People flow control system and people flow control method

Abstract

To optimize a human flow without making persons in a venue conscious of human flow control being performed.SOLUTION: A system for controlling a human flow in a venue including a plurality of areas comprises: a server; at least one of a sensor and a camera which acquires a value indicating the human flow between the plurality of areas; and a device which controls the human flow of the plurality of areas. The server holds person number information indicating the number of persons that have entered the venue and event information indicating an event performed in each of the plurality of areas, predicts the human flow in each of the plurality of areas on the basis of the person number information and the event information, decides at least one of arrangement and orientation of the device and processing by the device in the plurality of areas on the basis of the predicted human flow, and the value acquired by at least one of the sensor and the camera. The device operates at a speed equal to or less than a prescribed value on the basis of the decision.SELECTED DRAWING: Figure 13

Description

[Technical Field]

[0001] The present invention relates to a people flow control system and a people flow control method. [Background technology]

[0002] Japanese Patent Application Laid-Open No. 2019-215906 (Patent Document 1) is a background technology of the present invention. This publication states that "the guidance processing device includes an information acquisition unit that acquires multiple pieces of guidance information that are different from each other based on the states of multiple people in one or more images, and a control unit that controls multiple target devices that exist in different spaces or executes time-sharing control of the target devices so that the target devices are in multiple different states that correspond to the multiple pieces of guidance information" (see Abstract). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-215906 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology described in Patent Document 1 guides crowds by displaying guidance information indicating less crowded areas on a display device connected to a guidance processing device. However, even if less crowded areas are presented to visitors at a venue such as an amusement park and made aware of them, it is difficult to change the visitors' desire to visit more popular areas, and there is a risk that the influx of people into popular areas and congestion may not be reduced. Therefore, one aspect of the present invention optimizes people flow without making people in the venue aware that people flow control is being performed. [Means for solving the problem]

[0005] To solve the above problem, one aspect of the present invention employs the following configuration: A system for controlling people flow in a venue including multiple areas includes a server, at least one of a sensor and a camera that acquires values ​​indicating people flow, which indicate the flow of people in and out of the multiple areas and the accumulation of people, and a device that can be placed in any of the multiple areas and controls the people flow, wherein the server stores headcount information indicating the number of people entering the venue and event information indicating events taking place in each of the multiple areas, predicts the people flow for each of the multiple areas based on the headcount information and the event information, and executes a device placement optimization process that determines the placement and orientation of devices and at least one of processes performed by the devices in the multiple areas based on the predicted people flow and values ​​acquired by at least one of the sensor and the camera, and the devices operate at a speed equal to or less than a predetermined value based on the results of the device placement optimization process. [Effects of the Invention]

[0006] According to one aspect of the present invention, it is possible to optimize the flow of people at a venue without making people aware that people flow control is being performed.

