Display control device, display control method, and display control program

Abstract

This display control device estimates, on the basis of a prediction distribution of occurrence locations that represent locations where a call for an emergency vehicle has occurred, an occurrence time when a call occurs at an occurrence location for each of the plurality of occurrence locations. The display control device executes, on the basis of the respective occurrence times at the plurality of occurrence locations and an activity status of each of the plurality of emergency vehicles, a simulation of emergency activity in which any one emergency vehicle that can be dispatched among the plurality of emergency vehicles is dispatched to an occurrence location at an occurrence time for each of the plurality of occurrence locations. On the basis of the simulation result, the display control device extracts, from the plurality of occurrence locations, an occurrence location for which the distance between an emergency vehicle that can be dispatched and the occurrence location is no less than a threshold, and calculates a risk so that the risk in an area to which the extracted occurrence location belongs increases. The display control device controls a display unit so as to display the risk.

Description

Technical Field

[0001] The disclosed technology relates to a display control device, a display control method, and a display control program.

Background Art

[0002] Conventionally, a technology related to an optimal operation system for emergency ambulances using emergency big data has been known (see, for example, Non-Patent Document 1). Non-Patent Document 1 discloses a technology aimed at shortening the on-site arrival time and hospital accommodation time in the transportation of injured or sick persons by ambulances.

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, when there is a call for an ambulance, which is an example of an emergency vehicle, depending on the dispatch status of the ambulance, it may take time for the ambulance to arrive at the location where the call was made. For example, consider the case where all the ambulances belonging to a fire department near a certain area have been dispatched. In this case, if an ambulance is called in this area, it is considered that an ambulance belonging to a fire department far from the area will be dispatched to the area. In this case, although there is a fire department close to the area, an ambulance belonging to a far - away fire department will be dispatched to the area, and it will take time for the ambulance to arrive.

[0005] As one method for dealing with such a situation, for example, there is a method of pre - moving an ambulance to an area far from a fire department or near an area where no ambulance is waiting. Examples of methods for determining how to move the ambulance include a method determined by a person or a method calculated by a system.

[0006] However, in an area not covered by such an ambulance, it is generally affected by the real - time activity status of multiple ambulances. It is difficult for a person to consider the real - time activity status of each such ambulance and determine the ambulance deployment. Furthermore, whether the ambulance deployment is determined by a person or by a system, it is difficult to judge the validity of the ambulance movement, so it is considered difficult to obtain a sense of security.

[0007] It is also expected that the goodness of the ambulance deployment can be evaluated by comparing the visualization of the predicted demand for ambulance calls in each region with the current positions of the ambulances. In addition to the positions of the ambulances, it is also conceivable to further display the states of the ambulances such as standby or in operation, or to display only the ambulances in a predetermined state. However, in the entire region, there are many areas where the demand for ambulances is predicted, and it is assumed that the number of ambulances is also large. It is extremely difficult to make appropriate judgments considering all such diverse and large amounts of information. The prior art has a problem in that it cannot provide information that can uniquely recognize or judge the goodness of the ambulance deployment, such as an index value based on the time required until the arrival of the ambulance, after appropriately processing such diverse information.

[0008] The disclosed technology has been made in view of the above points, and aims to visualize the locations that require time until the arrival of an emergency vehicle.

Means for Solving the Problem

[0009] A first aspect of the present disclosure is a display control device including a display control unit that controls to display on a display unit position information of an emergency vehicle, a predicted distribution of occurrence points representing the points where calls for the emergency vehicle occur, and a degree of risk corresponding to the time required for the emergency vehicle to arrive at the occurrence point after the call for the emergency vehicle occurs or the distance between the emergency vehicle and the occurrence point.

[0010] A second aspect of the present disclosure is a display control method in which a computer executes a process of controlling to display on a display unit position information of an emergency vehicle, a predicted distribution of occurrence points representing the points where calls for the emergency vehicle occur, and a degree of risk corresponding to the time required for the emergency vehicle to arrive at the occurrence point after the call for the emergency vehicle occurs or the distance between the emergency vehicle and the occurrence point.

[0011] A third aspect of the present disclosure is a display control program for causing a computer to execute a process of controlling a display unit to display position information of an emergency vehicle, a predicted distribution of occurrence points representing points where calls for the emergency vehicle occur, and a degree of risk corresponding to the time required for the emergency vehicle to arrive at the occurrence point after the call for the emergency vehicle occurs or the distance between the emergency vehicle and the occurrence point.

[0012] A fourth aspect of the present disclosure is a display control device including: an estimation unit that estimates, for each of a plurality of the occurrence points, an occurrence time at which a call occurs at the occurrence point based on a predicted distribution of occurrence points representing points where calls for an emergency vehicle occur; a simulation unit that executes, for each of the plurality of the occurrence points, a simulation of an emergency activity in which any one of a plurality of emergency vehicles that can be dispatched departs for the occurrence point at the occurrence time based on the occurrence time of each of the plurality of the occurrence points, which is an estimation result by the estimation unit, and the activity status of each of the plurality of emergency vehicles; a calculation unit that extracts, from the plurality of the occurrence points, an occurrence point at which the distance between the dispatchable emergency vehicle and the occurrence point is equal to or greater than a threshold value based on the simulation result by the simulation unit, and calculates the degree of risk so that the degree of risk of the area to which the extracted occurrence point belongs becomes high; and a display control unit that controls the display unit to display the degree of risk calculated by the calculation unit.

[0013] A fifth aspect of the present disclosure estimates, for each of a plurality of occurrence locations, an occurrence time at which a call occurs at the occurrence location based on a predicted distribution of the occurrence locations representing the locations where calls for emergency vehicles occur, and based on the occurrence time of each of the plurality of occurrence locations as an estimation result and the activity status of each of the plurality of emergency vehicles, for each of the plurality of occurrence locations, a computer executes a simulation of an emergency activity in which any one of the plurality of emergency vehicles that can be dispatched travels to the occurrence location at the occurrence time, extracts, based on the simulation result, an occurrence location from the plurality of occurrence locations where the distance between the dispatchable emergency vehicle and the occurrence location is equal to or greater than a threshold value, calculates the risk level so that the risk level of the area to which the extracted occurrence location belongs becomes high, and controls to display the calculated risk level on a display unit. This is a display control method executed by a computer.

Advantages of the Invention

[0014] According to the disclosed technology, it is possible to visualize locations that require time until the arrival of an emergency vehicle.

Brief Description of the Drawings

[0015]

Figure 1

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Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the disclosed technology will be described with reference to the drawings. In each of the drawings, the same or equivalent components and parts are given the same reference numerals. Also, the dimensional ratios in the drawings are exaggerated for the convenience of explanation and may be different from the actual ratios.

[0017] FIGS. 1 to 3 are diagrams for explaining the outline of the present embodiment.

[0018] FIG. 1 is an example of a predicted distribution M1 of a generation point P representing the point where a call for an ambulance, which is an example of an emergency vehicle, occurs. The predicted distribution M1 in FIG. 1 plots the generation point P that is predicted to occur in the map data partitioned by a plurality of meshes. The predicted distribution M1 predicts the demand for calls for each mesh.

