JP7317673B2 - LAYOUT SUPPORT DEVICE, LAYOUT SUPPORT METHOD, AND COMPUTER PROGRAM - Google Patents

Description

TECHNICAL FIELD Embodiments of the present invention relate to a layout support apparatus, a layout support method, and a computer program.

Conventionally, there is a technique of determining the layout of fixtures to be installed on a floor according to the attributes of people who use the floor. For example, as one of the conventional techniques, there is a technique that can determine the type, number, arrangement, etc. of fixtures to be installed on a floor according to the number and physiques of the people using the floor. However, in the prior art, the furniture layout is determined with the aim of accommodating the required number of people, mainly focusing on the number and size of the people. In some cases, it was not always the one that realized floor operation.

Japanese Patent No. 6240563

A problem to be solved by the present invention is to provide a layout support device, a layout support method, and a computer program capable of presenting a floor layout more suitable for actual operation conditions.

A layout support device according to an embodiment has an evaluation index calculation unit and an operation condition determination unit. The evaluation index calculation unit calculates in advance an evaluation index value related to the operation of the target floor based on floor layout information indicating the layout of fixtures on the target floor and distribution information indicating changes in the distribution of people on the target floor. It is calculated for each of one or more determined floor layouts. The operation condition determination unit determines in advance the operation of the target floor when the target floor is operated in each floor layout based on the value of the evaluation index calculated for each of the one or more floor layouts. It is determined for each of the one or more floor layouts whether or not the predetermined operational condition is satisfied.

1 is a diagram showing an outline of a layout support device 1 according to a first embodiment; FIG. 2 is a functional block diagram showing a specific example of the functional configuration of the layout support device 1 of the first embodiment; FIG. 4 is a flow chart showing the flow of processing by the layout support device 1 of the first embodiment to identify a passing layout from one or more predetermined floor layouts. The figure which shows an example of the estimation rule in 1st Embodiment. The figure which shows an example of the estimation rule in 1st Embodiment. 4 is a flowchart showing the flow of distribution estimation processing in the first embodiment; The figure which shows the specific example of the stay area set in the object floor in 1st Embodiment. FIG. 4 is a diagram showing a specific example of destinations determined by a destination function in the first embodiment; The figure which shows the output example of grounds information in 1st Embodiment. FIG. 4 is a diagram showing a specific example of a floor layout according to the first embodiment; FIG. FIG. 4 is a diagram showing a specific example of a floor layout according to the first embodiment; FIG. FIG. 4 is a diagram showing a specific example of a floor layout according to the first embodiment; FIG. FIG. 4 is a diagram for explaining a specific example of basis information shown for a floor layout before improvement in the first embodiment; FIG. FIG. 4 is a diagram for explaining a specific example of basis information shown for a floor layout before improvement in the first embodiment; FIG. FIG. 4 is a diagram for explaining a specific example of basis information shown for a floor layout before improvement in the first embodiment; FIG. FIG. 4 is a diagram for explaining a specific example of basis information shown for a floor layout before improvement in the first embodiment; FIG. FIG. 5 is a diagram for explaining a specific example of basis information shown for an improved floor layout in the first embodiment; FIG. FIG. 5 is a diagram for explaining a specific example of basis information shown for an improved floor layout in the first embodiment; FIG. FIG. 5 is a diagram for explaining a specific example of basis information shown for an improved floor layout in the first embodiment; FIG. FIG. 5 is a diagram for explaining a specific example of basis information shown for an improved floor layout in the first embodiment; FIG. FIG. 3 is a functional block diagram showing a specific example of the functional configuration of a layout support device 1a according to the second embodiment; 10 is a flow chart showing the flow of processing by the layout support apparatus 1a according to the second embodiment to identify acceptable layouts from one or more predetermined floor layouts. 10 is a flow chart showing the flow of calibration processing performed by the layout support device 1a of the second embodiment;

A layout support apparatus, a layout support method, and a computer program according to embodiments will be described below with reference to the drawings.

(First embodiment)
FIG. 1 is a diagram showing an outline of a layout support device 1 according to the first embodiment. The layout support device 1 is a device having a function of supporting the layout of fixtures on a target floor. Specifically, the layout support device 1 has a function of selecting, from among one or more floor layouts predetermined for a target floor, one that satisfies a predetermined operation condition determined for the target floor. Here, the floor layout is information indicating the arrangement of fixtures on the target floor. For example, the floor layout indicates the arrangement of tables, chairs, partitions, etc. on the target floor.

On the other hand, the operation condition is a condition that should be satisfied in the operation of the target floor, and expresses a condition regarding the distribution of people that can vary depending on the floor layout. For example, conditions related to the distribution of people include conditions related to density of people (hereinafter referred to as "congestion level"), residence time, flow of people, presence or absence of areas with low usage rates, and the like. In general, the less crowded the floor, the more comfortable it is for users of the floor. Therefore, for floors where user comfort is important, it is conceivable to set operating conditions such that the degree of congestion is set to a predetermined threshold value or less, for example.

In general, for the floor operator, it is not preferable in terms of floor operation efficiency to have a place with an excessively low degree of congestion and a place with an excessively high degree of congestion coexist. Therefore, in such a case, it is conceivable to set an operational condition such that the degree of congestion in the target floor is equal to or less than a predetermined upper limit and equal to or more than a predetermined lower limit.

Also, in general, the characteristics of the distribution of people on a floor often differ depending on the use and purpose of the floor. For example, if the target floor is a food court, it is important to ensure that the movement of people is not hindered as much as possible in order to improve operational efficiency while ensuring user comfort. In such a case, for example, it is conceivable to set operating conditions such that the residence time of people in a highly congested place is set to a predetermined time or less, or the flow of people in other places is set to a predetermined speed or more. . Also, in this case, it is conceivable to set different operating conditions for a time zone when people are crowded and for other time zones. In this way, the operating conditions may be set to arbitrary conditions according to the use and purpose of the target floor.

The layout support apparatus 1 of the embodiment determines whether or not each of one or more input floor layouts satisfies the operating condition based on the operating condition set in this way. Specifically, the layout support apparatus 1 first estimates, for each floor layout, the change in the distribution of people when the target floor is operated with each floor layout, and based on the estimation result, determines the operation conditions. is calculated for each floor layout. Then, the layout support device 1 outputs, among the input floor layouts, floor layouts whose evaluation indices satisfy the operating conditions as processing results. By presenting the floor layout selected in this manner to the user, the user can operate the target area with a floor layout that is more suitable for the actual operating conditions of the target floor. The configuration of the layout support device 1 according to the embodiment capable of achieving such effects will be described in detail below.

