SNS server 22 in the commercial facility server system 20.

In addition, the message analysis unit 23b analyzes the contents of the message received by the message transmission/reception unit 23a.

The disaster prediction information receiving unit 23c also receives disaster prediction information (including probability of precipitation and flooding) received by the disaster prevention server 21 in the commercial facility server system 20 from the disaster prediction information providing server 10.

The message creation unit 23d also creates a message providing route guidance from the user's current location to the parking lot when the user leaves the store, or a message including rainfall risk information and parking lot vacancy information for covered parking lots that are not damaged by rainfall, or a message including flood risk information and parking lot vacancy information for upper floor parking lots that are not damaged by flooding, based on the contents of the message from the user terminal 40 analyzed by the message analysis unit 23b, the disaster prediction information (including precipitation probability and flood probability) from the disaster prediction information providing server 10 received by the disaster prediction information receiving unit 23c, the commercial facility map information stored in the store information DB 24a, the parking lot's layout information including indoor/outdoor and floor location information and vacancy/full information stored in the parking lot management information DB 24b, and the user management information stored in the user management information DB 24c, and provides the message to the message transmission/reception unit 23a.

Figure 2 is a block diagram showing the hardware configuration common to the disaster prediction information server 10 included in the risk information system RIS, the disaster prevention server 21, the SNS server 22, the service server 23, and the SNS-ChatBot server 30 in the commercial facility server system 20.

The server 1 (10, 20 (21, 22, 23), 30) has a processor (CPU: central processing unit) 1A. The processor 1A controls the operation of the data memory 1C, the input/output interface 1D, and the communication interface 1E in accordance with the program stored in the program memory 1B, reads data stored in the data memory 1C, stores the data in the data memory 1C, and executes the functions of the server 1 according to the program.

The input/output interface 1D receives signals from an externally connected input device 2, such as a key input device or pointing device, and outputs the signals to the processor 1A. It also outputs image and audio signals generated by the processor 1A to an externally connected output device 3, such as a display or speaker.

The communication interface 1E connects the processor 1A to a communication network NW such as the Internet.

The performance, specifications, etc. of the processor 1A, program memory 1B, data memory 1C, input/output interface 1D, and communication interface 1E of each of the servers 10, 20 (21, 22, 23), and 30 may differ.

The hardware configuration of the user terminal 40 is similar to that of the server 1, and includes a processor 1A, a program memory 1B, a data memory 1C, and a communication interface 1E, but also includes an input device 2 and an output device 3, such as a touch panel display. The user terminal 40 may also include a GPS (global positioning system) receiver that acquires the current location.

In each of the servers 10, 20 (21, 22, 23), 30 and user terminal 40 thus configured, the processor 1A controls the operation of each part of the circuit according to the instructions written in the program (1B), and the software and hardware work together to realize various functions as described in the operation explanation below.

Here, we will explain the relationship between the cost value (C: indicating the sense of burden of actions taken for disaster prevention measures), loss value (L: indicating the sense of loss that would be incurred if no measures are taken), and the cost loss ratio (C/L) according to each user's disaster-related values stored in the aforementioned user management information DB24c (see Figure 8).

Figure 3 shows the general concept of the cost/loss model.

Cost (C) is the expense incurred when measures are taken to prevent a disaster event, and loss (L) is the loss incurred when no measures are taken.

For example, assume that the cost of taking measures is 500,000 yen, the loss if no measures are taken and the event occurs is 1,000,000 yen, and the probability P(x) of the event occurring is 50%.

If no countermeasures are taken and losses occur in 5 out of 10 situations in which the event could potentially occur, the total loss will be 5 million yen. On the other hand, if countermeasures costing 500,000 yen are taken in all 10 situations in which the event could potentially occur, the loss can be reduced to "0" by spending 5 million yen.

Therefore, under this assumption, if the probability P(x) of an event occurring exceeds the cost-loss ratio (C/L = 0.5), the cost will be less than the loss, so measures should be taken.

