Arrangement search device, arrangement search method, and arrangement search program

Abstract

This layout search device comprises: a resource data acquisition unit for acquiring resource data that includes the latest resource location and dispatchability; a layout plan creation unit for creating a plurality of resource layout plans, using the resource data, that corresponds to an event that occurs; a layout evaluation unit for adding an evaluation value to each of the plurality of layout plans, using the resource data; and a layout plan selection unit for selecting an effective layout plan from among the plurality of layout plans, on the basis of the evaluation value.

Description

[Technical Field]

[0001] The disclosed technology relates to a layout searching device, a layout searching method, and a layout searching program. [Background technology]

[0002] There are known techniques for predicting the occurrence of events in each region and optimally allocating multiple resources to respond to the events. For example, Non-Patent Document 1 proposes a method for predicting emergency demand on a regional basis and optimally allocating multiple emergency teams (ambulances) accordingly so as to minimize the time to arrive at the scene or the distance traveled to reach the scene. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] "Confirming the effectiveness of an optimal ambulance operation system using emergency big data ~ Aiming to reduce ambulance transport times through real-time emergency demand forecasts, etc." https: / / www.ntt.co.jp / news2018 / 1811 / 181126a.html Summary of the Invention [Problem to be solved by the invention]

[0004] However, suppose we want to predict the number of injured people per unit time for each regional mesh of 500m squares or 1km squares, and appropriately allocate multiple emergency teams (ambulances) to minimize the time or distance they need to travel to reach the scene. The locations where emergency teams can be deployed are the multiple fire stations within the target region, and the emergency team closest to the scene of an injured person will be dispatched. In this case, since we are trying to appropriately allocate multiple emergency teams among multiple fire stations, it becomes a discrete optimization problem.

[0005] However, for example, in a major city in Japan, there may be around 50 fire stations and 40 ambulance teams. In this case, if we simplify the situation by assuming that each fire station can deploy any number of ambulance teams, the number of deployment patterns becomes 50 to the 40th power. Therefore, it is difficult to find an optimal solution to such a problem in real time. Furthermore, even if the destination is considered in a continuous space rather than a discrete location such as a fire station, a discrete element arises in determining which ambulance team is best to mobilize from among multiple teams, and it is desirable to be able to decide which ambulance team to mobilize.

[0006] On the other hand, even if the occurrence of injuries and illnesses in each regional grid is predicted, the results may be inaccurate. Therefore, it is desirable to deploy emergency teams in a robust manner that can be expected to be effective even if the prediction is slightly off.

[0007] The disclosed technology has been made in consideration of the above points, and aims to provide an arrangement search device, an arrangement search method, and an arrangement search program that can determine a robust resource arrangement that is likely to be effective even if the predicted results of event occurrence are slightly off, during the short time period in which an effective resource arrangement must be determined. [Means for solving the problem]

[0008] A placement search device according to one aspect of the present disclosure includes a resource data acquisition unit that acquires resource data including the latest resource locations and availability of deployment, a placement plan creation unit that uses the resource data to create multiple placement plans for resources corresponding to an event that occurs, a placement evaluation unit that uses the resource data to assign an evaluation value to each of the multiple placement plans, and a placement plan selection unit that selects an effective placement plan from the multiple placement plans based on the evaluation value assigned to each of the multiple placement plans.

[0009] The system may further include a sample output unit that outputs, based on the latest event occurrence data, data on future event occurrences that may occur under predetermined conditions as main samples. In this case, the placement plan creation unit uses the resource data and the main samples to create multiple placement plans for resources corresponding to events that may occur. In the second embodiment described below, two forms are shown: (1) dynamic non-fire station placement and (2) dynamic fire station placement. In the case of dynamic non-fire station placement, a placement plan is created using the main samples, while in the case of dynamic fire station placement, a placement plan is created without using the main samples.

[0010] A placement search method according to one aspect of the present disclosure acquires resource data including the latest resource locations and availability for deployment, uses the resource data to create multiple placement plans for resources corresponding to an event that occurs, uses the resource data to assign an evaluation value to each of the multiple placement plans, and selects an effective placement plan from among the multiple placement plans based on the evaluation value assigned to each of the multiple placement plans.

[0011] A placement search program according to one aspect of the present disclosure causes a computer to acquire resource data including the latest resource locations and availability for deployment, use the resource data to create multiple resource placement plans corresponding to an event that occurs, use the resource data to assign an evaluation value to each of the multiple placement plans, and select an effective placement plan from among the multiple placement plans based on the evaluation value assigned to each of the multiple placement plans. [Effects of the Invention]

[0012] According to the disclosed technology, when there is a request to shorten the average distance it takes for resources (e.g., emergency teams) to arrive at the scene, it is possible to calculate in real time which resources should be moved and where those resources should be moved to, and to obtain a deployment plan that is expected to meet the request. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a block diagram showing an example of a hardware configuration of a layout searching device according to a first embodiment. [Figure 2] 1 is a block diagram showing an example of a functional configuration of a layout searching device according to a first embodiment. [Figure 3] FIG. 10 is a diagram showing an example of an injury occurrence data sequence according to the embodiment. [Figure 4] FIG. 10 is a diagram showing an example of a pseudo-generated data sequence according to the embodiment. [Figure 5] FIG. 10 is a diagram showing another example of a pseudo-occurrence data sequence according to the embodiment. [Figure 6] 10 is a graph showing an example of the probability of injury or illness occurring calculated by MCMC according to the embodiment. [Figure 7] FIG. 10 is a diagram illustrating an example of ambulance data according to the embodiment. [Figure 8] FIG. 4 is a diagram illustrating an example of fire station data according to the embodiment. [Figure 9] FIG. 1 is a diagram illustrating an example of an ambulance placement plan according to an embodiment. [Figure 10] FIG. 1 is a diagram illustrating an example of an original ambulance arrangement according to an embodiment. [Figure 11] FIG. 10 is a diagram illustrating an example of an effective layout plan that satisfies an evaluation criterion according to the embodiment. [Figure 12] FIG. 10 is a diagram illustrating an example of a placement plan that does not satisfy the evaluation criteria according to the embodiment. [Figure 13] 10 is a flowchart showing an example of the flow of processing in Phase 1 by the placement search program according to the first embodiment. [Figure 14] 10 is a flowchart showing an example of the flow of an evaluation process according to the first embodiment. [Figure 15] 10 is a flowchart showing an example of the flow of processing in Phase 2 by the placement search program according to the first embodiment. [Figure 16] FIG. 10 is a block diagram showing an example of the functional configuration of a layout searching device according to a second embodiment. [Figure 17] FIG. 4 is a diagram illustrating an example of resource data according to the embodiment. [Figure 18]FIG. 10 is a diagram illustrating an example of a main sample according to the embodiment. [Figure 19] FIG. 4 is a diagram illustrating an example of fire station data according to the embodiment. [Figure 20] FIG. 10 is a diagram illustrating an example of correspondence data between ambulances and fire stations according to the embodiment. [Figure 21] FIG. 2 is a diagram illustrating an example of a moving vehicle candidate list according to the embodiment. [Figure 22] FIG. 10 is a diagram illustrating an example of a placement plan list according to the embodiment. [Figure 23] FIG. 10 is a diagram showing an example of an evaluation result according to the embodiment. [Figure 24] 10 is a flowchart showing an example of a processing flow by a layout search program according to the second embodiment. [Figure 25] FIG. 10 is a diagram illustrating another example of fire station data according to the embodiment. [Figure 26] FIG. 10 is a diagram illustrating another example of a moving vehicle candidate list according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of the disclosed technology will be described below with reference to the drawings. Note that in each drawing, the same or equivalent components and parts are given the same reference numerals. Also, the dimensional proportions in the drawings are exaggerated for the convenience of explanation and may differ from the actual proportions.