FIG. 2 is a functional block diagram showing a specific example of the functional configuration of the layout support device 1 of the first embodiment. The layout support device 1 includes a CPU (Central Processing Unit), a memory, an auxiliary storage device, etc., which are connected via a bus, and executes a program. The layout support apparatus 1 executes a program to perform a floor layout input unit 101, an operating condition input unit 102, an initial condition input unit 103, a storage unit 11, a distribution estimation unit 12, an evaluation index calculation unit 13, an operation condition determination unit 14, and a determination unit. It functions as a device having the result output unit 15 . All or part of each function of the layout support apparatus 1 may be implemented using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). . The program may be recorded on a computer-readable recording medium. Computer-readable recording media include portable media such as flexible disks, magneto-optical disks, ROMs and CD-ROMs, and storage devices such as hard disks incorporated in computer systems. The program may be transmitted over telecommunications lines.

The floor layout input unit 101 has a function of inputting one or more predetermined floor layouts. Floor layout input section 101 outputs the input floor layout to distribution estimation section 12 .

The operational condition input unit 102 has a function of inputting operational conditions regarding the operation of the target floor. The operating condition input unit 102 outputs information indicating the input operating conditions to the operating condition determination unit 14 .

The initial condition input unit 103 has a function of inputting initial conditions when estimating changes in the distribution of people on the target floor. The initial condition input unit 103 outputs information indicating the input initial conditions to the distribution estimation unit 12 .

The floor layout input section 101, the operating condition input section 102, and the initial condition input section 103 are configured to input each information using an input device such as a touch panel, a mouse, and a keyboard. Further, for example, the floor layout input unit 101, the operating condition input unit 102, and the initial condition input unit 103 may be configured using a communication interface and configured to acquire each piece of information from an external communication device (not shown).

The storage unit 11 is configured using a storage device such as a magnetic hard disk device or a semiconductor storage device. The storage unit 11 stores various parameters (hereinafter referred to as "estimation parameters") for constructing the distribution estimation model. The distribution estimation model is a calculation model used when the distribution estimation unit 12 estimates changes in the distribution of people on the target floor. For example, the estimation parameters are a destination function, initial conditions, various thresholds, etc. used in the distribution estimation model. Information other than the estimated parameters may be stored in the storage unit 11 . For example, the storage unit 11 may store in advance the floor layout, operating conditions, initial conditions, and the like input to the device itself.

The distribution estimating unit 12 has a function of estimating changes in the distribution of people on the target floor by applying the floor layout and initial conditions input to the device to the distribution estimating model. Any model may be used as the distribution estimation model as long as it can determine changes in the distribution of people on the target floor. A case of using a cellular automaton as a distribution estimation model will be described below. A cellular automaton divides a target area into a plurality of cells, and converts the state of a certain cell (hereinafter referred to as the "targeted cell") at a certain time to the states of the target cell and its surrounding cells at an earlier time (hereinafter referred to as the "targeted cell"). This is a method of estimating based on "neighboring cells"). How the states of the neighboring cells affect the state of the target cell may be arbitrarily determined as an estimation rule to be described later. In this embodiment, the target area of the cellular automaton is the target floor, and the state of each cell is represented by the presence or absence of people and obstacles. By executing such estimation processing, the distribution estimation unit 12 acquires information indicating the presence or absence of people in each cell in time series (hereinafter referred to as “distribution information”). output to

Based on the distribution information acquired for each floor layout of the target floor, the evaluation index calculation unit 13 calculates an evaluation index relating to the operation of the floor layout from which the distribution information is obtained. For example, the evaluation index calculation unit 13 calculates the density of people (hereinafter referred to as “congestion level”) on the target floor, the residence time, the flow of people, the presence or absence of an area with a low usage rate, etc., as evaluation indices. The evaluation index calculation unit 13 outputs the calculated evaluation index value to the operation condition determination unit 14 .

The operating condition determination unit 14 determines whether each floor layout satisfies the operating condition based on the value of the evaluation index calculated for each floor layout and the operating condition of the target floor. The operating condition determination unit 14 outputs the floor layout determined to satisfy the operating condition (hereinafter referred to as “acceptable layout”) to the determination result output unit 15 .

The determination result output unit 15 has a function of outputting the acceptable layout output from the operating condition determination unit 14 . For example, the determination result output unit 15 is configured using a display device such as a CRT (Cathode Ray Tube) display, a liquid crystal display, an organic EL (Electro-Luminescence) display, etc., and causes these display devices to display the acceptable layout. Further, for example, the determination result output unit 15 may be configured using a communication interface and transmit the acceptable layout to another communication device.

FIG. 3 is a flow chart showing the flow of processing for identifying acceptable layouts from one or more predetermined floor layouts by the layout support apparatus 1 of the first embodiment. First, the distribution estimation unit 12 constructs a distribution estimation model (step S101). Specifically, the distribution estimating unit 12 constructs a distribution estimating model by reading the estimated parameters from the storage unit 11 and applying the read estimated parameter values to the cellular automaton. Subsequently, the floor layout input unit 101 inputs a predetermined floor layout for the target floor (step S102), and the operating condition input unit 102 inputs the operating conditions for the target floor (step S103).

The floor layout input here is input as data representing the target floor as a set of cells so that a distribution estimation model based on cellular automaton can be applied. In this case, the floor layout is expressed as data in which each cell constituting the target floor is given an attribute of the cell. For example, each cell is given an attribute indicating whether or not a person can move to that cell. This attribute is referred to as one of the constraints that limit the movement of people when estimating the distribution information of people on the target floor. Floor layout input section 101 outputs the input floor layout to distribution estimation section 12 . The operating conditions input here may be operating conditions for each floor layout, or may be operating conditions common to each floor layout.

Subsequently, the initial condition input unit 103 inputs initial conditions to be given to the distribution estimation model (step S104). Specifically, the initial conditions are the identification information of the person entering the target floor and the information giving the initial position to the distribution estimation model. For example, when obtaining changes in the distribution of people after the opening of the target floor, the initial positions of all people who enter the target floor at the initial time can be set as the entrance of the target floor. When assuming that a person enters the target floor at a time later than the initial time, the person's position may be fixed at the entrance of the target floor from the initial time until the entry time. The initial condition input unit 103 outputs the input initial conditions to the distribution estimation unit 12 .

Subsequently, the distribution estimation unit 12 selects one floor layout from one or more floor layouts input by the floor layout input unit 101, and constructs a distribution estimation model based on the selected floor layout and initial conditions in step S101. apply to Thereby, the distribution estimation unit 12 estimates a change in the distribution of people when the target floor is operated with the selected floor layout (step S105: distribution estimation processing). Specifically, based on the concept of cellular automaton, the distribution estimation unit 12 determines the state quantity of the cell of interest at the next time t+1 based on the state quantity of the cell of interest and the state quantities of neighboring cells at time t. do. As described above, the state quantity in this embodiment is a value indicating the presence or absence of a person or an obstacle in each cell. In this case, for example, the state quantity is “0”, which indicates that there are no people or obstacles, “1”, which indicates that there are people, and the pillars, walls, and fixtures (tables, etc.) of buildings that block people from passing. It can be represented either by "2", which represents the presence of obstacles such as partitions, etc.). These obstacles usually do not move and remain in the same cell over time. The distribution estimating unit 12 sequentially determines the state quantities of “0”, “1” or “2” for all cells in time series, thereby obtaining distribution information indicating changes in the distribution of the target floor during the estimation target period. can be obtained.