[0019] In the prediction distribution as shown in FIG. 1, the occurrence points predicted to have calls are visualized. However, in the prediction distribution as shown in FIG. 1, when a call occurs at a certain occurrence point, it is not visualized how much time it takes until an ambulance arrives at that occurrence point. For example, although the predicted demand in regions R1, R3, and R4 in FIG. 1 is "extremely large", since there is a fire station where an ambulance is waiting or an available ambulance is nearby, it is expected that the time until an ambulance arrives in regions R1, R3, and R4 is short. On the other hand, in region R2 of FIG. 1, although the predicted demand is "extremely large" and there is a fire station nearby, there is no ambulance waiting at that fire station, so it is expected that the time until an ambulance arrives in region R1 is long.

[0020] Therefore, in the present embodiment, the places that take time until the arrival of an emergency vehicle are visualized.

[0021] FIGS. 2 and 3 are diagrams showing an example of the risk distribution M2 generated according to the present embodiment. As shown in FIG. 2, in the risk distribution M2, the risk in region R2 becomes "extremely large", and the places that take time until the arrival of an emergency vehicle are visualized.

[0022] Note that instead of targeting all available ambulances, the visualization of the risk may be performed based only on the ambulances waiting at the fire station. Thereby, the visualization is performed such that the risk in the areas far from the ambulances waiting at the fire station becomes high. It is conceivable to set the route of the ambulances outside the fire station using this risk. Also, when the route of the ambulances outside the fire station is set, such a risk can be used to judge the validity of the route.

[0023] For example, as shown in FIG. 3, in the risk distribution M3, the risk level of the area R3 also becomes "extremely high", and the risk levels of locations that require time until the arrival of an emergency vehicle are visualized. In the example of FIG. 3, although there is an ambulance near the area R3, the risk level is high. This is because the ambulance near the area R3 is in motion, and the area R3 is far from the fire station where the ambulance is waiting. On the other hand, since there is a fire station where an ambulance is waiting near the area R4, the risk level is "low".

[0024] As described above, in this embodiment, the risk levels of areas not covered by ambulances are calculated and visualized. In this embodiment, considering the activity status of ambulances, the position information of available ambulances is used to extract the uncovered occurrence points. Also, in this embodiment, considering the ease of dispatch of available ambulances, the risk levels of occurrence points near ambulances that are easy to dispatch are set to be high. Thereby, when an emergency vehicle such as an ambulance is called, it is possible to visualize the locations that require time until the arrival of the emergency vehicle such as an ambulance. Also, according to this embodiment, for example, it is possible to support the ambulance deployment work.

[0025] <First Embodiment>

[0026] FIG. 4 is a block diagram showing the hardware configuration of the display control device 10.

[0027] As shown in FIG. 4, the display control device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display unit 16, and a communication interface (I / F) 17. Each component is connected to be communicable with each other via a bus 19.

[0028] The CPU 11 is a central processing unit that executes various programs and controls each part. That is, the CPU 11 reads a program from the ROM 12 or the storage 14 and executes the program using the RAM 13 as a working area. The CPU 11 performs control of each of the above configurations and various arithmetic processes according to the program stored in the ROM 12 or the storage 14. In the present embodiment, a language processing program for converting voice input by the mobile terminal 20 into characters is stored in the ROM 12 or the storage 14.

[0029] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores a program or data as a working area. The storage 14 is composed of a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs including an operating system and various data.

[0030] The input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to perform various inputs.

[0031] The display unit 16 is, for example, a liquid crystal display, and displays various information. The display unit 16 may adopt a touch panel method and function as the input unit 15.

[0032] The communication interface 17 is an interface for communicating with other devices such as a mobile terminal. For this communication, for example, a standard for wired communication such as Ethernet (registered trademark) or FDDI, or a standard for wireless communication such as 4G, 5G, or Wi-Fi (registered trademark) is used.

[0033] Next, the functional configuration of the display control device 10 will be described.

[0034] FIG. 5 is a block diagram showing an example of the functional configuration of the display control device 10.

[0035] As shown in FIG. 5, the display control device 10 includes, as functional components, an acquisition unit 100, a data storage unit 101, a demand prediction unit 102, a situation acquisition unit 104, a calculation unit 106, and a display control unit 108. Each functional component is realized by the CPU 11 reading out a display control program stored in the ROM 12 or the storage 14, expanding it in the RAM 13, and executing it.

[0036] The acquisition unit 100 acquires various data from a command desk system (not shown) where various data of each of a plurality of ambulances are collected. Further, the acquisition unit 100 may acquire various data from an external server (not shown) different from the command desk system. Then, the acquisition unit 100 stores the acquired various data in the data storage unit 101.

[0037] The data storage unit 101 stores the various data acquired by the acquisition unit 100. For example, the data stored in the data storage unit 101 includes, for each of a plurality of ambulances, the availability status of the ambulance, the position information of the ambulance, the position information of the fire station to which the ambulance belongs, the identification information of the fire station to which the ambulance belongs, and information representing the combination of the position and time at which the ambulance was called in the past, and the like. Therefore, new data is stored in the data storage unit 101 every moment.

[0038] The demand prediction unit 102 generates a prediction distribution representing the demand prediction for the occurrence location indicating the location where the ambulance is called. For example, the demand prediction unit 102 generates a prediction distribution for the occurrence location based on the information stored in the data storage unit 101 and representing the combination of the location and time when the ambulance was called in the past. For example, the demand prediction unit 102 samples the location for each mesh representing a certain area in the map data based on the past call locations. Then, the demand prediction unit 102 obtains the latitude and longitude information of a plurality of possible occurrence locations for each mesh in the map data. In addition, when the number of occurrence locations can be predicted on a monthly or weekly basis, as a simpler method, the demand prediction unit 102 may extract the data of the same month or week in the past and use the latitude and longitude information as the location information of the occurrence location. In this case, as the location information of the occurrence location, for example, the latitude and longitude information as shown in FIG. 6 can be obtained.

[0039] Alternatively, for example, the demand prediction unit 102 may generate a prediction distribution for the occurrence location using a trained model that has been pre-trained by machine learning using ambulance transfer information, population information of each past location, weather information of each past location, and the like.

[0040] The situation acquisition unit 104 acquires information on ambulances that can be dispatched from the data storage unit 101. For example, the situation acquisition unit 104 acquires the information on ambulances that can be dispatched by acquiring the data as shown in FIG. 8 from the data as shown in FIG. 7 stored in the data storage unit 101.

[0041] Examples of ambulances that can be dispatched include ambulances waiting at the fire station, ambulances in a driving state outside the fire station such as ambulances on the way back to the station or moving to another fire station, and ambulances waiting somewhere outside the fire station. In FIGS. 7 and 8, the ambulances with the activity status of "on the route" represent the situation where they are not waiting at the fire station but can be dispatched. Note that the situation acquisition unit 104 does not necessarily need to acquire the "ambulance name", which is the identification information for identifying the ambulance, in this data processing flow.