In the embodiment, for users who visit a commercial facility by car (vehicle), (1) the user (or vehicle) getting wet in the rain, and (2) the user's vehicle being flooded are regarded as disaster events. In addition, the probability of occurrence of the events (1: the user or vehicle getting wet in the rain) is regarded as the "precipitation probability," and the probability of occurrence of the events (2: the user's vehicle being flooded) is regarded as the "flooding probability."

The degree of burden felt by the user in moving the vehicle, which is a countermeasure against (1) the user or vehicle getting wet in the rain, and (2) the user's vehicle getting flooded, is taken as the cost value (C) of the cost loss ratio (C/L), and the degree of loss felt by the user in getting wet in the rain or the vehicle getting flooded if no countermeasure is taken is taken as the loss value (L) of the cost loss ratio (C/L).

Therefore, in the embodiment, when the "precipitation probability" or "flood probability" exceeds the user's cost-loss ratio (C/L), the user feels less burdened by taking action to take countermeasures than the loss he or she feels from suffering a disaster such as "rainfall" or "flooding," so it is considered that rainfall risk information or flood risk information should be provided to the user.

The degree of burden felt by the user in moving the vehicle (cost value C) is obtained, for example, from the results of a questionnaire in which the user is asked to answer on a 5-point scale how troublesome it is to move the vehicle (see Figures 5 and 6). The degree of loss felt by the user in response to a disaster (loss value L) is obtained, for example, from the results of a questionnaire in which the user is asked to answer on a 5-point scale how bothersome it is to get wet in the rain (see Figure 7).

Figure 4 shows an example of the implementation of the risk information provision system RIS in a commercial facility for users who are the target of providing information.

The information provision function of the risk information provision system RIS is executed in cooperation with a parking location guidance service provided by a commercial facility to a user P who comes to the store in a vehicle M, and the processing starts when the parking lot code QR photographed by the user terminal 40 when the user P comes to the store is received by the commercial facility server system 20 via the SNS-ChatBot server 30 together with the user account, and ends when route guidance to the parking location is sent to the user terminal 40 in response to receiving the user's store exit information together with the current location from the user terminal 40.

Figure 5 shows a questionnaire screen EG displayed on the user terminal 40, which asks the user to indicate the degree of burden of moving a vehicle as a cost value (C) on a five-point scale.

The survey screen EG may be displayed on the user terminal 40 by the service server 23 of the commercial facility server system 20 via the SNS server 22 and the SNS-ChatBot server 30, for example, at the initial setting timing when a user account is set on the user terminal 40, to obtain the answer of user P (i.e., cost value C), or may be included in various marketing information displayed from time to time on the user terminal 40 by the service server 23, and displayed to obtain the answer of user P (i.e., cost value C).

In addition, a questionnaire for asking the user to answer on a five-point scale the degree to which getting wet in the rain bothers them as a loss value (L) may be displayed on the user terminal 40 in the same manner as the questionnaire screen EG, and the answer of the user P (i.e., the loss value L) may be obtained.

Figure 6 shows a cost table TC that shows examples of cost values (C) that correspond to the degree of burden felt by the user in terms of how troublesome it is to move a vehicle.

The degree of burden felt by the user in terms of how troublesome it is to move a vehicle, i.e., the cost value (C), is set to the highest value of "5" for users who answer "I think it is quite troublesome" to the survey question "Is it troublesome to move a vehicle?", and to the lowest value of "1" for users who answer "It doesn't bother me at all."

Figure 7 shows an example of a loss table TL that shows loss values (L) corresponding to the degree of loss felt by a user, that is, how bothersome it is to get wet in the rain.

The degree to which a user feels that getting wet in the rain is a bother, i.e., the loss value (L), is set to the highest value of "5" for users who answer "definitely prefer a place with a roof" to the survey question "Would you prefer to park in a covered parking lot on a rainy day?", and to the lowest value of "1" for users who answer "I don't really care."

Figure 8 shows an example of user management information (user management information DB24c) that reflects the cost value (C) and loss value (L) obtained by a user questionnaire and their cost-loss ratio (C/L).