It should be noted that how to determine the state quantity of the target cell at the next time based on the state quantity of the target cell and the state quantities of the neighboring cells is a rule applied to the cellular automaton (hereinafter referred to as an "estimation rule"). It is set in advance. Here, for example, it is assumed that the estimation rule is stored in advance in the storage unit 11 as one of the estimation parameters. 4 and 5 are diagrams showing examples of such estimation rules. For simplicity, the estimation rule will be explained using 3×3 cells. The numbers 1 to 3 attached to the outside of the 3×3 cells are numbers for identifying each cell. For example, in the example of FIG. 4A, the cell in which a person exists is represented as cell (2, 3). FIG. 4 shows an example of human movement based on the following estimation rules.

(Rule 1) A person moves to a destination defined by a destination function.
The destination function is a function that determines the destination of a person existing in each cell, and is given as a different function for each area of stay. The stay area is a plurality of areas set within the target floor, and is an area determined according to usage and purpose. In other words, the person who reaches the destination has achieved the purpose in the stay area. shall be determined as follows: In addition, since it is possible that a person who has reached a destination stays at that destination for a certain amount of time, the destination function gives the person who has reached the destination the next destination after a predetermined time from the time of arrival. may be defined as 4 and 5 represent cells within the same stay area, and the same destination function is applied to each cell. That is, in the examples of FIGS. 4 and 5, it is assumed that the destination function determines the destinations of all cells in the direction of the solid arrows in the figures.

(Rule 2) People move at a constant speed.
For the sake of simplicity, it is assumed here that a person moves on the target floor at a constant speed. However, it is conceivable that the moving speed of a person changes depending on the destination and the situation of the target floor. With this in mind, the destination function may be defined as a function that determines the destination and the speed of movement toward that destination for a person present in each cell.

(Rule 3) If another person exists in the direction of the destination, the person stays there.
(Rule 4) If there are obstacles such as fixtures or other people in the direction of the destination, the person moves in any direction to avoid them. The dashed arrows in the figure represent the direction of movement that would be chosen if a detour is required.

In this case, in FIG. 4A, there is no other person in the cell (2, 2) adjacent in the traveling direction to the cell (2, 3) in which the person A is present. Therefore, person A who is present in cell (2,3) in FIG. 4A at time t moves to cell (2,2) as shown in FIG. 4B at time t+1. presumed to be

In this case, in FIG. 4C, another person B exists in the cell (2, 2) adjacent to the cell (2, 3) where the person A exists in the traveling direction. On the other hand, there is no other person in cell (2, 1), which is adjacent in the traveling direction to cell (2, 2) in which person B is present. Therefore, person A, who is present in cell (2, 3) in FIG. 4(C) at time t, remains in cell (2, 3) at time t+1, as shown in FIG. 4(D). It is estimated to be. On the other hand, at time t, person B in cell (2, 2) in FIG. 4(C) moves to cell (2, 1) at time t+1, as shown in FIG. 4(D). It is estimated to be.

In this case, in FIG. 4E, another person B exists in the cell (2, 2) adjacent to the cell (2, 3) where the person A exists in the traveling direction. On the other hand, an obstacle (a cell marked with "obstacle" in the drawing) exists in the cell (2, 1) adjacent to the cell (2, 2) in which the person B exists in the traveling direction. Therefore, person A who is present in cell (2, 3) in FIG. 4(E) at time t stays in cell (2, 3) at time t+1, as shown in FIG. 4(F). presumed to be On the other hand, at time t, person B in cell (2, 2) in FIG. Presumed to move. In this case, as shown in FIG. 4(F), person B is estimated to have moved to cell (3, 2) at time t+1. If there are multiple directions for detouring around the obstacle as in this example, one of the moving directions may be fixed, or one of them may be selected at random.

FIG. 5 shows another movement example of a person who moves based on the same estimation rule as in FIG. That is, the person's destination in FIG. 5 is determined to exist in the direction of the solid arrow in the figure by the destination function, as in FIG. In this case, in FIG. 5A, since there is an obstacle in the cell (2, 2) adjacent to the destination direction of the person A in the cell (2, 3), the person A avoids it. After moving in the direction of either dashed arrow, move in the direction of the solid arrow. As a result, it is estimated that person A moves from cell (2,3) to cell (1,2) or cell (3,2).

In FIG. 5B, there are obstacles in the cell (2,2) adjacent to the destination direction of the person A in the cell (2,3) and in the adjacent cell (3,2). exist. In this case, even if person A moves to cell (3, 3) to avoid an obstacle in cell (2, 2), person A moves to cell (3, 2) in the direction of the destination when viewed from the destination cell. Since there is also an obstacle, the only way is to return to the original cell (2, 3). Therefore, when obstacles exist adjacent to each other, the person A moves in the direction in which the obstacles are not adjacent (in the direction of the broken line arrow), and then moves in the direction of the solid line arrow to the destination. As a result, person A is estimated to move from cell (2,3) to cell (1,2).

In FIG. 5C, there is an obstacle in the cell (2, 2) adjacent to the destination direction of the person A in the cell (2, 3), and the cell (1 , 2) exists another person B. In this case, even if person A moves to cell (1,3) to avoid an obstacle in cell (2,2), he or she moves to cell (1,2) in the direction of the destination when viewed from the destination cell. has no choice but to stay in the destination cell because another person B is present. Therefore, when there is an obstacle or another person adjacent to it, Person A moves in the direction in which the obstacle or another person B is not adjacent (in the direction of the dashed arrow), and then moves to the solid line. Move in the direction of the arrow. As a result, person A is estimated to move from cell (2,3) to cell (3,2).

In FIG. 5(D), there is an obstacle in the cell (2, 2) adjacent to the destination direction of the person A in the cell (2, 3), and the cell (1 , 2) and cell (3, 2) are also obstructed. In this case, the obstacle exists in the destination direction in whichever direction the person A moves. Therefore, when there is no route to move from the destination cell to the destination in this way, the person A stays in the current cell (2, 3) or the adjacent cell (1, 3) or It is estimated to move to (3,3). Among these, which cell to move to may be determined based on a predetermined rule, or may be selected at random. For example, a destination cell closer to the destination may be selected, or a cell with the shortest path length to the destination may be selected. In selecting a destination cell, the proximity to the destination or the shortness of the route to the destination may be prioritized over other conditions.