[0042] Based on the position information of a plurality of ambulances acquired by the situation acquisition unit 104 and the predicted distribution generated by the demand prediction unit 102, the calculation unit 106 calculates the degree of danger corresponding to the distance between any one of the plurality of ambulances and the occurrence point.

[0043] Specifically, for each of the plurality of occurrence points in the predicted distribution generated by the demand prediction unit 102, the calculation unit 106 identifies a target ambulance representing the ambulance with the shortest distance to the occurrence point from among the plurality of ambulances.

[0044] Here, let the set of occurrence points be N and the set of ambulances that can be dispatched be A. In this case, when ambulance j is called at occurrence point i, the distance d ij is calculated. Note that i is an element of N and j is an element of A. In this case, the distance d i from occurrence point i to the nearest ambulance is represented by the following formula (1).

[0045]

Equation

[0046] Next, the calculation unit 106 extracts occurrence points from among the plurality of occurrence points where the distance d i between the target ambulance and the occurrence point i is greater than or equal to the threshold value d th . Thereby, the set of occurrence points {i|d th <d i} existing at a position far from the nearest ambulance is extracted.

[0047] Then, the calculation unit 106 plots the extracted occurrence points in the map data partitioned by a plurality of meshes. For each mesh included in the map data, the calculation unit 106 calculates the degree of danger for each mesh such that the higher the number of occurrence points included in the mesh, the higher the degree of danger, and the lower the number of occurrence points included in the mesh, the lower the degree of danger.

[0048] The display control unit 108 controls the display unit 16 to display the position information of a plurality of ambulances acquired by the situation acquisition unit 104, the prediction distribution generated by the demand prediction unit 102, and the risk level calculated by the calculation unit 106. The risk level of each mesh included in the map data is visualized by the display control unit 108. Note that the prediction distribution may not be displayed, and only the risk level may be visualized.

[0049] Next, the operation of the display control device 10 will be described.

[0050] FIG. 9 is a flowchart showing the flow of the display control process by the display control device 10. The display control process is performed by the CPU 11 reading the display control process program from the ROM 12 or the storage 14, expanding it in the RAM 13, and executing it.

[0051] In step S100, the CPU 11, as the demand prediction unit 102, generates a prediction distribution representing the demand prediction at the occurrence point representing the position where the ambulance is called.

[0052] In step S102, the CPU 11, as the situation acquisition unit 104, the situation acquisition unit 104 acquires, for each of the plurality of ambulances, the dispatchable situation of the ambulance, the position information of the ambulance, the position information of the fire station to which the ambulance belongs, and the identification information of the fire station to which the ambulance belongs, etc. from the data storage unit 101.

[0053] In step S104, the CPU 11, as the calculation unit 106, for each of the plurality of occurrence points in the prediction distribution generated in step S100, identifies a target ambulance representing the ambulance with the shortest distance to the occurrence point from among the plurality of ambulances.

[0054] In step S106, the CPU 11, as the calculation unit 106, extracts occurrence points from among the plurality of occurrence points where the distance between the target ambulance identified in step S104 and the occurrence point is equal to or greater than the threshold value.

[0055] In step S108, the CPU 11, as the calculation unit 106, plots the occurrence point extracted in step S106 in the map data partitioned by a plurality of meshes. Then, the calculation unit 106 totals the number of occurrence points for each mesh included in the map data.

[0056] In step S110, the CPU 11, as the calculation unit 106, calculates the risk level for each mesh such that the higher the number of occurrence points included in the mesh, the higher the risk level, and the lower the number of occurrence points included in the mesh, the lower the risk level, for each of the meshes included in the map data.

[0057] In step S112, the CPU 11, as the display control unit 108, controls the display unit 16 to display the position information of a plurality of ambulances acquired in step S102, the predicted distribution generated in step S100, and the risk level calculated in step S110.

[0058] As described above, according to the display control device of the first embodiment, the position information of an ambulance, which is an example of an emergency vehicle, the predicted distribution of occurrence points representing the points where ambulance calls occur, and the risk level corresponding to the distance information representing the distance between the ambulance and the occurrence point are displayed on the display unit. Thereby, when an ambulance call occurs, it is possible to visualize the locations where it takes time until the arrival of the emergency vehicle.

[0059] <Second Embodiment>

[0060] Next, the second embodiment will be described. The second embodiment is different from the first embodiment in that an ambulance is set as the center of a cluster, occurrence points are assigned to the cluster, and the risk level is calculated based on the result. Since the configuration of the display control device according to the second embodiment is the same as that of the first embodiment, the same reference numerals are used and the description thereof is omitted.

[0061] When an ambulance is the closest ambulance to multiple incident locations, it will be easy to dispatch. In this case, even if an ambulance is nearby, the risk levels of those incident locations need to be increased.

[0062] Therefore, in the second embodiment, a cluster centered on each ambulance is configured, and the risk level is calculated for the area included in the cluster as the area that the ambulance can satisfy the demand. Note that the cluster is, for example, an area indicating a predetermined range in the real space. Specifically, in the second embodiment, for each of the incident locations, by associating the incident location with the target ambulance that is an ambulance that can be dispatched and is the closest ambulance to the incident location, a plurality of incident locations are clustered. In this case, as many clusters as the number of ambulances that can be dispatched will be set. The set of incident locations belonging to this cluster will be the set of incident locations existing within the coverage range of the ambulance set at the center of the cluster.

[0063] Then, in the second embodiment, it is calculated how many incident locations belong to the cluster corresponding to the ambulance, and each of the incident locations belonging to the cluster in which the number of incident locations assigned to the cluster corresponding to the ambulance is larger than a preset number is extracted. And in the second embodiment, the risk level is calculated according to the number of the extracted incident locations. This will be specifically described below.

[0064] The calculation unit 106 of the second embodiment sets each of the plurality of ambulances as each of the centers of the plurality of clusters.

[0065] Next, in the same manner as in the first embodiment, the calculation unit 106 identifies, from among the plurality of ambulances, the target ambulance representing the ambulance with the shortest distance to each of the incident locations in the predicted distribution generated by the demand prediction unit 102. Then, the calculation unit 106 assigns those incident locations to the center of the cluster corresponding to the target ambulance.

[0066] Here, the target ambulance a for the incident location i iis represented by the following formula (2).

[0067] [Number] (2)

[0068] Note that, if the cluster of ambulance j is C j then C j = {i | a i = j}.

[0069] Next, in the same manner as in the first embodiment, the calculation unit 106 extracts each of the occurrence locations where the distance d i between the target ambulance a i and the occurrence location i is greater than or equal to the threshold value d th . Further, the calculation unit 106 extracts each of the occurrence locations belonging to a cluster in which the number of assigned occurrence locations is greater than a preset number.

[0070] The following will be specifically described.

[0071] Specifically, first, the calculation unit 106 sets a positive constant b j for each of the plurality of ambulances in the cluster C j of ambulance j. Next, the calculation unit 106 initializes the counter c j corresponding to the cluster C j of ambulance j by substituting 0. Note that the constant b j may be designed to be larger as the number of occurrence locations to be processed by ambulance j or the number of elements of N is larger. Here, one of the meanings of the constant b j will be supplemented. The constant b j can be regarded as the capacity for the demand of the ambulance. That is, it is assumed that the capacity varies according to the ambulance or the regional characteristics. For this reason, the constant b j may be designed according to the ambulance or the place where this embodiment is implemented.