In the user management information shown in Figure 8, the user's risk preference (RS) indicates the user's risk tolerance, and can be measured, for example, by the tolerance for things with uncertainty. A user who can easily tolerate things with uncertainty has a high risk tolerance, and a user who cannot easily tolerate things with uncertainty has a low risk tolerance.

For example, when a user purchases the same type of item at a commercial facility, if the user tends to purchase items with the same product name, the user's risk tolerance can be determined to be low because the user is more likely to tolerate uncertainty, whereas if the user tends to purchase items with different product names even when purchasing the same type of item, the user's risk tolerance can be determined to be high because the user is less likely to tolerate uncertainty.

Therefore, the user's risk tolerance, i.e., risk preference (RS), may be obtained, for example, by the service server 23 of the commercial facility server system 20 by analyzing the purchasing trends of each user that can be collected from the commercial facility's POS system (point of sales system).

In the risk information provision system RIS of the embodiment, in addition to using the cost loss ratio (C/L) described above, a risk preference value (RS) is also used, so that for users whose risk preference value (RS) is close to "1" and who have a high risk tolerance (for example, who don't mind getting wet in the rain), rainfall risk information or flood risk information is provided from a stage where the probability of precipitation or flooding is high corresponding to the risk preference value (RS), and for users whose risk preference value (RS) is close to "0" and who have a low risk tolerance, rainfall risk information or flood risk information is provided from a stage where the probability of precipitation or flooding is low corresponding to the risk preference value (RS).

We believe that this will encourage each user to take spontaneous action to prepare for disasters.

Next, the operation of the risk information provision system RIS of the embodiment will be described.

Figure 9 is a flowchart showing the parking location notification process of the risk information provision system RIS.

Every day at 0:00 AM, the service server 23 of the commercial facility server system 20 resets the store visit flags associated with all user IDs stored in the user management information DB 24c (see Figure 8) to "X" (step S1).

When user P visits a commercial facility and photographs the parking lot code QR where user P parks his/her vehicle M using the user terminal 40 as shown in FIG. 4, the photographed parking lot code QR and user account data are sent to the SNS server 22 of the commercial facility server system 20 via the SNS-ChatBot server 30 (step P1).

When the service server 23 of the commercial facility server system 20 receives the parking lot code QR and user account data (message) sent from the user terminal 40 as store visit information via the SNS server 22 (step S2 (Yes)), the received message is analyzed by the message analysis unit 23b, the analyzed parking location number is stored in association with the user ID in the user management information DB 24c (see Figure 8) corresponding to the analyzed user account, and the store visit flag is set to "○" (step S3).

The service server 23, for example, connects to the servers (not shown) of each store in the commercial facility and expands store visit information related to user P, causing each store's server to transmit various marketing information, such as sales of products on sale, to the user terminal 40 (step S4).

The user terminal 40 receives various marketing information from the servers of each store in the commercial facility, transmitted via the SNS server 22 of the commercial facility server system 20, and displays or outputs the information as audio to provide to the user P (step P2).

At this time, the service server 23 of the commercial facility server system 20 may include in the various marketing information sent to the user terminal 40 a questionnaire for obtaining the cost value (C) corresponding to the degree of the user's sense of burden and the loss value (L) corresponding to the degree of the user's sense of loss, as shown in Figures 5 to 7.

After that, when user P leaves the commercial facility, when the user performs an operation to leave using the user terminal 40, the user account, store leaving information, and current location information are transmitted from the user terminal 40 to the SNS server 22 of the commercial facility server system 20 via the SNS-ChatBot server 30 (step P3).

When the service server 23 receives data (message) of the user account, store exit information, and current location information sent from the user terminal 40 via the SNS server 22 (step S5 (Yes)), the received message is analyzed by the message analysis unit 23b, and route guidance from the current location of the user P to the parking location is generated based on the parking location number associated with the user ID in the user management information DB 24c (see Figure 8) corresponding to the analyzed user account, the current location information, and the map information stored in the store information DB 24a, and the generated route guidance is transmitted to the user terminal 40 together with the parking location number (step S6).