In FIG. 5(E), as in FIG. 5(D), other persons B, C, and D are in cells (1, 2), (2, 2), and (3, 2). Existing. In this case, unlike the case of FIG. 5D, there is a possibility that person B, person C, and person D will move, so there is a possibility that a route from person A to the destination direction will be opened. Therefore, in such a case, person A stays at the current cell (2, 3) for a predetermined period of time, and if a route to the destination is opened within the predetermined period of time, the person A moves toward the destination. Presumed to move. Also, it is presumed that person A will move to the movable cell (1,3) or cell (3,3) if the route to the destination direction cannot be opened within a predetermined time. A shaded arrow indicates the direction in which the person A moves after waiting for a predetermined time. In such a situation, it may be sufficient to assume that person A will stay at the current location until a route to the destination opens. In this case, as shown in FIG. 5(F), it may be estimated that person A present in cell (2, 3) will also be present in cell (2, 3) at the next time.

When estimating changes in the distribution of people on the target floor, it may be better to assume people whose moving speed changes. For example, in a situation in which there are other people or obstacles in the direction of travel and there are multiple options for the direction of travel to bypass the obstacles, it is assumed that the person will stop once to select the direction of travel. Also, for example, it is assumed that a person carrying a smartphone temporarily stops to operate it. In this way, a person may stop even if he or she is able to move, so if such a person is assumed, the person who is able to move should be stopped for a predetermined period of time with a predetermined probability. may

In addition, there are cases where a person who has stopped once starts heading to a different destination than before. Assuming such a person, another destination may be set stochastically for the person who has temporarily stopped. In this case, another destination may be selected at random, or may be selected based on a predetermined rule. For example, the next destination set when the target person reaches the current destination may be another destination. Further, for example, one stay area other than the stay area where the target person is present may be selected, and the destination determined by the destination function of the selected stay area may be set as the other destination.

As will be described later, when the destination function takes the position and time of a person as variables, the destination functions of other stay areas selected for changing the destination include A person's current location cannot be entered. Therefore, in such a case, as an exception, the value of each variable input to the selected destination function may be arbitrarily determined within the possible range of each variable. For example, in this case, the center coordinates of the selected stay area may be input to the selected destination function, or The nearest cell coordinates may be entered into the selected destination function.

Also, a person who is moving or who has stopped may start moving at a different speed than before. When assuming such a person, the moving speed of the person existing on the target floor may be changed to a speed different from that before.

The distribution estimator 12 acquires the distribution information of the target floor by determining the state quantity of each cell at each time based on such an estimation rule, and outputs the acquired distribution information to the evaluation index calculator 13 . Note that the estimation rule described above is merely an example, and how to determine the state quantity of each cell may be arbitrarily determined according to the usage, properties, and the like of the target floor.

Returning to the description of FIG. Subsequently, based on the distribution information acquired for the floor layout to be processed, the evaluation index calculation unit 13 calculates an evaluation index for operation of the floor layout for which the distribution information is obtained (step S106). The evaluation index calculation unit 13 outputs the calculated evaluation index value to the operation condition determination unit 14 .

Subsequently, the operating condition determination unit 14 determines whether the floor layout to be processed satisfies the operating conditions based on the value of the evaluation index calculated for the floor layout to be processed and the operating conditions input in step S103. It is determined whether or not (step S107). The operation condition determination unit 14 determines whether the floor layout to be processed is an acceptable layout based on the operation condition determination result (step S108). Specifically, the operating condition determination unit 14 determines that the floor layout to be processed is an acceptable layout when all the operating conditions to which the evaluation index values acquired for the floor layout to be processed are given. , is not satisfied, the floor layout to be processed is determined not to be an acceptable layout.

If it is determined that the floor layout to be processed is an acceptable layout (step S108-YES), the operating condition determining unit 14 outputs the acceptable layout to the determination result output unit 15 (step S109), and the process proceeds to step S110. proceed. On the other hand, if it is determined that the floor layout to be processed is not an acceptable layout (step S108-NO), the operation condition determining unit 14 advances the process to step S110 without outputting an acceptable layout.

Subsequently, the operation condition determination unit 14 determines whether or not all the floor layouts input in step S102 have been subjected to determination processing as to whether or not they are acceptable layouts (step S110). If unprocessed floor layouts remain (step S110-NO), the operating condition determination unit 14 returns the process to step S105. In this case, the distribution estimating section 12 selects one of the unprocessed floor layouts and executes the distribution estimating process. On the other hand, if all floor layouts have been processed (step S110-YES), the operating condition determination unit 14 ends the process.

FIG. 6 is a flowchart showing the flow of distribution estimation processing in the first embodiment. First, the distribution estimating unit 12 selects one of the floor layouts input in advance as a processing target (step S201). Based on the selected floor layout, the distribution estimating unit 12 sets the physical constraints of the target floor, the arrangement of fixtures, etc. to each variable. Subsequently, the distribution estimation unit 12 initializes the time t to 0 (step S202). Subsequently, the distribution estimating unit 12 sets the state quantity S(t, x, y) of each cell at the initial time t=0 based on the input initial conditions (step S203). Here, the state quantity S(t, x, y) represents the presence or absence of a person or an obstacle in cell (x, y) at time t. x and y represent the coordinates of the cell.

Subsequently, the distribution estimating unit 12 sets the destination function D(t, x, y) of the people present in the cell (x, y) at time t=0 (step S204). As described above, the destination function is a function that determines a person's destination, and is a function that is determined according to the stay area in which the person is present. For example, the destination function D(t, x, y) is represented by the following equation (1).

In Equation (1), i is the identification number of the stay area. For example, i takes any value from 1 to 4 when four stay areas are defined on the target floor. The stay area identified by the identification number i is hereinafter referred to as "stay area (i)". Also, equation _i (t, x, y) represents a different function for each stay area (i), and x and y represent coordinates of cells included in each stay area. That is, the formula (1) expresses that the destination function D is defined as a function that differs for each stay area, and that the time t and the cell coordinates (x, y) are defined as variables.

FIG. 7 is a diagram showing a specific example of stay areas set within the target floor. As described above, the stay area is a plurality of areas set within the target floor, and is an area determined according to usage and purpose. For example, FIG. 7 shows an example in which four stay areas #1 to #4 are defined with the employee cafeteria as the target floor. For example, in this example, stay areas #1 to #4 are defined as areas in which a person entering the target floor performs the following actions. Also, as shown in FIG. 7, each stay area may be defined so that a part thereof overlaps each other.

(Stay Area #1) An area where a person entering from the entrance E1 moves to a desired meal serving area and waits for the order of serving the meal in the meal serving area. Each serving area is partitioned by partitions P1 to P4.
(Stay Area #2) An area where a person who has been served moves to a desired table.
(Stay Area #3) An area where people who have finished eating at the table move to the lower dining area and wait for their turn in the lower dining area. In the lower dining area, three lower dining sinks S1 to S3 are installed.
(Stay area #4) An area where a person who has finished eating a table moves to the exit E2.