[0015] In this embodiment, an example will be described in which emergency demand is predicted for each region, and multiple emergency teams (ambulances) are optimally allocated so that the arrival time or the distance to the scene is as short as possible. However, this embodiment can be applied to any form in which resources are optimally allocated in response to an event that occurs.

[0016] [First embodiment] FIG. 1 is a block diagram showing an example of the hardware configuration of a layout searching device 10 according to the first embodiment.

[0017] 1, the layout searching 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 each other via a bus 18 so as to be able to communicate with each other.

[0018] The CPU 11 is a central processing unit that executes various programs and controls each component. 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 work area. The CPU 11 controls the above components and performs various arithmetic processing in accordance with the program stored in the ROM 12 or the storage 14. In this embodiment, the ROM 12 or the storage 14 stores an arrangement search program for searching for an optimal arrangement of resources.

[0019] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores programs or data as a working area. The storage 14 is configured with an HDD (Hard Disk Drive) or SSD (Solid State Drive) and stores various programs including the operating system and various data.

[0020] The input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to input various information to the device itself.

[0021] The display unit 16 is, for example, a liquid crystal display, and displays various information. The display unit 16 may also function as the input unit 15 by adopting a touch panel system.

[0022] The communication interface 17 is an interface for the device itself to communicate with other external devices. For this communication, a wired communication standard such as Ethernet (registered trademark) or FDDI (Fiber Distributed Data Interface) or a wireless communication standard such as 4G, 5G, or Wi-Fi (registered trademark) is used.

[0023] The layout searching device 10 according to this embodiment is implemented by a general-purpose computer such as a server computer or a personal computer (PC).

[0024] Next, with reference to FIG. 2, the functional configuration of the layout searching device 10 according to the first embodiment will be described.

[0025] FIG. 2 is a block diagram showing an example of the functional configuration of the arrangement searching device 10 according to the first embodiment.

[0026] 2, the layout search device 10 has, as functional components, a first sample output unit 101, a layout plan creation unit 102, a first layout evaluation unit 103, a second sample output unit 104, a second layout evaluation unit 105, and a result output unit 106. Each functional component is realized by the CPU 11 reading out a layout search program stored in the ROM 12 or storage 14, expanding it in the RAM 13, and executing it.

[0027] Furthermore, first sample output section 101 includes first main sample output section 101A and first auxiliary sample output section 101B, and second sample output section 104 includes second main sample output section 104A and second auxiliary sample output section 104B.

[0028] The storage 14 stores ambulance data 141, fire station data 142, past event occurrence data 143, effective deployment plan 144, and latest event occurrence data 145. The ambulance data 141, fire station data 142, past event occurrence data 143, effective deployment plan 144, and latest event occurrence data 145 may be stored in an external storage device.

[0029] The past event occurrence data 143 is a data string of event occurrence data obtained in the past. Here, "past" means a certain period of time prior to the current time when the location search is performed, such as the past few months or past few years. The latest event occurrence data 145 is a data string of the latest event occurrence data. Here, "latest" means a certain period of time immediately prior to the current time when the location search is performed, such as the past few days or past few months. In other words, the latest period is a period shorter than the past period. Note that the event occurrence data is, for example, injured person occurrence data (that is, data indicating the longitude and latitude at which an injured person occurred, what year, what month, what day of the week, what hour, and what minute).

[0030] Based on past event occurrence data 143, first sample output section 101 outputs event occurrence data that may occur under predetermined conditions as a first main sample, and outputs event occurrence data that may occur under conditions similar to the predetermined conditions as a first auxiliary sample. Note that the predetermined conditions may be, for example, conditions such as 10:00 a.m. on a weekday in September, and conditions similar to the predetermined conditions may be, for example, conditions such as 10:00 a.m. on a weekday in August and October. In this embodiment, first main sample output section 101A outputs the first main sample, and first auxiliary sample output section 101B outputs the first auxiliary sample.

[0031] The first main sample may be represented as a pseudo-occurrence data sequence, for example, obtained by calculating an event occurrence frequency for each specific region from a data sequence that actually occurred under predetermined conditions, and then simulated according to the calculated event occurrence frequency. Similarly, the first auxiliary sample may be represented as a pseudo-occurrence data sequence, for example, obtained by calculating an event occurrence frequency for each specific region from a data sequence that actually occurred under conditions similar to the predetermined conditions, and then simulated according to the calculated event occurrence frequency. The first main sample may be represented as a pseudo-occurrence data sequence, for example, obtained by calculating an event occurrence probability as a random variable from a data sequence that actually occurred under predetermined conditions, and then simulated according to the calculated random variable. Similarly, the first auxiliary sample may be represented as a pseudo-occurrence data sequence, for example, obtained by calculating an event occurrence probability as a random variable from a data sequence that actually occurred under conditions similar to the predetermined conditions, and then simulated according to the calculated random variable. Specific examples of these event occurrence frequencies and event occurrence probabilities will be described later.

[0032] The placement plan creation unit 102 creates multiple placement plans for resources corresponding to events that may occur. In this embodiment, the ambulance data 141 and the fire station data 142 are used to create multiple placement plans for ambulances when ambulances are to be placed at fire stations.

[0033] The first placement evaluation unit 103 receives the first main sample, the first auxiliary sample, and multiple placement plans as input, and evaluates whether each of the multiple placement plans satisfies a predetermined evaluation criterion when the first main sample and the first auxiliary sample are applied to each of the multiple placement plans. The first placement evaluation unit 103 stores the placement plans that satisfy the predetermined evaluation criterion in the storage 14 as effective placement plans 144. The processing up to this point is "Phase 1."

[0034] Next, second sample output unit 104 outputs future event occurrence data that may occur under predetermined conditions as a second main sample, and outputs future event occurrence data that may occur under conditions similar to the predetermined conditions as a second auxiliary sample, based on latest event occurrence data 145. Note that the predetermined conditions and the conditions similar to the predetermined conditions are the same as the conditions in first sample output unit 101. In this embodiment, second main sample output unit 104A outputs the second main sample, and second auxiliary sample output unit 104B outputs the second auxiliary sample.

[0035] The second placement evaluation unit 105 receives the second main sample, the second auxiliary sample, and the effective placement plan 144 as input, and when the second main sample and the second auxiliary sample are applied to each of the effective placement plans 144, it re-evaluates whether each of the effective placement plans 144 satisfies a predetermined evaluation criterion.

[0036] The result output unit 106 outputs an effective allocation plan that satisfies a predetermined evaluation criterion as an optimal allocation of resources as a result of the re-evaluation by the second allocation evaluation unit 105. The processing up to this point is "Phase 2".

[0037] As described above, this embodiment broadly includes two implementation phases, Phase 1 and Phase 2. Phase 1 is a phase in which a large number of effective ambulance deployment patterns are found in advance from past data on the occurrence of injured or ill persons. Phase 1 is carried out, for example, at the beginning of a fiscal year, every quarter, or once a month. Phase 2 is a phase in which, for example, at a fixed time each day, the occurrence of injured or ill persons in the near future is predicted from the latest data on the occurrence of injured or ill persons, the most effective deployment pattern is found from the effective deployment patterns found in advance in Phase 1, and deployment changes are made accordingly.