The service server 23 also resets the store visit flag associated with the user ID in the user management information DB 24c to “X” (step S6).

The user terminal 40 receives and displays the parking location number and route guidance to the parking location transmitted from the service server 23 of the commercial facility server system 20 via the SNS server 22 (step P4).

During the business hours of the commercial facility, the commercial facility server system 20 executes the processing of steps S2 to S6 for the visiting user P each time it receives a parking code QR and user account data transmitted from the user terminal 40 in response to the visit of the user P (step S7 (Yes) → S2).

As a result, the commercial facility server system 20 of the risk information provision system RIS provides a service to guide a user P who comes to the store in a vehicle M to the parking location where the user P parked his/her vehicle M, and in conjunction with this, in cooperation with the disaster prediction information provision server 10, implements a service to provide rainfall risk information (see FIG. 10) and a service to provide flood risk information (see FIG. 11), which are described below.

Figure 10 is a flowchart showing the rainfall risk information provision process of the risk information provision system RIS.

In the rainfall risk information provision process shown in FIG. 10, the same processes as those included in the parking location notification process shown in FIG. 9 are denoted by the same reference numerals and their explanations are omitted.

The Push API 12 of the disaster prediction information providing server 10 transmits the prediction data of "precipitation probability" obtained based on the hydrological information DB 13a and the AI learning model DB 13b to the disaster prevention server 21 of the commercial facility server system 20 at each time interval measured by the trigger timer 12c (step R1).

When the disaster prevention server 21 of the commercial facility server system 20 receives the "precipitation probability" prediction data sent from the Push API 12 of the disaster prediction information providing server 10, it transmits the received "precipitation probability" prediction data to the service server 23, and the service server 23 stores the "precipitation probability" prediction data received by the disaster prediction information receiving unit 23c in the disaster prediction information DB 24e (step S41 (Yes)).

The service server 23 extracts user IDs that are subject to rainfall risk (risk of getting wet) based on the parking location number corresponding to the user P (user ID) whose visit flag is set to "○" stored in the user management information DB 24c, and the parking lot layout information including indoor/outdoor parking lot information and vacancy information stored in the parking lot management information DB 24b.

Furthermore, among the extracted users P (user IDs) who are subject to rainfall risk, the user IDs for which the "precipitation probability" received from the disaster prediction information providing server 10 in step S41 exceeds the cost-loss ratio of the corresponding user ID stored in the user management information DB 24c (or the user IDs for which the risk preference value RS is equal to or greater) are determined as users P (user IDs) to whom rainfall risk information should be provided (step S51).

Then, the service server 23 creates rainfall risk information (for example, "There is an N% chance of precipitation, so you may get wet. We recommend that you change your parking location") and transmits the created rainfall risk information and parking lot vacancy information, including the parking location number of the indoor parking lot stored in the parking lot management information DB 24b, from the SNS server 22 via the SNS-ChatBot server 30 to the user terminal 40 of the user P (user ID) to whom the rainfall risk information determined in step S51 should be provided (step S61).

The user terminal 40 receives and displays the rainfall risk information and parking lot vacancy information including the parking location number of the indoor parking lot transmitted from the service server 23 of the commercial facility server system 20 via the SNS server 22 (step P41).

As a result, when a user P who comes to the store in a vehicle M finds that the "precipitation probability" obtained from the disaster prediction information server 10 exceeds the user P's cost-loss ratio (C/L), the risk information provision system RIS determines that the user P's sense of burden in taking action to prevent the disaster will be lower than the sense of loss that the user P would feel from suffering a "rainfall" disaster, and can provide the user P with appropriate rainfall risk information and information on how to avoid the risk.

Even when the risk preference value (RS) is used, it is possible to determine that the "precipitation probability" obtained from the disaster prediction information providing server 10 for a user P who comes to the store in a vehicle M is, for example, above the user's tolerance for getting wet in the rain, and provide the user P with appropriate rainfall risk information and information on how to avoid the risk.

When a warning or advisory related to various hydrological information is issued, for example, by a government agency or local government (step R2 (Yes)), the Web API 11 of the disaster prediction information server 10 transmits the information on the warning or advisory to the disaster prevention server 21 of the commercial facility server system 20 (step R3).