Note that the formula (1) is a formula for uniquely determining the destination function according to the area of stay. It may be affected by the situation of the area (hereinafter referred to as "surrounding area"). Therefore, if it is necessary to consider the situation of the surrounding area when determining the destination, the destination function D(t, x, y) may be defined as shown in Equation (2) below.

In Equation (2), α is a constant that can take values from 0 to 1, and _iβ is an identification number indicating the area surrounding the stay area (i). That is, equation (2) defines the destination function as the α-based weighted sum of the function defined for the stay area (i) and the function defined for the stay area (i _β ). As a result, the destination function for a certain stay area can be determined in consideration of the conditions of the surrounding areas. For example, as the peripheral area (i _β ), it is conceivable to select a stay area adjacent to the moving direction of a person existing in the stay area (i). Also, in addition to the situation of the surrounding area (i _β ), when considering the situation of other surrounding areas (i _γ ), the formula (2) is the stay area (i), the surrounding area (i _β ) and the surrounding area ( i _γ ) may be modified so that the weighted sum of each equation of i γ ) is used as the destination function.

In this case, α is a parameter that determines the weight of each stay area. α may be set in advance as a fixed value, or may be set as a variable value that changes depending on factors that change the weight. For example, factors that change the weight include the situation of the surrounding area (for example, the degree of congestion) and the situation of the target person (for example, the destination and the speed of movement).

In addition, it is conceivable that the destination of a person is affected by the past situation of the target person and the past situation of the surrounding area. Therefore, if such a situation is assumed, the destination function may be defined as a function that includes statistical values (for example, average values, etc.) regarding such past situations as parameters.

In this way, by setting a stay area for each use and purpose in the target area and defining a destination function that determines the destination of people in the target area according to the stay area of the person, the degree of congestion in the target area can be calculated. can be analyzed including potential factors. For example, it is possible to analyze whether the number of vacant seats is sufficient for the number of people who are looking for seats, using the determination formula shown in the following formula (3).

In equation (3), _Nvac represents the number of vacant seats in a stay area (hereinafter referred to as "seat area") whose destination is the seat. The closer N _vac is to 0, the closer to being full the seats are, so it can be used as an index for measuring the degree of congestion in the seating area. On the other hand, n _area represents the number of people looking for seats in the seating area. Here, the fact that a seat area is a 'stay area whose destination is a seat' means that the destination function defined for that stay area determines the seat as the destination of a person within that stay area. In addition, as described above, the destination function determines a different stay _area as the next destination for those who have reached the destination. .

In addition, n _add is the number of people who are moving at a speed exceeding a predetermined threshold, with a seat in the seating area as their destination, among the people in the stay area adjacent to the seating area (hereinafter referred to as "adjacent area"). represents n _sub represents the number of people who are moving at a predetermined speed with the seat as their destination among the people present in the seat area. In this case, assuming that the movement speed of a person who is present in the seat area and whose destination is the seat is below a predetermined threshold, even if the person is present in the seat area, the purpose is to move to another stay area. A person moving as a ground is counted in n _sub . As described above, the destination function in this case can determine not only the destination but also the moving speed toward the destination. In addition, the destination determination may take into consideration the degree of congestion of each stay area. shall be able to decide.

Note that δ is a predetermined constant and is a parameter that enables adjustment of the determination result of equation (3). For example, δ may be adjusted based on actual measurements of N _{vac ,} n _area , n _add , n _sub obtained during operation with an initial value (eg, 1).

By setting such a destination function D(t, x, y), the destination of a person present in the cell (x, y) at time t can be determined according to the stay area. Note that the equation that determines the destination function D(t, x, y) may include arbitrary parameters that are correlated with the reason why the person decides the destination, in addition to the time and area of stay. For example, the above-mentioned parameters can be the areas of stay where the person has stayed in the past, the total time the person has stayed in each area of stay, the destination in the past, the movement route to the present, and the like.

As described above, in the distribution estimation model using the cellular automaton, the state quantity of the target cell at the next time is determined based on the state quantities of the target cell and the neighboring cells at a certain time. It is not always necessary to identify a person individually. However, as described above, when information based on each person's movement record (hereinafter referred to as "history information") is used as a parameter for the distribution estimation process, while individually identifying each person present on the target floor, Its movement may be estimated. For example, the distribution estimation unit 12 can acquire history information of each person by assigning an identification number to each person and recording position information of each person at each time in association with the identification number.

Returning to the description of FIG. After setting the destination function D(t, x, y) of each cell (x, y) at the initial time t=0 in step S204, the distribution estimation unit 12 advances the time t to the next time (step S205), Based on the destination function D(t-1, x, y) at time t-1, the state quantity S(t, x, y) of each cell (x, y) at current time t is determined (step S206 ). That is, when step S206 is executed for the first time, the state quantity S(1, x, y) of each cell at time t=1 is determined based on the destination function D(0, x, y) at time t=0. be.

Subsequently, the distribution estimation unit 12 determines whether the current time t has reached the maximum time _tmax of the estimation target period (step S207). Note that the time t _max may be arbitrarily set according to a desired estimation target period, and is set in advance as one parameter of the distribution estimation model. If the current time t has reached the time t _max (step S207-YES), the distribution estimating unit 12 outputs the processing result to the evaluation index calculating unit 13, and ends the distribution estimating process for the floor layout to be processed. . On the other hand, if the current time t has not reached the time t _max (step S207-NO), the distribution estimating unit 12 determines that each cell exists based on the state quantity S(t, x, y) of each cell at the current time t. The destination function D(t, x, y) of the person who travels is updated (step S208), and the process returns to step S205. By executing such processing, the state quantity S(t, x, y) of each cell at each time t from the initial time t=0 to the maximum time t=t _max is determined.

FIG. 8 is a diagram showing a specific example of destinations determined by the destination function for people present in each stay area on the target floor shown in FIG. First, the user of the employee cafeteria enters the target floor through the entrance E1 included in the stay area #1. In the stay area #1, the user's destination is set to one of the serving areas (target coordinates 1a to 1e). Destinations may be determined so that people are dispersed according to the degree of congestion in the vicinity of each serving area, or the destination may be changed to another serving area along the way according to the degree of congestion in the vicinity of each serving area. good too. On the other hand, for a person who has reached one of the serving areas, which is the destination in stay area #1, one of the seats (target coordinates 2a to 2h) in stay area #2 is set as the next destination. . For example, the next destination may be randomly assigned a vacant seat at that point in time, or may be assigned a vacant seat close to the current position.