[0038] First, the processing of Phase 1 will be described using an example case in which, for example, optimal allocation of ambulances in the 10:00 a.m. time slot on a weekday in September is sought in August. First main sample output unit 101A outputs, as first main samples, multiple occurrence data sequences that are predicted to be most likely to occur under predetermined conditions (e.g., between 10:00 a.m. on a weekday in September) based on past injury occurrence data accumulated up to August. Furthermore, first auxiliary sample output unit 101B outputs, as first auxiliary samples, multiple occurrence data sequences that are predicted to be most likely to occur under conditions similar to the predetermined conditions (e.g., between 10:00 a.m. on a weekday in August and October) based on past injury occurrence data accumulated up to August.

[0039] There are several possible methods for outputting the first main sample and the first auxiliary sample. The first and simplest method is to output the actual occurrence data sequence from 10:00 a.m. on weekdays in September for the past few years as the first main sample, and output the actual occurrence data sequence from 10:00 a.m. on weekdays in August and October for the past few years as the first auxiliary sample. When processing in this way, it is assumed that a similar pattern of injury occurrence occurs every year in the same month, on the same day of the week, and in the same time period, and that the occurrence data sequences for August and October are also similar to those for September because, for example, the average daily temperatures are relatively close.

[0040] FIG. 3 is a diagram showing an example of an injury occurrence data sequence according to this embodiment.

[0041] The sick person occurrence data string shown in FIG. 3 is an example of an occurrence data string for one day between 10:00 and 10:00 on a weekday, output by first main sample output unit 101A as the first main sample using the method described above. In the example of FIG. 3, information on the year, month, date, and day of the week is not output because it is not necessary for subsequent processing. Although the example of FIG. 3 shows only one day's worth of data, in reality, data for multiple days (for example, if the number of days in question is 100, then 100 days' worth of data) is output. The same is true for the sick person occurrence data string output by first auxiliary sample output unit 101B as the first auxiliary sample. With this method, the number of days output by first auxiliary sample output unit 101B is generally greater than the number of days output by first main sample output unit 101A.

[0042] Another simple second method is to output the actual occurrence data sequence from the 10:00 hour on weekdays in September for the past few years as the first main sample, and output the actual occurrence data sequence from the 10:00 hour on weekdays from January to December, including the 10:00 hour on weekdays in September for the past few years, as the first auxiliary sample.

[0043] Next, a third method will be described, which involves creating and using a pseudo-occurrence data sequence. Specifically, the first main sample output unit 101A calculates the frequency of injury or illness occurrence in a certain area (e.g., every 500 m square, every 1 km square, etc.) from actual occurrence data sequences from the 10:00 a.m. time slot on weekdays in September for the past few years, and generates a pseudo-occurrence data sequence based on that frequency. The injury or illness occurrence frequency is an example of an event occurrence frequency. For example, if the past occurrence data sequence used covers, say, 100 days, and the total number of injury or illness occurrences during that time is, say, 30, the frequency of injury or illness occurrence in a certain 500 m square area during the 10:00 a.m. time slot on weekdays in September can be calculated as 30 / 100 = 0.3. Similarly, the occurrence frequency can be calculated for all areas of the target region, and pseudo-occurrence data sequences corresponding to those values ​​can be generated, and the data sequences for each area can be combined and used. The locations where injured people will occur within a target area may be determined by plotting actual locations of past incidents, performing kernel density estimation, and generating the locations based on that density. The advantage of creating a pseudo-incidence data sequence is that it generates more samples than actual incident data sequences that have occurred in the past, allowing for robust verification using a greater number of occurrence patterns.

[0044] In this case, the auxiliary sample output unit 101B may increase or decrease the occurrence frequency value of 0.3 calculated as above by a fixed value (for example, by increasing or decreasing it by 0.05 each time to calculate values ​​of 0.35 and 0.25), and generate a pseudo occurrence data sequence from that value. Alternatively, as in the above example, the occurrence frequency may be calculated from occurrence data sequences from August and October in the past, and then a pseudo occurrence data sequence may be generated.

[0045] FIG. 4 is a diagram showing an example of a pseudo-generated data string according to this embodiment.

[0046] The pseudo occurrence data string shown in FIG. 4 is obtained by calculating the occurrence frequency for each area, generating pseudo occurrence data, and compiling them into one data string, and has the same occurrence frequency as the example in FIG. 3 described above.

[0047] FIG. 5 is a diagram showing another example of a pseudo-generated data string according to this embodiment.

[0048] The pseudo-occurrence data string shown in Figure 5 was created by calculating the occurrence frequency for each area and then reducing the occurrence frequency of each area by a fixed value.As a result, the total number of occurrences in the 10:00 hour range is lower than in the example of Figure 4.

[0049] As a fourth alternative method, a method for generating pseudo-occurrence data by calculating the probability of occurrence of an injury or illness as a random variable will be described. The probability of occurrence of an injury or illness is an example of an event occurrence probability. Calculating the probability as a random variable means considering the possibility that the probability of occurrence of an injury or illness may take various values, such as a 50% probability of 0.3 injuries, a 10% probability of 0.31 injuries, a 5% probability of 0.32 injuries, and so on, and expressing each possibility as a probability. To calculate the probability as a random variable, it is preferable to assume that the target past occurrence data sequence occurs according to a Poisson distribution and use a Markov Chain Monte Carlo method (MCMC) or the like to calculate the parameters of the Poisson distribution (representing the number of occurrences within a certain period of time) as random variables. Based on the results, the first main sample output unit 101A generates pseudo-occurrence data from the parameter with the highest probability, and the first auxiliary sample output unit 101B generates pseudo-occurrence data from parameters with other probabilities. In reality, the distribution of occurrence probability is continuous, so it is advisable to pick up parameters at regular intervals from it and generate pseudo-occurrence data.

[0050] FIG. 6 is a graph showing an example of the probability of injury or illness occurring calculated by the MCMC according to this embodiment.

[0051] The graph shown in Figure 6 visualizes the results of calculating the Poisson distribution parameters as random variables as described above. The horizontal axis represents the value of the Poisson distribution parameters, and the vertical axis represents the probability. In the example shown in Figure 6, four graphs are drawn, each corresponding to an MCMC chain, representing the calculations performed using the four chains. The four graphs nearly overlap, indicating that the MCMC calculations have nearly converged. In this case, the first main sample output unit 101A generates pseudo-generated data to follow a Poisson distribution with a parameter of 0.6, which has the highest probability. Meanwhile, the first auxiliary sample output unit 101B generates generated data from parameters around 0.6, such as 0.2, 0.4, 0.8, and 1.0.

[0052] When first main sample output unit 101A and first auxiliary sample output unit 101B each generate a pseudo-occurrence data sequence, the number of days for first auxiliary sample output unit 101B may be intentionally reduced. However, this does not affect the length of the data sequence. The length of the data sequence increases as the occurrence probability parameter increases. When the Poisson distribution parameters are calculated as random variables using the MCMC method described above, the number of days for data generation may be determined according to the magnitude of the probability. In the example of Figure 6, the number of samples is reduced in the order of parameters: 0.6, 0.8, 0.4, 1.0, and 0.2. The subsequent first placement evaluation unit 103 generally performs an evaluation that places weight on the data sequence output by first main sample output unit 101A. However, if the number of days for the data sequence output by first auxiliary sample output unit 101B is intentionally reduced compared to first main sample output unit 101A, the evaluation can be equivalently performed by using a weighted average based on a unified evaluation standard.