[0007] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is an explanatory diagram illustrating an example of a venue that is a target of people flow control by the people flow control system according to a first embodiment. [Figure 2] 1 is a block diagram showing a configuration example of a people flow control system according to a first embodiment. [Figure 3] FIG. 2 is a block diagram showing the hardware configuration of a people flow control server according to the first embodiment. [Figure 4] FIG. 3 is a diagram illustrating an example of the data configuration of sensor camera arrangement information according to the first embodiment. [Figure 5] FIG. 3 is a diagram illustrating an example of the data configuration of marker / beacon placement information in the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating an example of the data configuration of sensor camera data in the first embodiment. [Figure 7] FIG. 3 is a diagram illustrating an example of a data configuration of entrance / exit information in the first embodiment. [Figure 8] FIG. 3 is a diagram illustrating an example of a data configuration of ticket reservation information according to the first embodiment. [Figure 9] FIG. 3 is a diagram illustrating an example of a data configuration of event schedule information according to the first embodiment. [Figure 10] FIG. 4 is a diagram illustrating an example of a data configuration of area information according to the first embodiment. [Figure 11] FIG. 4 is a diagram illustrating an example of a data configuration of device control information according to the first embodiment. [Figure 12] FIG. 3 is a diagram illustrating an example of a data configuration of people flow information in the first embodiment. [Figure 13] FIG. 2 is a block diagram showing an example of the functional configuration of a people flow control calculation unit in the first embodiment. [Figure 14] 10 is a flowchart showing an example of a people flow and congestion visualization process in the first embodiment. [Figure 15] 10 is a flowchart illustrating an example of a device location visualization process according to the first embodiment. [Figure 16] 10 is a flowchart showing an example of a people flow and congestion prediction simulation process in the first embodiment. [Figure 17] 10 is a flowchart illustrating an example of an apparatus placement determination process according to the first embodiment. [Figure 18A] FIG. 2 is an explanatory diagram showing an example of device placement optimized by the device placement optimization simulation unit in the first embodiment. [Figure 18B] FIG. 2 is an explanatory diagram showing an example of device placement optimized by the device placement optimization simulation unit in the first embodiment. [Figure 18C] FIG. 2 is an explanatory diagram showing an example of device placement optimized by the device placement optimization simulation unit in the first embodiment. [Figure 19] 1 is a block diagram illustrating an example of a functional configuration of an apparatus according to a first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In this embodiment, the same components are generally designated by the same reference numerals, and repeated explanations will be omitted. It should be noted that this embodiment is merely an example for realizing the present invention, and does not limit the technical scope of the present invention. [Example]

[0010] 1 is an explanatory diagram showing an example of a venue where people flow is controlled by a people flow control system. The venue 1000 includes, for example, one or more facilities 1001. Examples of the facility 1001 include structures visited by people in the venue 1000, such as pavilions, amusement facilities, and restrooms, as well as non-structures (e.g., plazas) visited by visitors to the venue 1000, such as entrance and exit gates and event venues.

[0011] One or more sensors 400 and one or more cameras 500 are installed in the venue 1000. The sensors 400 include, for example, a sensor that detects people within the venue 1000, such as a motion sensor that uses infrared light, as well as sensors that measure the environment, such as temperature, humidity, and illuminance. The camera 500 captures images within the venue 1000, and the images identify the positions and number of visitors and devices 600 placed within the venue. From the information acquired by the motion sensor and camera 500 (camera 500 can also be considered an example of a sensor), it is possible to identify the number of people in each area within the venue 1000, the speed at which people are moving within the venue 1000, and the direction of people within the venue 1000. In other words, the motion sensor and camera 500 can acquire values ​​indicating people flow, which will be described later.

[0012] One or more devices 600 are also placed in the venue 1000. The devices 600 include partition poles, fences, benches, snack bars, signage, a screen for projecting projection mapping, and sunshades.

[0013] For example, when partition poles or fences are installed, it becomes impossible to move across them, thereby restricting the direction in which visitors can move and reducing congestion in areas or facilities 1001 located in directions where movement is not possible.

[0014] Furthermore, for example, when benches, shops, signage, and background objects are placed, people will stop by these, which will attract visitors in these directions and reduce congestion in areas and facilities 1001 where these objects are not installed.

[0015] Furthermore, for example, if a sunshade is placed in a position that blocks direct sunlight when the temperature and illuminance are high, people seeking shade will move toward the sunshade, which controls the direction in which visitors move and reduces congestion in areas where sunshades are not installed and in facility 1001. Also, installing sunshades can help prevent visitors from suffering from heatstroke. In this way, placing a sunshade can lower the temperature and illuminance (change the environment in the area where the sunshade is installed).

[0016] The benches are also used by visitors to rest, the shops are also used by visitors to shop, the screens are also used to create the atmosphere of the venue 1000, and the sunshades are also used to prevent heatstroke. In other words, the device 600 includes devices that have other uses in addition to controlling people flow and congestion. By using such device 600 that also has other uses to control people flow and congestion, it is possible to control people flow and congestion without visitors being aware that control of people flow and congestion is being performed (i.e., without causing discomfort to visitors).