Next, for a person who has reached one of the destination seats in stay area #2, one of the lower dining areas (target coordinates 3a to 3c) of stay area #3 is set as the next destination. be. In this case as well, the next destination may be determined according to the degree of congestion in the vicinity of each lower tray area so that the number of people may be dispersed, or the destination may be set halfway in accordance with the degree of congestion in the vicinity of each lower tray area. may be changed to another lower dining area.

Next, exit E2 (target coordinates 4a) is set as the next destination for a person who has reached one of the lower dining areas, which is the destination, in stay area #3. In this case, the next destination of some of the users leaving the target floor is set to the entrance E1 (target coordinates 4b) so that the users leaving the target floor do not concentrate too much on the exit E2. good too. Whether the next destination of some users should be the entrance E1 may be determined according to the degree of congestion near the entrance E1 or the exit E2.

In this way, by dividing the target area into multiple stay areas according to usage and purpose and defining different destination functions for each stay area, users who move within the target area according to a series of purposes It is possible to analyze the movement of

The layout support device 1 of the first embodiment configured as described above can select a floor layout that satisfies the operating conditions of the target floor from among a plurality of predetermined floor layouts. Specifically, the layout support apparatus 1 performs distribution estimation processing on a predetermined floor layout to estimate the degree of congestion of the target floor in time series, and whether or not the operating conditions are satisfied based on the estimation result. determine whether As a result, the layout support device 1 of the first embodiment can adopt a floor layout that is more suitable for the actual operating conditions of the target floor, taking into consideration the temporal change in the distribution of people.

Note that the layout support device 1 may be configured to output information (hereinafter referred to as "basis information") indicating the basis for the selection of the acceptable layout. For example, the determination result output unit 15 outputs distribution information obtained based on the acceptable layout and information indicating how the acceptable layout satisfies the operation conditions as basis information in association with the acceptable layout. may For example, the determination result output unit 15 may output ground information as shown in FIG. 9 below.

For example, FIG. 9 shows the result of evaluation of the level at which each of floor layouts #1 to #4, which are acceptable layouts obtained for the same target floor and have different characteristics, satisfies the operating conditions. indicates Note that the basis information may include various auxiliary information related to each floor layout, such as the number of seats shown in FIG. In this example, floor layout #1 is a floor layout that emphasizes the accommodation efficiency of users who use the target floor. For example, floor layout #1 is the floor layout illustrated in FIG. 10, and the number of seats is 90, which is 10 more than the example of FIG. 7 (the number of seats is 80). Therefore, the evaluations in terms of spaciousness and degree of congestion are not so high, and both operational conditions #1 and #2 are evaluated as moderate (Δ in the figure).

Also, in this example, floor layout #2 is a floor layout designed for the purpose of making the target floor a suitable space for lunch meetings with customers. For example, floor layout #2 is the floor layout illustrated in FIG. 11 (73 seats). In this example, on the left half of the target floor, a plurality of partitioned sections are provided with relatively large margins as spaces for lunch meetings with customers. On the other hand, in the space on the right side of the target floor, a lunch space for employees is provided with an emphasis on storage efficiency. In this way, floor layout #2 is a floor layout that prioritizes lunch meetings with customers, so both operating conditions #1 and #2 were highly evaluated by customers (◎ and 〇 in the figure). For employees, it represents a medium evaluation (△ in the figure).

Also, in this example, floor layout #3 is a floor layout designed with an emphasis on allowing users of the target floor to stay for a long time. For example, floor layout #3 is the floor layout illustrated in FIG. In this example, a space where users can stay for long periods of time is provided by setting up tables with sofas, etc., and both operating conditions #1 and #2 were highly evaluated (◎ and 〇 in the figure). It means that

On the other hand, the floor layout #4 is an improved floor layout for the purpose of enhancing the comfort of the flow line of the floor layout #3. For example, floor layout #4 is an improved version of floor layout #3, and is the floor layout illustrated in FIG. Floor layout #4 removes the furniture E3 on the left side of the page shown in Fig. 7, and provides guides E41 to E45 in front of each serving area to adjust the flow of people to improve the comfort of the flow line. be. In this case, based on the display of ground information as shown in the example of FIG. It becomes possible to quantitatively grasp that the improvement over #3 is appropriate.

Here, the evaluation of validity based on the determination result of operating conditions is shown as ground information, but the ground information may be any other information as long as it is information related to the validity of each floor layout. For example, the determination result output unit 15 may output the index value calculated for each operating condition and the threshold value determined for the index value as basis information, or may output the distribution estimated by the distribution estimation process. Information or information obtained based on distribution information may be output as ground information.

For example, FIG. 13 shows an example in which the target floor shown in floor layout #3 is divided into units of cells handled by the cellular automaton, and FIGS. FIG. 10 is a diagram showing estimated frequencies for each cell. Here, FIG. 14 shows the frequencies for region A shown in FIG. 15 shows the frequencies for the area B shown in FIG. 13, and FIG. 16 shows the frequencies for the area C shown in FIG. For example, the frequency can be obtained by summing up the number of times it is determined that a person exists for each cell, based on the distribution information for a predetermined period estimated in floor layout #3. By displaying such information as ground information, it is possible to visualize the congestion degree of the target floor in a predetermined period. Note that the division of cells in FIGS. 14 to 16 does not strictly match the division of cells in FIG. 13 due to space limitations.

According to this example, it can be seen that the flow of people (arrows in the figure) collides locally, such as near the entrance E1 (area A) and near the exit E2 (area B), causing people to stagnate (Fig. 14 and Figure 15). For example, near the entrance E1, it can be seen that there is a high possibility that stagnation will occur over a wide area due to the collision of the flow of people flowing into the target floor and the flow of people entering and exiting the catering area. Similarly, near the exit E2, it can be seen that there is a high possibility that stagnation will occur over a wide area due to the collision of people flowing toward the exit from the left, right, and downward directions. In addition, it can be seen that there is a high possibility that stagnation will occur in the vicinity of the lower tray area (region C) as well, due to the influence of the congestion situation in the vicinity of the entrance E1 and the vicinity of the exit E2.

On the other hand, FIG. 17 shows an example in which the target floor shown in floor layout #4 is divided into units of cells similar to those in FIG. 13, and FIGS. It is the figure which showed the frequency estimated that a person exists for every cell. Here, FIG. 18 shows the frequencies for region A shown in FIG. FIG. 19 shows the frequencies for the area B shown in FIG. 17, and FIG. 20 shows the frequencies for the area C shown in FIG. As described above, floor layout #4 is a floor layout that attempts to improve floor layout #3 in order to improve the comfort of the line of flow. 14 to 16, the cell division in FIGS. 18 to 20 does not strictly match the cell division in FIG. 17, but the cell division in FIGS. is consistent with

According to this example, it can be seen that the stagnation of people that occurred near the entrance E1 (region A) and near the exit E2 (region B) of the floor layout #3 was improved by partially changing the layout ( 18 and 19). Specifically, in FIG. 17, the fixture E3 shown in FIG. 13 is removed, and guides E41 to E45 are installed to regulate the flow of people in each serving area. In addition, it can be seen from FIG. 20 that the congestion near the entrance E1 and the exit E2 has been alleviated, thereby improving the retention in the vicinity of the lower tray area (region C).