[0053] The generated data strings created by the first main sample output section 101A and the first auxiliary sample output section 101B are output to the first arrangement evaluation section 103, respectively.

[0054] On the other hand, the placement plan creating unit 102 creates a plurality of placement plans for placing ambulances at fire stations based on, for example, ambulance data 141 shown in FIG. 7 and fire station data 142 shown in FIG.

[0055] Fig. 7 is a diagram showing an example of ambulance data 141 according to this embodiment, and Fig. 8 is a diagram showing an example of fire station data 142 according to this embodiment.

[0056] In the example of ambulance data 141 shown in Fig. 7, six ambulances having ambulance IDs (Identification) a to f are registered. In addition, in the example of fire station data 142 shown in Fig. 8, nine fire stations having fire station IDs A to I are registered. As an example of a method for creating an initial placement plan, placement plans may be created randomly as shown in Fig. 9.

[0057] 9 is a diagram showing an example of an ambulance placement plan according to this embodiment. A unique ID (not shown) is assigned to each of the multiple placement plans created by the placement plan creating unit 102.

[0058] The placement plan created by the placement plan creation unit 102 is output to the first placement evaluation unit 103. There are various possible methods for creating the second and subsequent placement plans. The simplest method is to randomly create placement plans for the second and subsequent placement plans as well. However, in this case, if the number of combinations is enormous, it is thought that it will take a long time for the first placement evaluation unit 103 to identify a placement plan that is evaluated as effective. Therefore, it is possible to use various heuristics (also called discovery methods). One such method is to use a genetic algorithm.

[0059] An example of a method using a genetic algorithm will be described below. Placement plans are randomly created until the first placement evaluation unit 103 identifies a placement plan that is evaluated as effective. If the placement plan is evaluated as effective, the next placement plan is created by randomly changing part of the placement plan based on the identified placement plan, or by combining multiple placement plans that have been evaluated as effective. Combining multiple placement plans means, for example, extracting and combining the placement of ambulances a to c from one placement plan and the placement of ambulances d to f from another placement plan. It has been empirically found that this method allows a solution close to the optimal solution to be obtained in a relatively short time.

[0060] Next, the first placement evaluation unit 103 evaluates the placement plan acquired from the placement plan creation unit 102 using the occurrence data strings acquired from each of the first main sample output unit 101A and the first auxiliary sample output unit 101B. As an example of the evaluation method, the average distance traveled by the nearest available ambulance to the scene of an injured person is calculated, and the calculated average traveled distance is compared with the average traveled distance in the case of the original ambulance placement shown in Fig. 10, for example.

[0061] FIG. 10 is a diagram showing an example of the original arrangement of ambulances according to this embodiment.

[0062] Once an ambulance has been dispatched, it will be unable to respond to subsequent dispatch requests for a certain period of time. This certain period of time can be determined, for example, by calculating the average time it takes to complete transporting an injured person and using that value. For example, if the average time it takes to complete transporting an injured person is 50 minutes, then it will be unable to respond to subsequent dispatch requests for 50 minutes. As an example of how to calculate the distance to the scene of an injured person, the simplest method would be the straight-line distance, or, if road network data is available, the shortest distance on the road network.

[0063] Furthermore, if the first main sample output unit 101A outputs, for example, 100 days' worth of data, the first allocation evaluation unit 103 evaluates all 100 days' worth of data. The same applies to the data output from the first auxiliary sample output unit 101B.

[0064] Furthermore, regarding the initial value of the ambulance status, it is possible that some ambulances may already be dispatched. For this reason, it is desirable to perform evaluations from various initial states for each day's worth of samples, for example. The initial state could be, for example, that ambulance A is currently dispatched and will return in 30 minutes, or that ambulance A and ambulance B are currently dispatched, with ambulance A available in 30 minutes and ambulance B available in 40 minutes.

[0065] If the placement plan satisfies a predetermined evaluation criterion as a result of the above evaluation, the placement plan is deemed valid and is stored in the storage 14 as a valid placement plan 144 .

[0066] In this case, since the generated data sequence acquired from first main sample output unit 101A and the generated data sequence acquired from first auxiliary sample output unit 101B basically have different importance, they may be evaluated using different evaluation criteria. For example, the generated data sequence acquired from first main sample output unit 101A is considered to satisfy the evaluation criteria if the average distance to arrive at the site (hereinafter referred to as the "site arrival distance") is reduced by an average of 100 meters or more compared to the original reference layout. On the other hand, the generated data sequence acquired from first auxiliary sample output unit 101B is considered to satisfy the evaluation criteria if the average site arrival distance is reduced by an average of 50 meters or more.

[0067] Fig. 11 is a diagram showing an example of an effective placement plan 144 that satisfies the evaluation criteria according to this embodiment, and Fig. 12 is a diagram showing an example of a placement plan that does not satisfy the evaluation criteria according to this embodiment.

[0068] In the effective placement plan 144 shown in FIG. 11, a primary evaluation and an auxiliary evaluation are associated with a placement ID indicating the placement plan. The primary evaluation represents the distance difference when the generated data sequence acquired from the first primary sample output unit 101A is applied to the placement plan, and the auxiliary evaluation represents the distance difference when the generated data sequence acquired from the first auxiliary sample output unit 101B is applied to the placement plan. As described above, the distance difference here represents the difference between the average on-site arrival distance when the generated data sequence is applied to the placement plan and the average on-site arrival distance when the generated data sequence is applied to the original placement. Note that, as described above, the evaluation criteria differ between the primary evaluation and the auxiliary evaluation (for example, the average reduced distance difference is 100 m or more in the primary evaluation, and the average reduced distance difference is 50 m or more in the auxiliary evaluation). In other words, the effective placement plan 144 shown in FIG. 11 is a placement plan in which the primary evaluation and the auxiliary evaluation each satisfy their respective evaluation criteria.

[0069] On the other hand, the placement plan shown in Figure 12 is a placement plan that did not satisfy the evaluation criteria. For example, the placement plan with placement ID D1F2ED3A does not satisfy the evaluation criteria in either the primary or secondary evaluation. Also, the placement plan with placement ID 3A721C59 satisfies the evaluation criteria in the primary evaluation but not in the secondary evaluation. Also, the placement plan with placement ID C12FA275 is the opposite, that is, it does not satisfy the evaluation criteria in the primary evaluation but satisfies the evaluation criteria in the secondary evaluation.

[0070] As mentioned above, if the number of days of the generated data sequence output by the first auxiliary sample output unit 101B is intentionally reduced compared to that of the first main sample output unit 101A, evaluation may be performed using a weighted average based on a unified evaluation standard.

[0071] Furthermore, the first placement evaluation unit 103 may output the evaluation result of a certain placement plan to the placement plan creation unit 102 so that the evaluation result is reflected in the creation of the next placement plan.

[0072] By repeating the above-described Phase 1 processing for over 10 hours on a general-purpose PC or the like, it is possible to empirically find tens to hundreds of effective layout patterns.

[0073] Next, with reference to FIG. 13, the operation of the layout searching device 10 according to the first embodiment will be described.

[0074] 13 is a flowchart showing an example of the processing flow of Phase 1 by the arrangement search program according to the embodiment 1. The processing of Phase 1 by the arrangement search program is realized by the CPU 11 of the arrangement search device 10 writing the arrangement search program stored in the ROM 12 or the storage 14 into the RAM 13 and executing it.