When the disaster prevention server 21 of the commercial facility server system 20 receives warning/alert information from the disaster prediction information providing server 10, the service server 23 stops the series of rainfall risk information providing processes described above.

Figure 11 is a flowchart showing the flood risk information provision process of the risk information provision system RIS.

In the flood risk information provision process shown in FIG. 11, the same processes as those included in the parking location notification process shown in FIG. 9 and the precipitation risk information provision process shown in FIG. 10 are denoted by the same reference numerals and their descriptions are omitted.

The Push API 12 of the disaster prediction information providing server 10 transmits the "flood probability" prediction data obtained based on the hydrological information DB 13a and the AI learning model DB 13b to the disaster prevention server 21 of the commercial facility server system 20 at each time interval measured by the trigger timer 12c (step R11).

When the disaster prevention server 21 of the commercial facility server system 20 receives the "flood probability" prediction data sent from the Push API 12 of the disaster prediction information providing server 10, it transmits the received "flood probability" prediction data to the service server 23, and the service server 23 stores the "flood probability" prediction data received by the disaster prediction information receiving unit 23c in the disaster prediction information DB 24e (step S42 (Yes)).

The service server 23 extracts user IDs that are at risk of flooding (risk of a vehicle being flooded) based on the parking location number corresponding to the user P (user ID) whose visit flag is set to "○" stored in the user management information DB 24c, and the parking lot location information including the parking lot location floor and vacancy information stored in the parking lot management information DB 24b.

The floors on which parking lots are located that are at risk of flooding for vehicles may be set as the basement to first floor, for example, based on a flood hazard map provided by a local government.

Furthermore, among the extracted users P (user IDs) who are subject to flood risk, the user IDs whose "flood probability" received from the disaster prediction information providing server 10 in step S42 exceeds the cost-loss ratio of the corresponding user ID stored in the user management information DB 24c (or whose risk preference value RS is equal to or greater than the risk preference value RS) are determined as users P (user IDs) to whom flood risk information should be provided (step S52).

Then, the service server 23 creates flood risk information (for example, "There is a possibility that your vehicle will be flooded with a flood probability of N%. We recommend that you change your vehicle's parking location") and transmits the created flood risk information and parking lot vacancy information including the parking lot location numbers of parking lots that are not subject to the risk of vehicle flooding stored in the parking lot management information DB 24b from the SNS server 22 via the SNS-ChatBot server 30 to the user terminal 40 of the user P (user ID) to whom the flood risk information determined in step S52 should be provided (step S62).

The user terminal 40 receives and displays the flood risk information transmitted from the service server 23 of the commercial facility server system 20 via the SNS server 22 and parking lot vacancy information including parking position numbers of parking lots that are not subject to the risk of vehicle flooding (step P42).

As a result, when the "probability of flooding" obtained from the disaster prediction information server 10 for user P who comes to the store in vehicle M exceeds user P's cost-loss ratio (C/L), the risk information system RIS determines that user P's sense of burden in taking action to prevent the flooding will be less than the sense of loss that user P would feel from suffering a "vehicle flooding" disaster, and can provide appropriate flood risk information and information on how to avoid the risk.

Even when using the risk preference value (RS), it is possible to determine that the "probability of flooding" obtained from the disaster prediction information providing server 10 for a user P who comes to the store in a vehicle M is, for example, above the tolerable level of flooding for the vehicle M, and provide the user P with appropriate flood risk information and information on how to avoid it.

This will allow users to appropriately induce spontaneous actions for disaster prevention based on their individual values and tolerance regarding disasters.

Second Embodiment
In the risk information provision system RIS of the first embodiment described above, the user management information DB 24c of the commercial facility server system 20 stores a visit flag, last visit date and time, parking location number, cost value (C), loss value (L), cost loss ratio (C/L), and risk preference value (RS) in association with the user ID of a user P who is a customer using the commercial facility, and when disaster prediction information including "precipitation probability" or "flooding probability" is received from the disaster prediction information provision server 10 at preset time intervals, an appropriate user P (user ID) to whom rainfall risk information or flood risk information should be provided is determined according to the "precipitation probability" or "flooding probability," and the rainfall risk information or flood risk information is provided to the user terminal 40 of the determined user ID.