In this way, by outputting the distribution information estimated for each floor layout as evidence information, the operator of the target floor can visualize and quantify how effective the designed floor layout is in actual operation. It is possible to grasp the In addition, by outputting such distribution information as ground information for partially common floor layouts such as floor layout #3 and floor layout #4, the operator of the target floor can improve the intended layout. It is possible to visually and quantitatively grasp whether or not is effective.

Here, the information indicating the grounds of the acceptable layout is shown as the grounds information. However, the grounds information includes other floor layouts that were not judged as acceptable layouts (hereinafter referred to as "failed layouts"). Information indicating the grounds for the decision may be included. By displaying the basis information of the acceptable layout and the basis information of the unacceptable layout in this way, the operator of the target floor can quantitatively grasp the difference between the acceptable layout and the unacceptable layout. Become.

(Second embodiment)
FIG. 21 is a functional block diagram showing a specific example of the functional configuration of the layout support device 1a of the second embodiment. The layout support apparatus 1a further includes a constraint condition input unit 104, a performance data input unit 16, and an estimated parameter adjustment unit 17, and has a distribution estimator 12a instead of the distribution estimator 12. It is different from the support device 1. Other configurations of the layout support device 1a are the same as those of the layout support device 1 of the first embodiment. Therefore, in FIG. 21, the same configurations as in the first embodiment are denoted by the same reference numerals as in FIG. 2, and descriptions of the same configurations are omitted.

The constraint input unit 104 has a function of inputting constraints regarding the arrangement of fixtures on the target floor. Constraint conditions are information that is referred to when editing a floor layout, and are used as conditions for determining whether or not an editing operation on a floor layout satisfies the constraints defined for the target floor. For example, the constraint conditions indicate restrictions on places where fixtures can be placed on the target floor, types of fixtures that can be placed on those places, and the like. Constraints are defined based on the design of the facility that has the target floor. The constraint input unit 104 outputs information indicating the input constraint to the distribution estimation unit 12 .

The performance data input unit 16 has a function of inputting performance data. The performance data is data indicating the measured value of the distribution of people observed in the actual operation of the target floor, that is, the data indicating the performance of the distribution information. The performance data may be acquired by any method as long as it indicates the presence or absence of people for each cell of the target floor. For example, performance data may be acquired by a human sensor provided for each cell, or may be acquired by an image sensor capable of detecting the presence or absence of a person in each cell. The performance data input unit 16 outputs the input performance data to the estimated parameter adjustment unit 17 .

Note that the constraint condition input unit 104 and the performance data input unit 16 described above are configured to input each information using an input device such as a touch panel, a mouse, and a keyboard, like the other input units. Further, for example, the constraint condition input unit 104 and the performance data input unit 16 may be configured using a communication interface and configured to acquire each piece of information from an external communication device (not shown).

The estimated parameter adjustment unit 17 has a function of adjusting estimated parameters used for distribution estimation processing based on performance data. Specifically, the estimation parameter adjustment unit 17 determines the estimation parameters so that the difference between the distribution information obtained by the distribution estimation model and the distribution information indicated by the performance data is equal to or less than a predetermined threshold. For example, the estimated parameter adjuster 17 performs the same distribution estimation process as the distribution estimator 12a while gradually changing the estimated parameter values for the floor layout of the target area when the performance data is obtained. The estimated parameter adjustment unit 17 extracts the distribution information whose difference value from the distribution information indicated by the performance data is equal to or less than a predetermined threshold from among the plurality of pieces of distribution information obtained as a result, and uses the extracted distribution information as The estimated parameter used in the distribution estimation process when obtained is determined as the adjusted estimated parameter.

In adjusting the estimated parameter, the value of the estimated parameter may be changed based on a predetermined rule, or may be changed to a value specified by the operator. Further, the distribution estimating unit 12a may be configured to determine the contribution of each estimated parameter based on the results of distribution estimation processing performed a plurality of times, and to preferentially change parameters with a large contribution. Further, the process of adjusting the estimated parameters by the estimated parameter adjustment unit 17 is hereinafter referred to as "calibration process".

The distribution estimating unit 12a has a function of changing the floor layout to be processed or various conditions (operating conditions or initial conditions) in addition to the functions of the distribution estimating unit 12 in the first embodiment. Moreover, the distribution estimating unit 12a can determine whether or not the floor layout after the change is an acceptable layout by performing the distribution estimating process on the floor layout after the change.

FIG. 22 is a flow chart showing the flow of processing for identifying acceptable layouts from one or more predetermined floor layouts by the layout support apparatus 1a of the second embodiment. In addition, in FIG. 22, the same processing as in the flowchart in the first embodiment is given the same reference numerals as in FIG. 3, and the description thereof is omitted. First, the constraint condition input unit 104 inputs a constraint condition (step S301). The constraint condition input unit 104 outputs the input constraint condition to the distribution estimation unit 12a. Subsequently, in step S108, when it is determined that the floor layout to be processed is not an acceptable layout (step S108-YES), the distribution estimating unit 12a determines the floor layout to be processed or various conditions (operating conditions or initial conditions). It is determined whether or not to change (step S302).

For example, this determination is made based on information input by a user (for example, a floor operator) indicating whether or not to make the above change. When it is determined that the floor layout to be processed or various conditions are not changed (step S302-NO), the distribution estimating section 12a shifts the process to step S110. On the other hand, if it is determined to change the floor layout to be processed or various conditions (step S302-YES), the distribution estimating unit 12a changes the floor layout to be processed or various conditions (step S303), then step The process is returned to S105.

When changing the floor layout, the distribution estimating unit 12a changes the floor layout within a range in which the content of the change satisfies the constraint conditions defined for the target layout. For example, when the user inputs changes to the floor layout, there is a possibility that changes that satisfy the constraint conditions may not always be entered. For example, it is conceivable that an operation to place or move a fixture to a location where placement of the fixture is not permitted is input. Therefore, it is possible to prevent an incorrect floor layout from being processed by the distribution estimating unit 12a judging the constraint conditions for the input changes.

Further, the floor layout and various conditions may be changed based on predetermined rules, or may be changed based on input of a change operation by the user. Further, the distribution estimating unit 12a may be configured so as not to accept subsequent changes when the floor layout has been changed a predetermined number of times.