[0075] 13, the CPU 11 receives input of past event occurrence data 143 representing, for example, event occurrence data for the past several years. In this example, the event occurrence data represents, for example, injury occurrence data, as described above.

[0076] In step S102, the CPU 11 outputs, as a first main sample, event occurrence data that may occur under predetermined conditions, based on the past event occurrence data 143 received in step S101. In this example, the predetermined conditions may be, for example, between 10:00 and 10:00 on a weekday in September, as described above.

[0077] In step S103, CPU 11 outputs, as a first auxiliary sample, event occurrence data that may occur under conditions similar to the above conditions, based on past event occurrence data 143 received as input in step S101. In this example, the similar conditions refer to conditions such as, for example, around 10:00 on weekdays in August and October before and after September, as described above. Furthermore, since the first auxiliary sample is not essential, step S103 may be skipped if the first auxiliary sample is not used.

[0078] In step S104, the CPU 11 creates multiple ambulance deployment plans for responding to an incident in which an injured person occurs, based on, for example, the ambulance data 141 shown in FIG. 7 described above and the fire station data 142 shown in FIG. 8 described above.

[0079] In step S105, the CPU 11 applies the first main sample output in step S102 and the first auxiliary sample output in step S103 to each of the multiple placement plans created in step S104, and evaluates whether each of the multiple placement plans satisfies the evaluation criteria. Then, the CPU 11 stores an effective placement plan that satisfies the evaluation criteria from among the multiple placement plans in the storage 14 as, for example, the effective placement plan 144 shown in Fig. 11 above, and ends the processing of Phase 1 by this placement search program.

[0080] 14 is a flowchart showing an example of the flow of the evaluation process according to the first embodiment. The flow in Fig. 14 specifically shows the evaluation process in step S105 in Fig. 13.

[0081] 14, the CPU 11 determines whether or not event occurrence data representing the first main sample or the first auxiliary sample exists. If it is determined that the event occurrence data exists (positive determination), the process proceeds to step S112, and if it is determined that the event occurrence data does not exist (negative determination), the process proceeds to step S115.

[0082] In step S112, the CPU 11 extracts one piece of event occurrence data.

[0083] In step S113, the CPU 11 dispatches the nearest ambulance for the event occurrence data extracted in step S112, and assigns a dispatch disable flag to the dispatched ambulance indicating that the ambulance cannot be dispatched again for a certain period of time.

[0084] In step S114, the CPU 11 calculates the distance from the fire station where the ambulance that was flagged as unavailable for dispatch in step S113 is located to its arrival at the scene, stores the calculated distance in the storage 14, returns to step S111, and repeats the process for all event occurrence data.

[0085] Meanwhile, in step S115, the CPU 11 calculates the average distance to arrival at the scene, and returns to step S105 in Fig. 13. As described above, the evaluation method is to calculate the average distance traveled by the ambulance that is closest to the scene of an injured person and is available to be dispatched until it arrives at the scene, and compare the calculated average traveled distance with, for example, the average traveled distance in the case of the original ambulance placement shown in Fig. 10 above.

[0086] Next, the processing of Phase 2 will be described. The processing of Phase 2 is executed, for example, in September, at a fixed time (e.g., 9:00) before 10:00 every weekday. The second sample output unit 104 calculates the frequency of injuries and illnesses for each area between 10:00 and 10:00 on weekdays for the past month from the latest event occurrence data 145, and samples future pseudo-occurrence data from the results. Note that the latest event occurrence data 145 represents the most recent past injury and illness occurrence data accumulated up to the immediately preceding time. The number of days for sampling is set to, for example, 100 days. Alternatively, similar to the processing of Phase 1 described above, the second main sample output unit 104A and the second auxiliary sample output unit 104B may share roles and output samples by increasing or decreasing the occurrence frequency by a fixed value, or samples may be generated based on random variables using the MCMC algorithm shown in FIG. 6 described above.

[0087] The second placement evaluation unit 105 evaluates all of the effective placement plans 144 stored in the storage 14. The evaluation method here is the same as the evaluation method in the processing of phase 1 described above, except that the placement plans to be acquired are not the placement plans created by the placement plan creation unit 102, but the effective placement plans 144 evaluated by the first placement evaluation unit 103.

[0088] 15 is a flowchart showing an example of the processing flow of Phase 2 by the arrangement search program according to the embodiment 1. The processing of Phase 2 by the arrangement search program is realized by the CPU 11 of the arrangement search device 10 writing the arrangement search program stored in the ROM 12 or the storage 14 into the RAM 13 and executing it.

[0089] 15, the CPU 11 receives input of latest event occurrence data 145 representing, for example, event occurrence data for the most recent month. In this example, the event occurrence data represents, for example, injury occurrence data, as in the above-described Phase 1.

[0090] In step S122, the CPU 11 outputs future event occurrence data that may occur under predetermined conditions as a second main sample, based on the latest event occurrence data 145 received in step S121. Note that in this example, the predetermined conditions are assumed to be, for example, between 10:00 and 10:00 on a weekday in September, as in the above-described Phase 1.

[0091] In step S123, the CPU 11 outputs future event occurrence data that may occur under conditions similar to the above conditions as a second auxiliary sample, based on the latest event occurrence data 145 received as input in step S121. In this example, the similar conditions refer to conditions such as 10:00 a.m. on weekdays in August and October before and after September, as in the above-mentioned Phase 1. In addition, since the second auxiliary sample is not essential, step S123 may be skipped if the second auxiliary sample is not used.

[0092] In step S124, the CPU 11 applies the second main sample output in step S122 and the second auxiliary sample output in step S123 to each of the effective placement plans 144 (see FIG. 11) stored in the storage 14 in the above-described phase 1, and re-evaluates whether each of the effective placement plans 144 satisfies the evaluation criteria. The re-evaluation method is the same as the evaluation method in the above-described phase 1.

[0093] In step S125, the CPU 11 outputs the final evaluation result obtained by the re-evaluation in step S124, and ends the processing of phase 2 by this placement search program.

[0094] In this way, the effective allocation plan is evaluated again based on the latest generated data sequence. As a result, the evaluation result may differ from the evaluation result shown in Figure 11. The user can look at this evaluation result and make a final decision on which resource allocation to go with.

[0095] Phase 2 processing can be performed on a general-purpose PC in a matter of tens of seconds to a few minutes. Therefore, for example, on the day when you want to change the allocation of resources, you can quickly find and adopt an appropriate allocation of resources based on the latest event occurrence data.

[0096] The above method can also be applied to other situations. For example, if the number of emergency teams is reduced for some reason, it can be used to determine the effective deployment of fewer emergency teams in advance.

[0097] Thus, according to this embodiment, when an effective allocation of resources must be determined within a relatively short period of time, it is possible to obtain a robust resource allocation that is likely to be effective even if the predicted results of the event occurrence are slightly off.

[0098] [Second embodiment] In the second embodiment, a form will be described in which the destination of a resource (for example, an ambulance) is calculated in real time using the latest data.

[0099] The functional configuration of a placement search device 10A according to the second embodiment will be described with reference to Fig. 16. Below, the above-mentioned (1) dynamic placement outside a fire station, that is, a form in which a placement plan is created using a main sample, will be described.

[0100] FIG. 16 is a block diagram showing an example of the functional configuration of a layout searching device 10A according to the second embodiment.