[0072] Next, the calculation unit 106 sorts the distances d calculated for each of the plurality of occurrence points in ascending order. Then, for each of all the distances d belonging to the set N, the calculation unit 106 compares the smaller distances d in order with the threshold value d. i When the distance d is greater than or equal to the threshold value d, the calculation unit 106 extracts the occurrence point i. On the other hand, when the distance d is less than the threshold value d, the calculation unit 106 adds 1 to the counter c of the cluster C to which the occurrence point i belongs. i Then, the calculation unit 106 compares the counter c of the cluster C with the positive constant b, and when b < c, extracts each of the occurrence points belonging to the cluster C. Instead of the occurrence points belonging to the cluster C where b < c, it may be configured to extract the overflow occurrence points. The overflow occurrence points are, for example, the occurrence points belonging to the cluster C determined based on a predetermined criterion such as position or time, and when the number of occurrence points belonging to the cluster C exceeds the constant b, they are occurrence points that do not belong to any cluster. i th th

[0073] When the distance d is greater than or equal to the threshold value d, the calculation unit 106 extracts the occurrence point i. On the other hand, when the distance d is less than the threshold value d, the calculation unit 106 adds 1 to the counter c of the cluster C to which the occurrence point i belongs. i th th When the distance d is greater than or equal to the threshold value d, the calculation unit 106 extracts the occurrence point i. On the other hand, when the distance d is less than the threshold value d, the calculation unit 106 adds 1 to the counter c of the cluster C to which the occurrence point i belongs. i th th When the distance d is less than the threshold value d, the calculation unit 106 adds 1 to the counter c of the cluster C to which the occurrence point i belongs. j j j

[0074] Then, the calculation unit 106 compares the counter c of the cluster C with the positive constant b, and when b < c, extracts each of the occurrence points belonging to the cluster C. Instead of the occurrence points belonging to the cluster C where b < c, it may be configured to extract the overflow occurrence points. The overflow occurrence points are, for example, the occurrence points belonging to the cluster C determined based on a predetermined criterion such as position or time, and when the number of occurrence points belonging to the cluster C exceeds the constant b, they are occurrence points that do not belong to any cluster. j j j j j j j j j When b < c, the calculation unit 106 extracts each of the occurrence points belonging to the cluster C. Instead of the occurrence points belonging to the cluster C where b < c, it may be configured to extract the overflow occurrence points. The overflow occurrence points are, for example, the occurrence points belonging to the cluster C determined based on a predetermined criterion such as position or time, and when the number of occurrence points belonging to the cluster C exceeds the constant b, they are occurrence points that do not belong to any cluster. j j j j j j j j j j j j j When the number of occurrence points belonging to the cluster C exceeds the constant b, they are occurrence points that do not belong to any cluster.

[0075] As a result, it is reflected that the ambulance j corresponding to the cluster C where the number c of assigned occurrence points is greater than the preset b is more likely to be dispatched. Therefore, the risk level of the mesh area including the occurrence points belonging to such an ambulance cluster is set to be high. j j j j j As a result, it is reflected that the ambulance j corresponding to the cluster C where the number c of assigned occurrence points is greater than the preset b is more likely to be dispatched. Therefore, the risk level of the mesh area including the occurrence points belonging to such an ambulance cluster is set to be high.​​

[0076] Note that when the number c of assigned occurrence locations j is larger than a preset b j the occurrence locations of the number of the difference between the constant b j and the number of assigned occurrence locations may be extracted as occurrence locations that the target ambulance cannot cover.

[0077] Next, the operation of the display control device 10 will be described.

[0078] FIG. 10 is a flowchart showing the flow of display control processing by the display control device 10. The display control processing is performed by the CPU 11 reading a display control processing program from the ROM 12 or the storage 14, expanding it in the RAM 13, and executing it.

[0079] Steps S100 to S104 and step S112 are executed in the same manner as in the first embodiment.

[0080] In step S200, the CPU 11 calculates the risk level by executing the flowchart shown in FIG. 11 as the calculation unit 106.

[0081] In step S201 of the flowchart shown in FIG. 11, the CPU 11 sets each of the plurality of ambulances j as the center of each of the plurality of clusters C j

[0082] In step S202, the CPU 11 assigns each of the plurality of occurrence locations i to the cluster C i of the target ambulance a j

[0083] In step S204, the CPU 11 initializes the counter c j corresponding to the ambulance j.

[0084] ​​In step S206, the CPU 11 sets, as the calculation unit 106, a constant b corresponding to the ambulance j. j to set.

[0085] In step S208, the CPU 11 sorts, as the calculation unit 106, the distances d of each of the plurality of occurrence points in ascending order. i to sort in ascending order.

[0086] In step S210, the CPU 11 sets, as the calculation unit 106, the occurrence point i.

[0087] In step S212, the CPU 11 determines, as the calculation unit 106, whether the distance d corresponding to the occurrence point i set in step S210 is greater than or equal to the threshold value d. If the distance d is greater than or equal to the threshold value d, the process proceeds to step S213. On the other hand, if the distance d is less than the threshold value d, the process proceeds to step S214. i is the threshold d th or more. The distance d i is the threshold d th or more, the process proceeds to step S213. On the other hand, if the distance d i is the threshold d th less than, the process proceeds to step S214.

[0088] In step S213, the CPU 11 extracts, as the calculation unit 106, the occurrence point i set in step S210 and returns to step S210.

[0089] In step S214, the CPU 11 adds 1 to the counter c corresponding to the target ambulance a of the cluster C j to which the occurrence point i belongs. i corresponding to j to add 1.

[0090] In step S216, the CPU 11 determines, as the calculation unit 106, whether the processing of steps S201 to S214 has been completed for all occurrence points. If the processing of steps S210 to S214 has been completed for all occurrence points, the process proceeds to step S218. If there are occurrence points for which the processing of steps S210 to S214 has not been completed, the process returns to step S210.

[0091] In step S218, as the calculation unit 106, the CPU 11 extracts the occurrence points where b < c for each of the counters c of the plurality of clusters C based on the calculation result of the counter in the above step S214. j of the counter c j for each of j < c j becomes.

[0092] In step S220, as the calculation unit 106, the CPU 11 totals the occurrence points extracted in the above step S213 and the above step S218 for each mesh of the map data.

[0093] In step S222, as the calculation unit 106, the CPU 11 calculates the risk level for each mesh based on the total result obtained in the above step S220.

[0094] In step S224, as the calculation unit 106, the CPU 11 outputs the risk level calculated in the above step S222 as a result.

[0095] Note that since the other configurations and operations of the display control device according to the second embodiment are the same as those of the first embodiment, the description thereof is omitted.