In addition to this, the user management information DB 13c of the disaster prediction information providing server 10 stores the visit flag, last visit date and time, parking location number (including layout information), cost value (C), loss value (L), cost loss ratio (C/L), and risk preference value (RS) acquired from the commercial facility server system 20 in association with the user ID of a user P who is a customer using a commercial facility. Then, similar to the first embodiment, the disaster prediction information providing server 10 may determine an appropriate user P (user ID) to be provided with rainfall risk information or flood risk information according to the "precipitation probability" or "flood probability" acquired by the server 10 itself, and provide the rainfall risk information or flood risk information to the user terminal 40 of the determined user ID via the commercial facility server system 20.

(Summary of the embodiment)
According to the risk information provision system RIS of the embodiment, when a user P who comes to the store in a vehicle M obtains a "precipitation probability" from the disaster prediction information provision server 10 that exceeds the cost-loss ratio (C/L) of the user P, it is determined that the burden of the user P taking action to take measures will be lower than the loss the user P would feel if he were to suffer a "rainfall" disaster, and appropriate rainfall risk information and information for avoidance actions can be provided to the user P. Also, when a user P who comes to the store in a vehicle M obtains a "flood probability" from the disaster prediction information provision server 10 that exceeds the cost-loss ratio (C/L) of the user P, it is determined that the burden of the user P taking action to take measures will be lower than the loss the user P would feel if he were to suffer a "vehicle flooding" disaster, and appropriate flood risk information and information for avoidance actions can be provided to the user P.

Therefore, by inducing recipients of disaster prediction information to take "property-saving action" in accordance with economic principles before taking "life-saving evacuation action," it is possible to speed up and decentralize evacuation.

In the risk information provision system RIS of the first and second embodiments, it is assumed that the disaster prediction information is "precipitation probability" or "flooding probability", and based on parking position information including the layout information of the parking lot where the user P parks the vehicle M, "rainfall risk information" is provided to the user P who may get wet in the rain, and "flood risk information" is provided to the user P whose vehicle M may be flooded. However, it goes without saying that the disaster prediction information is not limited to "precipitation probability" or "flooding probability", and the risk information is not limited to "rainfall risk information" or "flooding risk information".

In addition, the risk information provision system RIS in the first and second embodiments is configured to provide appropriate risk information to each user P according to their values or tolerance regarding disasters based on their cost-loss ratio (C/L) or risk preference value (RS). However, without being limited to this, the cost-loss ratio (C/L) and risk preference value (RS) may be combined, and risk information may be provided to the corresponding user when, for example, the probability of disaster prediction information exceeds either the value of the cost-loss ratio (C/L) or the risk preference value (RS).

Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are included in the scope of the invention and its equivalents described in the claims.

RIS...risk information provision system, 10...disaster prediction information provision server, 13a...hydrological information DB, 13b...AI learning model DB, 20...commercial facility server system, 21...disaster prevention server, 22...SNS server, 23...service server, 24a...store information DB, 24b...parking lot management information DB, 24c...user management information DB, 24d...message management DB, 24e...disaster prediction information DB, 30...SNS-ChatBot server, 40...user terminal.

Claims (7)