[0101] 16, the layout search device 10A has, as its functional configuration, a resource data acquisition unit 111, a sample output unit 112, a various data acquisition unit 113, a layout plan creation unit 114, a layout evaluation unit 115, and a layout plan selection unit 116. Each functional configuration is realized by the CPU 11 reading out a layout search program stored in the ROM 12 or the storage 14, expanding it in the RAM 13, and executing it.

[0102] The resource data acquisition unit 111 acquires resource data including the latest resource locations and availability of dispatch. The resource data is, for example, data relating to the latest ambulances (vehicles), and is acquired from the ambulance data 141.

[0103] FIG. 17 is a diagram showing an example of resource data according to this embodiment.

[0104] 17 includes the ambulance's vehicle name, latitude and longitude indicating its current location, dispatch availability indicating whether it is available for dispatch, and the number of trips indicating the number of trips it has made. This resource data is updated at regular intervals or whenever there is a change in the data.

[0105] The sample output unit 112 outputs future event occurrence data that may occur under predetermined conditions as a main sample based on the latest event occurrence data. The latest event occurrence data is obtained from latest event occurrence data 145. As described above, the main sample is expressed as a pseudo-occurrence data sequence that is generated in a pseudo-manner according to an event occurrence frequency calculated for each region from a data sequence that actually occurred under predetermined conditions. Alternatively, the main sample may be expressed as a pseudo-occurrence data sequence that is generated in a pseudo-manner according to an event occurrence probability calculated as a random variable from a data sequence that actually occurred under predetermined conditions.

[0106] FIG. 18 is a diagram showing an example of a main sample according to this embodiment.

[0107] The main sample shown in Figure 18 is data predicting possible events, and includes the date and time of the event, the latitude and longitude of the location of the event, and data used to calculate the evaluation value. Note that the "data used to calculate the evaluation value" indicates, for example, the time it takes from dispatch to return to the station.

[0108] The various data acquisition unit 113 acquires fire station data related to fire stations and correspondence data between ambulances and fire stations. The fire station data is acquired from the fire station data 142, and the correspondence data between ambulances and fire stations is acquired from the ambulance data 141 or the fire station data 142.

[0109] FIG. 19 is a diagram showing an example of fire station data according to this embodiment.

[0110] The fire station data shown in FIG. 19 includes the signature, latitude and longitude of the location of the fire station.

[0111] FIG. 20 is a diagram showing an example of correspondence data between ambulances and fire stations according to this embodiment.

[0112] In the correspondence data between ambulances and fire stations shown in FIG. 20, the vehicle name of the ambulance is associated with the signature name of the fire station.

[0113] The placement plan creating unit 114 creates a plurality of placement plans for resources corresponding to events that occur in real time, using the various data shown in the above-mentioned Fig. 17 to Fig. 20. An example of a procedure for creating placement plans will be described with reference to Fig. 21 and Fig. 22.

[0114] FIG. 21 is a diagram showing an example of a moving vehicle candidate list according to this embodiment.

[0115] The moving vehicle candidate list shown in Fig. 21 may specify one vehicle or multiple vehicles as the moving target vehicle. The list may also reflect resource data obtained in real time, for example, by limiting the list to vehicles that are available for dispatch and on standby at a fire station, or to vehicles whose number of movements is below a threshold.

[0116] Next, the destination of each candidate moving vehicle registered in the candidate moving vehicle list shown in Fig. 21 is calculated using an arbitrary calculation method, and an arrangement plan list shown in Fig. 22 is created as an example. Note that the destination here is not limited to a fire station, but also includes any point other than a fire station.

[0117] FIG. 22 is a diagram showing an example of a placement plan list according to this embodiment.

[0118] The placement plan list shown in FIG. 22 is expressed as a list in which the latitude and longitude of the destination are assigned to each moving vehicle candidate in the moving vehicle candidate list.

[0119] When the main sample is applied to each of the plurality of placement plans, the placement evaluation unit 115 assigns an evaluation value to each of the plurality of placement plans using the resource data. Specifically, an evaluation value is assigned to each placement plan in the placement plan list shown in Fig. 22 above, and an evaluation result shown in Fig. 23 is obtained as an example.

[0120] FIG. 23 is a diagram showing an example of an evaluation result according to this embodiment.

[0121] Here, an example of a method for calculating the evaluation value is to perform a simulation of each candidate moving vehicle by moving the target vehicle to the destination latitude and longitude, reflecting the current location information and dispatch availability information of each vehicle obtained from the resource data shown in FIG. 17 above. If the average arrival distance to the site is calculated by this simulation, it can be used as the evaluation value. Furthermore, in the simulation, the "data used to calculate the evaluation value" of the main sample shown in FIG. 18 above can be used. As the "data used to calculate the evaluation value," data appropriate for the content of the simulation to be performed can be obtained.

[0122] The above simulation will now be described in detail. The deployment evaluation unit 115 determines the initial state of each resource using the current resource location information and dispatch readiness information included in the resource data, executes a simulation, and assigns an evaluation value to each of the multiple deployment plans based on the results of the simulation. For example, for each vehicle, an initial state reflecting the current location and current dispatch readiness is set, and the simulation is started. This makes it possible to perform a simulation that reflects the current situation more than if the initial state were determined randomly, and is expected to enable the calculation of a more appropriate evaluation value.

[0123] The deployment evaluation unit 115 may also use the current resource location information and deployment readiness information included in the resource data to perform a simulation of moving each resource to the latitude and longitude of the deployment destination, and assign an evaluation value to each of the multiple deployment plans based on the results of the simulation. In this case, the adequacy of the move can be evaluated taking into account the change in coverage area due to the deployment of each unit.

[0124] In the above simulation, by considering the near future, for example, one hour ahead, it is possible to focus on the time periods when vehicle movement has the greatest impact for evaluation, and it is expected that the calculation time will be shorter than running a simulation for a day, for example. Furthermore, if the initial state of the vehicle is determined using the latest information, it is expected that a reasonable evaluation value will be obtained by simulating the near future alone.

[0125] As mentioned above, an example of "data used to calculate the evaluation value" shown in Figure 18 is the time it takes for a vehicle to return to the station after being dispatched. In addition, the evaluation value to be assigned does not have to be one; for example, the number of cases in which the arrival distance to the scene was equal to or greater than a threshold value can also be assigned as an evaluation value. In order to obtain a more accurate evaluation value, the simulation can be performed multiple times, and an evaluation value calculated from the results of the multiple simulations can be assigned for each evaluation item. When performing the simulation multiple times, it is desirable to obtain key samples such as those shown in Figure 18 above for the number of simulations. Note that the calculation time can be adjusted by adjusting the number of simulations.

[0126] The simulation can be used not only to assign an evaluation value in the placement evaluation unit 115 but also to determine whether or not to perform calculations to calculate a placement plan by calculating an evaluation value by performing a simulation on the current situation before creating a placement plan. For example, the number of cases where the on-site arrival distance was equal to or greater than a threshold may be assigned as an evaluation value, and a placement plan may be calculated only if the number of cases exceeds a certain constant.

[0127] As a result of the above, in actual operation, the number of operations required to obtain placement plans can be reduced, which is expected to reduce the burden on operators, etc. On the other hand, from the analyst's perspective, the number of times placement plans need to be evaluated can be reduced, which is expected to reduce the calculation time for the entire simulation over a period of time, etc.