FIG. 23 is a flow chart showing the flow of calibration processing performed by the layout support device 1a of the second embodiment. First, the performance data input unit 16 inputs performance data (step S401). The performance data input unit 16 outputs the input performance data to the estimated parameter adjustment unit 17 . Subsequently, the estimated parameter adjustment unit 17 constructs a distribution estimation model based on the estimated parameters stored in the storage unit 11 at this time (step S402), inputs initial conditions (step S403), and determines the floor layout. Input (step S404). Note that the floor layout input in step S404 is the floor layout used in the actual operation of the target floor when the performance data was acquired. The estimation parameter adjustment unit 17 executes distribution estimation processing by applying the floor layout and initial conditions thus input to the constructed distribution estimation model (step S405). The distribution estimation processing executed here is the same as the distribution estimation processing in the first embodiment.

Subsequently, the estimated parameter adjustment unit 17 compares the distribution information obtained by the distribution estimation process with the distribution information indicated by the performance data (step S406), and determines whether or not the estimated parameter needs to be adjusted based on the comparison result. (step S407). If it is determined that the estimated parameters need not be adjusted (step S407-NO), the estimated parameter adjustment unit 17 ends the calibration process. On the other hand, if it is determined that the estimated parameter needs to be adjusted (step S407-YES), the estimated parameter adjustment unit 17 changes the estimated parameter (step S408), and then returns the process to step S405.

The layout support device 1a of the second embodiment configured as described above can adjust the parameters of the distribution estimation model based on the actual data of the distribution information obtained in the actual operation. Therefore, when changing the layout of a target floor that is operated with a predetermined floor layout, it is possible to evaluate the changed floor layout based on more accurate distribution information.

Further, the layout support apparatus 1a of the second embodiment can change a part of the floor layout to be processed, and can also determine the operating conditions for the changed floor layout. Therefore, even if a certain floor layout is determined to be an unacceptable layout, a user (for example, a floor operator) can create an acceptable layout while partially changing the unacceptable layout.

According to at least one of the embodiments described above, the layout support device of the embodiment performs the An evaluation index calculation unit that calculates an evaluation index value relating to the operation of the target floor for each of one or more predetermined floor layouts, and based on the evaluation index values calculated for each of the one or more floor layouts and an operation condition determination unit that determines, for each of the one or more floor layouts, whether or not predetermined operation conditions regarding the operation of the target floor are satisfied when the target floor is operated on each floor layout. By having it, it is possible to present a floor layout that is more suitable for the actual operation situation of the target floor.

While several embodiments of the invention have been described, these embodiments have been presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, as well as the scope of the invention described in the claims and equivalents thereof.

Reference Signs List 1, 1a Layout support device 101 Floor layout input unit 102 Operating condition input unit 103 Initial condition input unit 104 Constraint condition input unit 11 Storage unit 12, 12a Distribution estimation unit 13 ... evaluation index calculation section, 14 ... operating condition determination section, 15 ... determination result output section, 16 ... performance data input section, 17 ... estimation parameter adjustment section

Claims (10)

Based on floor layout information indicating the layout of fixtures on the target floor and distribution information indicating changes in the distribution of people on the target floor, one or more predetermined evaluation index values relating to the operation of the target floor are set. an evaluation index calculation unit that calculates each of the floor layouts of
Based on the value of the evaluation index calculated for each of the one or more floor layouts, when the target floor is operated in each floor layout, a predetermined operational condition regarding the operation of the target floor is satisfied. an operating condition determination unit that determines whether each of the one or more floor layouts is satisfied;
a distribution estimation unit that obtains the distribution information by estimating changes in the distribution of people on the target floor based on a given floor layout;
with
The evaluation index calculation unit calculates the value of the evaluation index based on the estimated distribution information.
Layout support device. further comprising a determination result output unit that outputs a determination result of the operating condition,
The operating condition determination unit determines whether each floor layout satisfies one or more predetermined operating conditions,
The determination result output unit outputs a floor layout that satisfies all of the one or more operating conditions as an acceptable layout.
2. The layout support device according to claim 1. The distribution estimating unit divides the target floor into a plurality of stay areas according to the purpose of the users, determines the destination of people existing in each stay area by a different destination function for each stay area, estimating the change in the distribution assuming that each moves to its set destination,
3. The layout support device according to claim 1 . The distribution estimating unit estimates the moving speed of each person present on the target floor based on the distribution information obtained for two or more times in the past, and determines in advance about the person moving at a speed equal to or higher than a predetermined threshold setting a predetermined destination specified by
4. The layout support device according to any one of claims 1 to 3 . The distribution estimating unit changes a part of the floor layout determined not to satisfy the operating condition, estimates changes in the distribution of the changed floor layout,
The operating condition determination unit determines whether the operating condition is satisfied for the floor layout after the change.
5. The layout support device according to any one of claims 1 to 4 . The distribution estimating unit changes a part of the floor layout that is determined not to satisfy the operating condition within the range of constraints predetermined for the target floor.
6. The layout support device according to claim 5 . Based on the distribution information acquired by the distribution estimating unit and performance data indicating changes in the distribution acquired in the actual operation of the target floor, the distribution estimating unit detects changes in the distribution of people on the target floor. Further comprising an estimation parameter adjustment unit that adjusts the parameters of the distribution estimation model used when estimating the
7. The layout support device according to any one of claims 1 to 6 . The estimated parameter adjusting unit adjusts the parameters so that a difference between the distribution information acquired by the distribution estimating unit and the distribution information indicated by the performance data is equal to or less than a predetermined threshold.
8. The layout support device according to claim 7 . Based on floor layout information indicating the layout of fixtures on the target floor and distribution information indicating changes in the distribution of people on the target floor, one or more predetermined evaluation index values relating to the operation of the target floor are set. an evaluation index calculation step for calculating each of the floor layouts of
Based on the value of the evaluation index calculated for each of the one or more floor layouts, when the target floor is operated in each floor layout, a predetermined operational condition regarding the operation of the target floor is satisfied. an operating condition determining step of determining whether each of the one or more floor layouts is satisfied;
a distribution estimation step of obtaining the distribution information by estimating changes in the distribution of people on the target floor based on a given floor layout;
has
calculating the value of the evaluation index based on the estimated distribution information in the evaluation index calculation step;
Layout assistance method. Based on floor layout information indicating the layout of fixtures on the target floor and distribution information indicating changes in the distribution of people on the target floor, one or more predetermined evaluation index values relating to the operation of the target floor are set. an evaluation index calculation step for calculating each of the floor layouts of
Based on the value of the evaluation index calculated for each of the one or more floor layouts, when the target floor is operated in each floor layout, a predetermined operational condition regarding the operation of the target floor is satisfied. an operating condition determining step of determining whether each of the one or more floor layouts is satisfied;
a distribution estimation step of obtaining the distribution information by estimating changes in the distribution of people on the target floor based on a given floor layout;
is executed by the computer,
calculating the value of the evaluation index based on the estimated distribution information in the evaluation index calculation step;
A computer program for

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