[0096] As described above, the display control device according to the second embodiment sets each of a plurality of ambulances as each of the centers of a plurality of clusters, and for each of the occurrence locations in the predicted distribution, identifies a target ambulance representing the ambulance with the shortest distance to the occurrence location from among the plurality of ambulances, and assigns the occurrence location to the center of the cluster corresponding to the target ambulance. Then, the display control device extracts each of the occurrence locations where the distance between the target ambulance and the occurrence location is equal to or greater than the threshold value, and extracts each of the occurrence locations belonging to a cluster in which the number of assigned occurrence locations is greater than a preset number. The display control device plots the extracted occurrence locations in the map data partitioned by a plurality of meshes and calculates the degree of danger. That is, according to the display control device of the second embodiment, it is possible to obtain the degree of danger associated with the occurrence location and the number of ambulances that can cover the occurrence location. This degree of danger may also be referred to as the degree of danger taking into account the number of occurrence locations that an ambulance can cover. Thereby, the degree of danger can be visualized in consideration of the ease of dispatch of ambulances.

[0097] <Third Embodiment>

[0098] Next, the third embodiment will be described. The third embodiment is different from the first and second embodiments in that the degree of dispatch indicating the ease of dispatch of ambulances is further displayed. Note that since the configuration of the display control device according to the third embodiment is the same as the configuration of the first embodiment, the same reference numerals are given and the description thereof is omitted.

[0099] The calculation unit 106 calculates, for each of the plurality of ambulances, the number of occurrence locations for which the ambulance is identified as the target ambulance.

[0100] Then, the calculation unit 106 calculates, for each of the plurality of ambulances, the degree of dispatch such that the higher the number of occurrence locations calculated for the ambulance, the higher the degree of dispatch indicating the ease of dispatch of the ambulance. Also, the calculation unit 106 calculates the degree of dispatch such that the lower the number of occurrence locations, the lower the degree of dispatch.

[0101] Then, the display control unit 108 controls the display unit 16 to further display the degree of activation calculated for each of the plurality of ambulances. As an example of the display, display of the numerical value of the degree of activation or display by color coding can be considered.

[0102] Next, the operation of the display control device 10 will be described.

[0103] FIG. 12 is a flowchart showing the flow of the display control process by the display control device 10. The display control process is performed by the CPU 11 reading the display control program from the ROM 12 or the storage 14, expanding it in the RAM 13, and executing it.

[0104] Steps S100 to S110 are executed in the same manner as in the first embodiment.

[0105] In step S410, the CPU 11, as the calculation unit 106, calculates the number of occurrence locations where the ambulance is specified as the target ambulance for each of the plurality of ambulances.

[0106] In step S411, the CPU 11, as the calculation unit 106, based on the calculation result obtained in step S410, for each of the plurality of ambulances, calculates the degree of activation such that the higher the number of occurrence locations calculated for the ambulance, the higher the degree of activation indicating the ease of dispatch of the ambulance. Also, the calculation unit 106 calculates the degree of activation such that the lower the number of occurrence locations, the lower the degree of activation.

[0107] In step S412, the CPU 11, as the display control unit 108, controls the display unit 16 to further display the degree of activation calculated for each of the plurality of ambulances obtained in step S411.

[0108] Note that since the other configurations and operations of the display control device of the third embodiment are the same as those of the first or second embodiment, the description thereof is omitted.

[0109] As described above, the display control device according to the third embodiment calculates, for each of a plurality of ambulances, the number of occurrence locations specified as the target ambulance. Then, for each of the plurality of ambulances, the display control device calculates the degree of readiness to dispatch according to the number of occurrence locations calculated for the ambulance, such that the higher the number of occurrence locations, the higher the degree of readiness to dispatch indicating the ease of dispatching the ambulance. Also, the display control device calculates the degree of readiness to dispatch such that the lower the number of occurrence locations, the lower the degree of readiness to dispatch. Then, the display control device controls the display unit to further display the degree of readiness to dispatch calculated for each of the plurality of emergency vehicles. Thereby, the ease of dispatching an ambulance can be further visualized. Also, when it becomes necessary to change the arrangement of some ambulances, such as when the demand for calls fluctuates in some areas, it may be configured to display, as candidate ambulances to be moved in order from the ambulance with fewer occurrence locations covered, i.e., the ambulance with a lower ease of dispatching. Or, it may be configured to display all ambulances whose number of occurrence locations covered is equal to or less than a predetermined threshold as candidate ambulances to be moved.

[0110] <Fourth Embodiment>

[0111] Next, the fourth embodiment will be described. The fourth embodiment is different from the first to third embodiments in that it performs a simulation of the emergency activities of ambulances and calculates the risk level of the area to which the occurrence location belongs based on the simulation results. Among the configurations of the display control device according to the fourth embodiment, the same configurations as those in the first to third embodiments are denoted by the same reference numerals and the description thereof is omitted.

[0112] For example, consider a situation where all the ambulances belonging to a fire department located near a certain area have been dispatched. In the fourth embodiment, one mesh in the map data shown in FIGS. 1 to 3 is regarded as one area. In such a situation, for example, a new call for an ambulance may occur in the area, and an ambulance belonging to a fire department far from the area may be dispatched to the area.

[0113] Therefore, the display control device according to the fourth embodiment performs a simulation of the emergency activities of the ambulance based on the predicted distribution of the occurrence points representing the points where the call for the ambulance occurs, and calculates the risk level of each area corresponding to the mesh included in the map data based on the simulation result. Specifically, the display control device according to the fourth embodiment calculates the distance between the occurrence point predicted to occur when the call for the ambulance occurs and the ambulance based on the simulation result. Then, the display control device according to the fourth embodiment extracts the occurrence points where the distance is greater than or equal to the threshold value d th or more, and increases the risk level of the area to which the occurrence point belongs.

[0114] The display control device 10 according to the first embodiment estimates the risk level of the area to which the occurrence point belongs based on whether the distance to the ambulance closest to each occurrence point is greater than or equal to the threshold value d th or more. However, the display control device 10 according to the first embodiment does not consider the number of occurrence points that can be handled by one ambulance per unit time. Therefore, in the first embodiment, when there are so many occurrence points that cannot be handled by one ambulance, the visualization of the risk level may not be appropriately performed.

[0115] In addition, the display control device 10 according to the second embodiment identifies a cluster to which the number of occurrence points assigned to the cluster corresponding to the ambulance is larger than a preset number, and extracts each of the occurrence points belonging to the cluster. Thereby, the risk level of the area exceeding the response capacity of the ambulance is visualized. However, the method of visualizing the risk level by the display control device 10 according to the second embodiment is also based on a simplified calculation method.

[0116] For example, consider the case where the number of occurrence locations within region A exceeds the response capacity of ambulance a deployed at the fire station near that region A. Specifically, when the first ambulance call occurs within region A and ambulance calls continue to occur in region A while ambulance a has been dispatched and is engaged in emergency activities, it means that the response capacity of ambulance a deployed at the fire station near region A has been exceeded. In this case, for example, assume that ambulance b in region B, which is relatively close to region A, heads towards region A. And if at this timing, casualties also occur in region B and ambulance calls are also made in region B, then ambulance c in region C, which is relatively close to region B, will head towards region B. Thus, even if initially the response capacity of ambulance b deployed at the fire station near region B has not been exceeded, as a result, ambulance calls may still occur in region B and the risk level in region B may increase.