[0128] The placement plan selection unit 116 selects an effective placement plan from among the multiple placement plans based on the evaluation value assigned to each of the multiple placement plans by the placement evaluation unit 115. Specifically, for example, by selecting the one with the highest evaluation value from the evaluation results shown in FIG. 23, it is possible to determine the vehicle to be moved and its destination. When there are multiple evaluation values, for example, if evaluation value 1 is the same, it is possible to select the one with the highest evaluation value 2, or to select the one with the highest evaluation value 1 among those with evaluation value 2 equal to or greater than a threshold. Furthermore, by calculating the evaluation values ​​of each evaluation item for the situation before moving the vehicle (the current situation), it is possible to calculate how these will change when the vehicle is moved, and if the evaluation value will decrease from the current situation, it is possible to perform processing such as not adopting the placement plan with the highest evaluation value in the list of placement plans.

[0129] Here, the sample output unit 112 may further output, as an auxiliary sample, event occurrence data that may occur in the future under conditions similar to the predetermined conditions, based on the new event occurrence data. As described above, the auxiliary sample is expressed as a pseudo-occurrence data sequence that is generated in a pseudo-manner according to the event occurrence frequency calculated for each certain region from a data sequence that actually occurred under similar conditions. Alternatively, the auxiliary sample may be expressed as a pseudo-occurrence data sequence that is generated in a pseudo-manner according to the event occurrence frequency calculated as a random variable from a data sequence that actually occurred under similar conditions.

[0130] In this case, the placement plan creation unit 114 creates multiple placement plans in real time using resource data, main samples, and auxiliary samples. Then, the placement evaluation unit 115 assigns an evaluation value to each of the multiple placement plans using resource data when applying the main sample and auxiliary sample to each of the multiple placement plans created by the placement plan creation unit 114. However, because auxiliary samples are not essential, whether or not to use auxiliary samples is optional.

[0131] 24 is a flowchart showing an example of the flow of processing by the arrangement search program according to the embodiment 2. The processing by the arrangement search program is realized by the CPU 11 of the arrangement search device 10A writing the arrangement search program stored in the ROM 12 or the storage 14 into the RAM 13 and executing it.

[0132] In step S131 of FIG. 24, the CPU 11 acquires, as an example, the resource data shown in FIG. 17, the main sample shown in FIG. 18, the fire station data shown in FIG. 19, and the correspondence data between ambulances and fire stations shown in FIG. 20.

[0133] In step S132, the CPU 11 uses the various data acquired in step S131 to create a candidate moving vehicle list, as shown in Fig. 21 above, according to the real-time situation. As described above, the candidate moving vehicle list may specify one vehicle or multiple vehicles as the target moving vehicle. The candidate moving vehicle list may also reflect resource data obtained in real time, such as limiting the list to vehicles that are available for deployment and on standby at a fire station, or limiting the list to vehicles whose number of movements is below a threshold.

[0134] In step S133, the CPU 11 calculates the destination of each moving vehicle candidate registered in the moving vehicle candidate list created in step S132 using an arbitrary calculation method, and creates, as an example, the placement plan list shown in the above-mentioned Fig. 22. Note that the destination here is not limited to a fire station, but also includes any point other than a fire station.

[0135] In step S134, when the CPU 11 applies the main sample or the main sample and the auxiliary sample to each placement plan registered in the placement plan list created in step S133, it assigns an evaluation value, for example, as shown in Fig. 23 above, using resource data. A specific example of a method for calculating the evaluation value is a method of reflecting the current position information and dispatch readiness information of each vehicle obtained from the resource data shown in Fig. 17 above, and performing a simulation for each candidate moving vehicle in case the target vehicle to be moved is moved to the latitude and longitude of the destination.

[0136] In step S135, the CPU 11 selects an effective placement plan from among the multiple placement plans based on the evaluation value assigned to each of the multiple placement plans in step S134, and ends the series of processes performed by this placement search program. Specifically, as an example, the vehicle to be moved and its destination can be determined by selecting the vehicle with the highest evaluation value from the evaluation results shown in Figure 23 above.

[0137] As described above, the arrangement searching device 10A according to this embodiment includes one sample output unit 112 and one arrangement evaluation unit 115. The sample output unit 112 can have one of two configurations: a configuration that outputs only the main sample, and a configuration that outputs the main sample and the auxiliary sample. Note that the evaluation criteria used by the arrangement evaluation unit 115 for the main sample and the auxiliary sample may be the same or different.

[0138] [Modification of the second embodiment] In the above embodiment, we have described a configuration in which the destination can be any point other than a fire station, but in the following modified example, we will describe a configuration in which the destination is limited to a fire station. That is, we will describe the above-mentioned (2) dynamic fire station placement, that is, a configuration in which a placement plan is created without using a primary sample.

[0139] In this case, the placement plan creation unit 114 uses the resource data to create multiple placement plans of resources corresponding to the event that will occur. Then, the placement evaluation unit 115 uses the resource data to assign an evaluation value to each of the multiple placement plans. Note that the placement evaluation unit 115 may assign an evaluation value by further using the main sample output from the sample output unit 112 in addition to the resource data. An example of a method for assigning such an evaluation value is a method using simulation.

[0140] In this modification, different processing will be described with reference to the flowchart shown in FIG.

[0141] As the fire station data acquired in step S131, it is desirable to use, for example, the fire station data shown in FIG.

[0142] FIG. 25 is a diagram showing another example of fire station data according to this embodiment.

[0143] The fire station data shown in Figure 25 includes the name of the fire station, the latitude and longitude of the location, and the maximum number of parking spaces. However, if the maximum number of parking spaces is not a consideration, the fire station data shown in Figure 19 above may be used.

[0144] In step S133, the moving vehicle candidate list shown in FIG. 26 is created from the moving vehicle candidate list shown in FIG. 21 described above.

[0145] FIG. 26 is a diagram showing another example of the moving vehicle candidate list according to this embodiment.

[0146] In the moving vehicle candidate list shown in Figure 26, for example, basically, if one vehicle is moved to any fire station, it is considered one candidate, and by finding all of these patterns, a list with the destination fire station can be obtained. In this case, the number of lines in the list with the destination fire station will be greater than the number of lines in the original list shown in Figure 21. In addition, the candidates can be narrowed down by the following process.

[0147] (a) Excluding the fire station to which the vehicle belongs. (b) Consider the parking space at each fire station. Extract fire stations that can be moved to (i.e., fire stations where parking is available) based on the current position of each vehicle, dispatch availability information, and the maximum parking capacity of each fire station. In this case, you can consider that if the vehicle is actually currently at a fire station, it is using a parking space, or if the vehicle is heading towards a fire station, it is using a parking space at the fire station it is heading towards. However, if parking space is not taken into consideration, this process does not need to be performed. (c) Set a distance restriction. For example, one possible method is to extract fire stations whose distance from the vehicle's own fire station is less than a threshold, or to extract fire stations so that the sum of the distance from the vehicle's current location to the destination fire station and the distance from the destination fire station to the vehicle's own fire station is less than a threshold. This process can exclude fire stations that are too far away. However, if no distance restriction is set, this process does not need to be performed.

[0148] In step S133, the latitude and longitude of each fire station is assigned to each moving vehicle candidate, and a list of placement plans such as that shown in FIG. 22 is obtained, for example.

[0149] After obtaining the placement plan list shown in Fig. 22, the process is the same as the above-mentioned form including any points other than fire stations. Note that it is considered that discrete standby points other than fire stations can also be handled in the same way if data related to the discrete standby points is used instead of data related to fire stations in step S133.