A server having a function of communicating with a user terminal,
The server,
A property location information acquisition means for acquiring location information of a user's property;
a cost value acquiring means for acquiring, as a cost value, a degree of burden felt by a user for measures taken by the user to prevent damage to the user's property;
a loss value acquiring means for acquiring, as a loss value, a degree of a user's sense of loss in regard to damage to the property caused by not taking the countermeasure or a disadvantage associated therewith;
A disaster prediction probability acquisition means for acquiring the probability of disaster prediction information;
a first risk information transmission means for transmitting risk information notifying the user terminal of the possibility of damage to the property when a location corresponding to the property location information acquired by the property location information acquisition means is related to the disaster prediction information and the property is likely to be damaged or the user may suffer a disadvantage related thereto, and when the probability of the disaster prediction information acquired by the disaster prediction probability acquisition means exceeds a cost loss ratio between the cost value acquired by the cost value acquisition means and the loss value acquired by the loss value acquisition means;
A risk preference value acquisition means for acquiring a risk preference value of the user;
a second risk information transmission means for transmitting risk information notifying the user terminal of the possibility of damage to the property when a location corresponding to the property location information acquired by the property location information acquisition means indicates a possibility that the property may be affected by a disaster related to the disaster prediction information or that the user may suffer a related disadvantage, and when the probability of the disaster prediction information acquired by the disaster prediction probability acquisition means is equal to or greater than the user's risk preference value acquired by the risk preference value acquisition means;
A risk information provision system equipped with the cost value acquired by the cost value acquiring means and the loss value acquired by the loss value acquiring means are acquired from the user terminal based on a questionnaire displayed on the user terminal;
2. A risk information providing system according to claim 1. The risk preference value of the user acquired by the risk preference value acquisition means is acquired based on an analysis of the purchasing trends of the user in a commercial facility server system.
3. A risk information providing system according to claim 1 or 2 . the property location information acquisition means is a vehicle location information acquisition means for acquiring location information of a vehicle of a user;
the cost value acquisition means acquires, as a cost value, a degree of burden felt by the user in moving the user's vehicle as a measure to prevent the user's vehicle from being affected by rainfall or flooding;
the loss value acquisition means acquires, as a loss value, a degree of a sense of loss felt by the user in the event that rain or flooding occurs to the vehicle or the user suffers a disadvantage associated therewith if the countermeasure is not taken;
The disaster prediction probability acquisition means acquires a probability of precipitation or a probability of flooding,
the first risk information transmission means, when a location corresponding to the vehicle's location information acquired by the vehicle location information acquisition means indicates that the vehicle may be subject to rainfall or flooding or that the user may suffer a related disadvantage, transmits to the user terminal rainfall risk information or flooding risk information notifying the user of the possibility of rainfall or flooding affecting the vehicle together with information for avoidance actions when the precipitation probability or flooding probability acquired by the disaster prediction probability acquisition means exceeds a cost-loss ratio between the cost value acquired by the cost value acquisition means and the loss value acquired by the loss value acquisition means;
4. A risk information providing system according to claim 1. The servers include a first server and a second server having a mutual communication function;
The first server includes the disaster prediction probability acquisition means,
The second server includes the property location information acquisition means, the cost value acquisition means, the loss value acquisition means, and the first risk information transmission means.
5. A risk information providing system according to any one of claims 1 to 4 . The servers include a first server and a second server having a mutual communication function;
the first server includes the cost value acquisition means, the loss value acquisition means, the disaster prediction probability acquisition means, and the first risk information transmission means,
The second server includes the property location information acquisition means.
5. A risk information providing system according to any one of claims 1 to 4 . A server with a communication function with the user terminal
Obtain location information of the user's property;
acquiring, as a cost value, a degree of burden felt by a user for measures taken by the user to prevent damage to the user's property;
acquiring a loss value representing the degree of the user's sense of loss regarding damage to the property or the disadvantage the user will suffer if the countermeasure is not taken;
Obtain the probability of disaster prediction information,
When a location corresponding to the location information of the property is related to the disaster prediction information and there is a possibility that the property may be damaged or the user may suffer a disadvantage related thereto, and when the probability of the disaster prediction information exceeds a cost-loss ratio between the cost value and the loss value, risk information is transmitted to the user terminal to notify the user of the possibility of damage to the property;
obtaining a risk preference value for the user;
When a location corresponding to the location information of the property is related to the disaster prediction information and there is a possibility that the property may be affected by a disaster or that the user may suffer a related disadvantage, and when the probability of the disaster prediction information is equal to or greater than the acquired risk preference value of the user, transmitting risk information to the user terminal notifying the user of the possibility of damage to the property;
This is a method of providing risk information.