[0117] Therefore, the display control device according to the fourth embodiment estimates the risk level of the region more precisely by simulating the above situation. This will be specifically described below.

[0118] FIG. 13 is a block diagram showing an example of the functional configuration of the display control device 410 according to the fourth embodiment.

[0119] As shown in FIG. 13, the display control device 410 includes, as functional components, an acquisition unit 100, a data storage unit 101, a demand prediction unit 102, a situation acquisition unit 104, an estimation unit 405, a simulation unit 406, a calculation unit 407, and a display control unit 108. Each functional component is realized by the CPU 21 reading the identification program stored in the ROM 22 or the storage 24, expanding it in the RAM 23, and executing it.

[0120] Based on the prediction distribution generated by the demand prediction unit 102, the estimation unit 405 estimates the occurrence time at which a call occurs at each of the plurality of occurrence locations, and associates the occurrence location with the occurrence time.

[0121] For example, in region A, an average of three ambulance calls may occur per unit time, and in region B, an average of one ambulance call may occur per unit time. If it can be assumed that the number of ambulance calls in each region follows a Poisson distribution, it is known that the intervals between the occurrence times of ambulance calls follow an exponential distribution. In this case, a method of pseudo-simulating the occurrence times of ambulance calls is also known in the field of probability and statistics.

[0122] Therefore, the estimation unit 405 estimates the occurrence time representing the time when an ambulance call occurs for each of the plurality of occurrence locations using known techniques. For example, the estimation unit 405 estimates the occurrence time representing the time when an ambulance call occurs at each of the plurality of occurrence locations by performing sampling based on the probability distribution generated by known techniques.

[0123] Next, for each combination of the occurrence location and the occurrence time for each of the plurality of occurrence locations, the estimation unit 405 calculates the required time representing the time required for the ambulance dispatched to the occurrence location at the occurrence time. Then, the estimation unit 405 associates the combination of the occurrence location and the occurrence time and the calculated required time for each of the plurality of occurrence locations. Note that this required time is the total time such as the time required for the emergency team to respond to the scene after the ambulance heads to the scene and the time required for transportation from the scene to the hospital.

[0124] Note that, for example, if there is some tendency known in advance for each region, this required time is set based on that tendency. Or, the estimation unit 405 may set a more simplified time, such as the average across all regions, as the required time.

[0125] Then, the estimation unit 405 rearranges the combinations of the occurrence location, the occurrence time, and the required time for each of the plurality of occurrence locations in ascending order of the occurrence time, and creates an occurrence time table as shown in FIG. 14. The estimation unit 405 stores the generated occurrence time table in the data storage unit 101. Note that the number of the occurrence time table is the identification number of the occurrence location.

[0126] The simulation unit 406 reads out the activity status indicating the availability of each of a plurality of ambulances from the data storage unit 101. Next, the simulation unit 406 reads out the occurrence time of each of a plurality of occurrence locations, which is the estimation result by the estimation unit 405 and stored in the data storage unit 101. Then, based on the occurrence time of each of the plurality of occurrence locations and the activity status of each of the plurality of ambulances, the simulation unit 406 performs a simulation of an emergency activity in which any one of the available ambulances among the plurality of ambulances departs for the occurrence location at the occurrence time for each of the plurality of occurrence locations.

[0127] Specifically, the simulation unit 406 performs a simulation of an emergency activity in which the available ambulance that takes the shortest time to reach the occurrence location or the available ambulance with the shortest distance to the occurrence location among the available ambulances departs for the occurrence location at the occurrence time.

[0128] Next, for each of the plurality of occurrence locations, the simulation unit 406 calculates a round-trip travel time corresponding to the travel distance from the departing ambulance to the occurrence location.

[0129] Then, for each of the plurality of occurrence locations, the simulation unit 406 refers to the occurrence time table stored in the data storage unit 101, and adds the required time and the round-trip travel time to the occurrence time associated with the occurrence location to calculate a correspondence completion time representing the time when the dispatched ambulance completes the correspondence. Then, the simulation unit 406 stores a simulation table as shown in FIG. 15 in the data storage unit 101. Note that the activity status of the ambulance in the simulation table reflects the simulation result executed by the simulation unit 406.

[0130] The calculation unit 407 selects, from a plurality of occurrence locations, the one with the shortest distance to the occurrence location and the distance between the available ambulance and the occurrence location is less than or equal to the threshold value d thExtract the occurrence locations as described above, and calculate the risk level so that the risk level of the area to which the extracted occurrence location belongs becomes high.

[0131] Specifically, the calculation unit 407 increments a risk level counter by 1 for the area to which the occurrence location belongs where the distance between the occurrence location and the available ambulance is greater than or equal to the threshold value d th Note that for an ambulance that has already been called and is on a mission, the calculation unit 407 sets that the ambulance is not available during the time period until the corresponding completion time in the simulation table. Also, it is assumed that an ambulance that has already been called and is on a mission returns to the location of the original fire station after the corresponding completion time has passed. The calculation unit 407 calculates the risk level of the area to which the occurrence location belongs for each of the plurality of occurrence locations existing in the occurrence time table, assuming that an ambulance call has occurred at the occurrence location.

[0132] Similar to the first embodiment, the display control unit 108 controls the display unit 16 to display the position information of the plurality of ambulances acquired by the situation acquisition unit 104, the prediction distribution generated by the demand prediction unit 102, and the risk level calculated by the calculation unit 407.

[0133] Next, the operation of the display control device 410 of the fourth embodiment will be described.

[0134] FIG. 16 is a flowchart showing the flow of the display control process by the display control device 410. The display control process is performed by the CPU 11 reading the display control process program from the ROM 12 or the storage 14, expanding it in the RAM 13, and executing it.

[0135] In step S500, the CPU 11, as the estimation unit 405, estimates the occurrence time at which an ambulance call occurs for each of the plurality of occurrence locations based on the prediction distribution generated by the demand prediction unit 102.

[0136] In step S502, the CPU 11, as the estimation unit 405, calculates, for each of a plurality of occurrence locations, the required time for an ambulance that has been dispatched to the occurrence location at the occurrence time to respond.

[0137] In step S504, the CPU 11, as the estimation unit 405, associates, for each of the plurality of occurrence locations, the occurrence location, the occurrence time estimated in step S500, and the required time calculated in step S502, and stores the data in the data storage unit 101 as an occurrence time table.

[0138] In step S506, the CPU 11, as the simulation unit 406, reads out the activity status of each of the plurality of ambulances from the data storage unit 101. Next, in step S506, the CPU 11, as the simulation unit 406, reads out the occurrence time table stored in the data storage unit 101. Then, in step S506, the CPU 11, as the simulation unit 406, sets one occurrence location from the plurality of occurrence locations stored in the occurrence time table.