[0150] As described above, according to this embodiment, the destination of a resource can be calculated in real time using the latest data.

[0151] In the above embodiment, the placement search process executed by the CPU after reading the software (program) may be executed by various processors other than the CPU. Examples of processors in this case include dedicated electrical circuits, such as programmable logic devices (PLDs) (such as field-programmable gate arrays (FPGAs)) whose circuit configuration can be changed after fabrication, and application-specific integrated circuits (ASICs) that are processors with circuit configurations specifically designed to execute specific processes. The placement search process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, or a combination of a CPU and an FPGA). The hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor devices.

[0152] In the above embodiment, the configuration in which the location search program is pre-stored (installed) in storage 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. The program may also be downloaded from an external device via a network.

[0153] The following additional notes are provided regarding the above-described embodiments.

[0154] (Additional note 1) Memory and at least one processor coupled to said memory; Including, The processor: Obtaining resource data including the latest resource locations and availability; Using the resource data, create multiple resource allocation plans corresponding to the events that occur; assigning an evaluation value to each of the plurality of placement plans using the resource data; selecting an effective placement plan from among the plurality of placement plans based on the evaluation value assigned to each of the plurality of placement plans; The arrangement search device is configured as follows.

[0155] (Additional note 2) A non-transitory storage medium storing a program executable by a computer to perform a placement search process, The placement search process includes: Obtaining resource data including the latest resource locations and availability; Using the resource data, create multiple resource allocation plans corresponding to the events that occur; assigning an evaluation value to each of the plurality of placement plans using the resource data; selecting an effective placement plan from among the plurality of placement plans based on the evaluation value assigned to each of the plurality of placement plans; Non-transitory storage medium. [Explanation of symbols]

[0156] 10, 10A placement search device 11 CPU 12 ROM 13 RAM 14. Storage 15 Input section 16 Display section 17 Communication I / F 18 Bus 101 First sample output unit 101A First main sample output section 101B First auxiliary sample output unit 102 Layout Planning Department 103 First Placement Evaluation Department 104 Second sample output unit 104A Second main sample output section 104B Second auxiliary sample output unit 105 Second Placement Evaluation Department 106 Result output section 111 Resource Data Acquisition Unit 112 Sample Output Section 113 Various Data Acquisition Section 114 Layout Planning Department 115 Placement Evaluation Department 116 Placement Plan Selection Section 141 Ambulance Data 142 Fire Station Data 143 Past Event Occurrence Data 144 Effective layout plan 145 Latest Event Occurrence Data

Claims

1. A sample output unit that outputs a main sample, which is a sample of event occurrence data that may occur under predetermined conditions; a placement plan creating unit that creates a plurality of placement plans, which are plans for placing each resource candidate that is available for deployment and on standby and that corresponds to an event that occurs in real time, at a destination, for each resource candidate that is registered in advance in a resource candidate list; a placement evaluation unit that executes a simulation of what would happen if the main sample were applied to each of the plurality of placement plans, and that would move each resource candidate to a destination latitude and longitude while reflecting the current location and dispatch availability of each resource candidate, and assigns an evaluation value to each of the plurality of placement plans based on the result of the simulation; a placement plan selection unit that selects an effective placement plan from among the plurality of placement plans based on an evaluation value assigned to each of the plurality of placement plans; A placement search device comprising:

2. A sample output unit that outputs a main sample, which is a sample of event occurrence data that may occur under predetermined conditions; a placement plan creating unit that creates a plurality of placement plans, which are plans for placing each resource candidate that is available for deployment and on standby and that corresponds to an event that occurs in real time, at a destination, for each resource candidate that is registered in advance in a resource candidate list; a placement evaluation unit that, when the main sample is applied to each of the plurality of placement plans, determines an initial state of each resource candidate, reflects the current location and dispatch readiness of each resource candidate, executes a simulation, and assigns an evaluation value to each of the plurality of placement plans based on the result of the simulation; a placement plan selection unit that selects an effective placement plan from among the plurality of placement plans based on an evaluation value assigned to each of the plurality of placement plans; A placement search device comprising:

3. The predetermined conditions include a month, a weekday or a holiday, and a time period related to the occurrence of the event, The sample output unit determines an event occurrence frequency for each certain region from a data sequence indicating that an event actually occurred under the predetermined conditions, and outputs a pseudo-occurrence data sequence that is pseudo-generated in accordance with the event occurrence frequency, or determines an event occurrence probability as a random variable from a data sequence indicating that an event actually occurred under the predetermined conditions, and outputs a pseudo-occurrence data sequence that is pseudo-generated in accordance with the random variable as the main sample.

3. The arrangement searching device according to claim 1 or 2.

4. Outputting a main sample, which is a sample of event occurrence data that may occur under predetermined conditions; For each resource candidate that is available for deployment and on standby and registered in advance in the resource candidate list, a plurality of placement plans are created, which are plans for placing each resource candidate at a destination corresponding to an event that occurs in real time; a simulation is performed of a case where the main sample is applied to each of the plurality of placement plans, and each resource candidate is moved to a destination latitude and longitude while reflecting the current location and dispatch availability of each resource candidate; and an evaluation value is assigned to each of the plurality of placement plans based on the results of the simulation; selecting an effective placement plan from among the plurality of placement plans based on the evaluation value assigned to each of the plurality of placement plans; A computer-implemented placement search method.

5. Outputting a main sample, which is a sample of event occurrence data that may occur under predetermined conditions; For each resource candidate that is available for deployment and on standby and registered in advance in the resource candidate list, a plurality of placement plans are created, which are plans for placing each resource candidate at a destination corresponding to an event that occurs in real time; When the main sample is applied to each of the plurality of placement plans, a simulation is performed by determining an initial state of each resource candidate after reflecting the current location and dispatch availability of each resource candidate, and assigning an evaluation value to each of the plurality of placement plans based on the result of the simulation; selecting an effective placement plan from among the plurality of placement plans based on the evaluation value assigned to each of the plurality of placement plans; A computer-implemented placement search method.

6. Outputting a main sample, which is a sample of event occurrence data that may occur under predetermined conditions; For each resource candidate that is available for deployment and on standby and registered in advance in the resource candidate list, a plurality of placement plans are created, which are plans for placing each resource candidate at a destination corresponding to an event that occurs in real time; a simulation is performed of a case where the main sample is applied to each of the plurality of placement plans, and each resource candidate is moved to a destination latitude and longitude while reflecting the current location and dispatch availability of each resource candidate; and an evaluation value is assigned to each of the plurality of placement plans based on the results of the simulation; selecting an effective placement plan from among the plurality of placement plans based on the evaluation value assigned to each of the plurality of placement plans; A placement search program to be executed by a computer.

7. Outputting a main sample, which is a sample of event occurrence data that may occur under predetermined conditions; For each resource candidate that is available for deployment and on standby and registered in advance in the resource candidate list, a plurality of placement plans are created, which are plans for placing each resource candidate at a destination corresponding to an event that occurs in real time; When the main sample is applied to each of the plurality of placement plans, a simulation is performed by determining an initial state of each resource candidate after reflecting the current location and dispatch availability of each resource candidate, and assigning an evaluation value to each of the plurality of placement plans based on the result of the simulation; selecting an effective placement plan from among the plurality of placement plans based on the evaluation value assigned to each of the plurality of placement plans; A placement search program to be executed by a computer.

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