[0139] In step S508, the CPU 11, as the simulation unit 406, refers to the activity status of each of the plurality of ambulances read out in step S506, and selects one ambulance from among the ambulances that can be dispatched. Specifically, the CPU 11, as the simulation unit 406, selects the ambulance that can be dispatched and has the shortest time required to reach the occurrence location set in step S506 or the ambulance that can be dispatched and has the shortest distance to the occurrence location set in step S506.

[0140] In step S510, the CPU 11, as the simulation unit 406, calculates a round-trip time corresponding to the moving distance from the ambulance selected in step S508 to the occurrence location set in step S506.

[0141] In step S512, as the simulation unit 406, the CPU 11 refers to the generation time table read in step S506, and adds the round-trip travel time calculated in step S510 and the required time in the generation time table to the generation time associated with the generation point set in step S506, thereby calculating the completion time representing the time when the dispatched ambulance completes the response.

[0142] In step S514, as the calculation unit 407, the CPU 11 determines whether the distance between the available ambulance with the shortest distance to the generation point among the plurality of ambulances and the generation point set in step S506 is greater than or equal to the threshold value d th or not. If the distance between the available ambulance and the generation point is greater than or equal to the threshold value d th or more, the process proceeds to step S515. On the other hand, if the distance between the available ambulance and the generation point is less than the threshold value d th or less, the process proceeds to step S516.

[0143] In step S515, as the calculation unit 407, the CPU 11 increases the risk level for the area to which the generation point set in step S506 belongs so that the risk level of the area becomes high. Specifically, as the calculation unit 407, the CPU 11 increments the risk level counter by one for the area to which the generation point belongs.

[0144] In step S516, as the calculation unit 407, the CPU 11 determines whether the processes of steps S506 to S515 have been executed for all the generation points existing in the generation time table. If the processes of steps S506 to S515 have been executed for all the generation points existing in the generation time table, the process proceeds to step S518. On the other hand, if there is a generation point for which the processes of steps S506 to S515 have not been executed, the process returns to step S506.

[0145] In step S518, the CPU 11 controls the display control unit 108 to cause the display unit 16 to display the risk level for each area calculated in step S516.

[0146] If it can be assumed that the number of occurrence locations follows a Poisson distribution, as described above, the intervals between occurrence times follow an exponential distribution and the occurrence times can be pseudo-simulated. In this case, although the patterns of the time-series data have a common tendency, many patterns are conceivable. Therefore, a plurality of time-series data may be created, simulations may be performed using all of them, and the average risk level for each area may be calculated to enhance statistical reliability.

[0147] Note that since the other configurations and operations of the display control device according to the fourth embodiment are the same as those of the first, second, or third embodiment, the description thereof is omitted.

[0148] As described above, the display control device according to the fourth embodiment estimates the occurrence time at which a call occurs at each of a plurality of occurrence locations based on the predicted distribution of the occurrence locations representing the locations where calls for ambulances occur. The display control device performs, for each of the plurality of occurrence locations, a simulation of an emergency operation in which one of the plurality of ambulances that can be dispatched departs for the occurrence location at the occurrence time, based on the occurrence time of each of the plurality of occurrence locations, which is the estimation result, and the activity status of each of the plurality of ambulances. The display control device extracts, from the plurality of occurrence locations, the occurrence locations where the distance between the ambulance that can be dispatched and the occurrence location is equal to or greater than a threshold value based on the simulation result, and calculates the risk level so that the risk level of the area to which the extracted occurrence location belongs becomes high. The display control device controls the display unit to display the calculated risk level. Thereby, it is possible to visualize the locations that require time until the arrival of the ambulance. Specifically, since the risk level of the area represents the degree that requires time until the arrival of the ambulance, it is possible to visualize the locations that require time until the arrival of the ambulance by accurately calculating the risk level.

[0149] In addition, by the above processing, the occurrence locations where the distance to the available ambulances is equal to or greater than the threshold value are counted for each region, and the risk level for each region can be visualized more precisely than in the first to third embodiments.

[0150] Note that in each of the above embodiments, the display control process executed by the CPU by loading software (program) may be executed by various processors other than the CPU. Examples of the processor in this case include a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing, such as an FPGA (Field-Programmable Gate Array), and a dedicated electric circuit such as an ASIC (Application Specific Integrated Circuit) having a circuit configuration designed specifically to execute specific processing. Further, the display control process may be executed by one of these various processors, or may be executed by a combination of two or more processors of the same type or different types (for example, a plurality of FPGAs, and a combination of a CPU and an FPGA, etc.). Further, the hardware structure of these various processors is, more specifically, an electric circuit combining circuit elements such as semiconductor elements.

[0151] In addition, in each of the above embodiments, the mode in which the display control processing program is pre-stored (installed) in the storage 14 has been described, but the present invention is not limited to this. The program may be provided in a form stored in a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. Further, the program may be in a form downloaded from an external device via a network.

[0152] In the above-described embodiment, the case of targeting an emergency vehicle has been described as an example, but it is not limited thereto. For example, if a moving object is called according to a predetermined demand, the present embodiment can be applied. For this reason, in the above-described embodiment, the case where the emergency vehicle is an ambulance has been described as an example, but it is not limited thereto. For example, the emergency vehicle may be a police vehicle.

[0153] In the above-described embodiment, the case of calculating the degree of danger according to the distance representing the distance between the emergency vehicle and the occurrence point has been described as an example, but it is not limited thereto. For example, the degree of danger may be calculated according to the time required from the occurrence of the call for the emergency vehicle until the emergency vehicle arrives at the occurrence point. In this case, for example, when the time required from the occurrence of the call for the emergency vehicle until the emergency vehicle arrives at the occurrence point is equal to or greater than a predetermined threshold value, the occurrence point is extracted and plotted on the map data.

[0154] In the above-described embodiment, the case of calculating the degree of danger using the latitude and longitude information of the occurrence point representing the point where the call for the ambulance occurs has been described as an example, but it is not limited thereto. For example, the degree of danger may be calculated by treating one mesh in the map data as one occurrence point. In this case, for example, an expected value of the ambulance being called at one mesh is calculated based on past information, and the degree of danger may be calculated using the expected value.

[0155] In the above-described second embodiment, the case of extracting the occurrence points belonging to a cluster in which the number of the belonging occurrence points is larger than a preset number and calculating the degree of danger based on the extracted occurrence points has been described as an example, but it is not limited thereto. For example, the ambulance corresponding to the cluster in which the number of the belonging occurrence points is larger than a preset number may be excluded, and the excluded ambulance is set to be unavailable for dispatch, and clustering may be performed again. In this case, for each occurrence point i whose belonging to cluster C j has not been determined, the distance d i and the target ambulance a iis calculated again. Then, the target ambulance a i Cluster C of ambulance j corresponding to j The occurrence point i is assigned to the distance d i is the threshold d th If it is greater than or equal to the distance d i is the threshold d th If the value is less than the cluster C to which the occurrence point i belongs, j Counter c j is added. By repeating these processes, the risk level is calculated more appropriately. It is possible to end such a repetitive process when a termination condition is satisfied, such as, for example, a predetermined number or more of occurrence points are extracted, a predetermined number or less of occurrence points belong to one cluster, or the number of occurrence points not belonging to any cluster is a predetermined number or less. In addition, when the occurrence point is the subject, it may be determined that the occurrence point belongs to any cluster, or that the occurrence point cannot belong to any cluster (for example, when there is no ambulance that can cover the occurrence point, or when the distance from any ambulance exceeds a threshold value), or the like.

[0156] In the above embodiment, the risk level is calculated for each mesh, but the present invention is not limited to this. For example, the risk level may be calculated for each point. Alternatively, the risk level may be displayed in a format such as contour lines.

[0157] Although the embodiments of the present disclosure have been described above, the present disclosure is not limited to the above-described embodiments, and the components of the embodiments and various modified examples may be appropriately combined.

[0158] The following supplementary notes are further disclosed regarding the above embodiment.

[0159] (Additional note 1) Memory, at least one processor coupled to the memory; Including, The processor is configured to: control the display unit to display the position information of the emergency vehicle, the predicted distribution of the occurrence points representing the points where the calls for the emergency vehicle occur, and the degree of danger corresponding to the time required for the emergency vehicle to arrive at the occurrence point after the call for the emergency vehicle occurs or the distance between the emergency vehicle and the occurrence point. A display control device configured as described above.

[0160] (Additional item 2) A non-transitory storage medium storing a program executable by a computer to execute a display control process, wherein the display control process controls the display unit to display the position information of the emergency vehicle, the predicted distribution of the occurrence points representing the points where the calls for the emergency vehicle occur, and the degree of danger corresponding to the time required for the emergency vehicle to arrive at the occurrence point after the call for the emergency vehicle occurs or the distance between the emergency vehicle and the occurrence point. Non-transitory storage medium.

[0161] (Additional item 3) a memory, at least one processor connected to the memory, comprising: wherein the processor estimates the occurrence time of the call at each of the plurality of occurrence points based on the predicted distribution of the occurrence points representing the points where the calls for the emergency vehicle occur; performs a simulation of an emergency activity in which any one of the available emergency vehicles among the plurality of emergency vehicles departs for the occurrence point at the occurrence time for each of the plurality of occurrence points based on the occurrence time of each of the plurality of occurrence points obtained as the estimation result and the activity status of each of the plurality of emergency vehicles; extracts, from the plurality of occurrence points, the occurrence points where the distance between the available emergency vehicle and the occurrence point is equal to or greater than a threshold based on the simulation result, and calculates the degree of danger so that the degree of danger of the area to which the extracted occurrence points belong becomes high. Control to cause the display unit to display the calculated risk level. A display control device configured as such.

[0162] (Appended Claim 4) A non - transitory storage medium storing a program executable by a computer to execute display control processing, wherein the display control processing Based on the predicted distribution of occurrence points representing the points where calls for emergency vehicles occur, for each of the plurality of occurrence points, estimates the occurrence time at which a call occurs at the occurrence point, Based on the occurrence time of each of the plurality of occurrence points which is the estimation result and the activity status of each of the plurality of emergency vehicles, for each of the plurality of occurrence points, simulates an emergency activity in which any one of the deployable emergency vehicles among the plurality of emergency vehicles deploys to the occurrence point at the occurrence time, Based on the simulation result, extracts occurrence points from the plurality of occurrence points where the distance between the deployable emergency vehicle and the occurrence point is equal to or greater than a threshold value, and calculates the risk level so that the risk level of the area to which the extracted occurrence point belongs becomes high, Control to cause the display unit to display the calculated risk level. Non - transitory storage medium.

Explanation of Signs

[0163] 100 Acquisition unit 101 Data storage unit 102 Demand prediction unit 104 Situation acquisition unit 106,407 Calculation unit 108 Display control unit 405 Estimation unit 406 Simulation unit 10,410 Display control device

Claims

1. Based on the predicted distribution of the occurrence locations representing the locations where the calls for emergency vehicles occur, for each of the plurality of the occurrence locations, an estimation unit that estimates the occurrence time when a call occurs at the occurrence location; Based on the occurrence time of each of the plurality of the occurrence locations, which is the estimation result by the estimation unit, and the activity status of each of the plurality of emergency vehicles, for each of the plurality of the occurrence locations, a simulation unit that executes a simulation of an emergency activity in which any one of the plurality of deployable emergency vehicles deploys to the occurrence location at the occurrence time; Based on the simulation result by the simulation unit, from the plurality of the occurrence locations, an extraction unit that extracts an occurrence location where the distance between the deployable emergency vehicle and the occurrence location is equal to or greater than a threshold value, and a calculation unit that calculates the risk level such that the risk level of the area to which the extracted occurrence location belongs becomes high; A display control unit that controls to display the risk level calculated by the calculation unit on a display unit; A display control device comprising the same.

2. For each of the plurality of the occurrence locations, the estimation unit calculates a required time representing the time required for the emergency vehicle that deploys to the occurrence location at the occurrence time for each combination of the occurrence location and the occurrence time; For each of the plurality of the occurrence locations, the simulation unit calculates a round-trip travel time corresponding to the travel distance from the deployable emergency vehicle to the occurrence location by executing the simulation of the emergency activity, and calculates a correspondence completion time representing the time when the deployable emergency vehicle completes the correspondence by adding the required time and the round-trip travel time to the occurrence time; For each of the plurality of emergency vehicles, the calculation unit sets that the emergency vehicle is not deployable in the time period until the correspondence completion time, extracts an occurrence location where the distance to the occurrence location is the shortest and the distance between the deployable emergency vehicle and the occurrence location is equal to or greater than a threshold value, and calculates the risk level such that the risk level of the area to which the extracted occurrence location belongs becomes high. The display control device according to Claim 1.

3. The simulation unit performs a simulation of an emergency activity in which an emergency vehicle that can be dispatched and has the shortest time required to reach the occurrence point or an emergency vehicle that can be dispatched and has the shortest distance to the occurrence point departs for the occurrence point at the occurrence time. The display control device according to claim 1 or claim 2.

4. The display control unit controls the display unit to display the position information of a plurality of emergency vehicles, the prediction distribution, and the risk level calculated by the calculation unit. The display control device according to any one of claims 1 to 3.

5. Based on the predicted distribution of the occurrence points representing the points where emergency vehicle calls occur, for each of the plurality of occurrence points, the occurrence time when a call occurs at the occurrence point is estimated. Based on the occurrence time of each of the plurality of occurrence points which is the estimation result and the activity status of each of the plurality of emergency vehicles, for each of the plurality of occurrence points, a simulation of an emergency activity in which any one of the plurality of emergency vehicles that can be dispatched departs for the occurrence point at the occurrence time is executed. Based on the simulation result, from the plurality of occurrence points, occurrence points where the distance between the dispatchable emergency vehicle and the occurrence point is equal to or greater than a threshold value are extracted, and the risk level is calculated so that the risk level of the area to which the extracted occurrence points belong becomes high. Controls the display unit to display the calculated risk level. A display control method executed by a computer.

6. A display control program for causing a computer to function as each part of the display control device according to any one of claims 1 to 4.

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