[0017] The location of each device 600 can be changed, and the location of the device 600 is determined by a people flow control server (described later). That is, the people flow control server controls the flow of people and areas where people are congested. The device 600 may also be equipped with a control mechanism such as wheels or a motor, and the device 600 may automatically move to the location indicated by the location determined by the people flow control server.

[0018] One or more markers 700 and one or more beacons 800 may also be installed in the venue 1000. The markers 700 are, for example, reference points for identifying the positions of the devices 600. The beacons 800 are, for example, used to identify the current positions of some or all of the devices 600.

[0019] Note that some or all of the devices 600 may be equipped with sensors 400 and / or cameras 500, and by changing the location of the devices 600 equipped with the sensors 400 and / or 500, it is possible to change the detection range of the sensors 400 and / or cameras 500. Also, some or all of the devices 600 may be equipped with beacons 800. The location of the devices 600 equipped with the beacons 800 can be identified by the beacons 800.

[0020] 2 is a block diagram showing an example of the configuration of a people flow control system. The people flow control system includes, for example, a people flow control server 100, one or more sensors 400, one or more cameras 500, one or more devices 600, one or more markers 700, one or more beacons 800, an entrance / exit gate management system 910, a ticket reservation / sales management system 920, and an event schedule management system 930.

[0021] The people flow control server 100 executes processing to control the flow of people within the venue 1000 using information received from other systems, sensors 400, cameras 500, etc., as well as preset information.

[0022] The people flow control server 100 includes, for example, a people flow control calculation unit 110, a sensor data processing unit 120, a camera data processing unit 130, a marker / beacon data processing unit 140, and a device control unit 150, all of which are functional units.

[0023] The people flow control calculation unit 110 calculates the predicted people flow and predicted congestion level within the venue 1000, as well as the actual people flow and congestion level within the venue 1000. The people flow control calculation unit 110 also optimizes the placement and orientation of the device 600 and the processing performed by the device 600. The people flow control calculation unit 110 also detects any abnormalities that occur within the venue 1000.

[0024] The sensor data processing unit 120, the camera data processing unit 130, and the marker / beacon data processing unit 140 process the data received from the sensor 400, the data received from the camera 500, and the data received from the beacon 800, respectively. Specifically, for example, the sensor data processing unit 120, the camera data processing unit 130, and the marker / beacon data processing unit 140 perform processing such as adding a sequential number header so that data can be processed collectively for each installation area of ​​the sensor 400, the camera 500, and the beacon 800, and separating files to be recorded according to the date the data was received. The device control unit 150 controls the device 600 in accordance with the placement, orientation, processing, etc. of the device 600 determined by the people flow control calculation unit 110.

[0025] The people flow control server 100 also stores, for example, venue space information 161, sensor and camera placement information 162, marker and beacon placement information 163, sensor and camera data 164, entrance and exit information 165, ticket reservation information 166, event schedule information 167, area information 168, device control information 169, and people flow information 170. These pieces of information will be described in detail later.

[0026] The entrance / exit gate management system 910 transmits information regarding the number of people entering and exiting the venue 1000 to the people flow control server 100. The ticket reservation / sales management system 920 transmits information regarding the number of reservations for admission tickets to the venue 1000 to the people flow control server 100. The event schedule management system 930 transmits information regarding events taking place within the venue 1000 to the people flow control server 100.

[0027] 3 is a block diagram showing the hardware configuration of the people flow control server 100. The people flow control server 100 is configured by a computer having, for example, a CPU (Central Processing Unit) 1, a memory 2, an auxiliary storage device 3, a communication I / F (Interface) 4, an input I / F 5, and an output I / F 6.

[0028] CPU 1 includes a processor and executes programs stored in memory 2. Memory 2 includes ROM (Read Only Memory), which is a non-volatile storage element, and RAM (Random Access Memory), which is a volatile storage element. ROM stores unchanging programs (e.g., BIOS (Basic Input / Output System)). RAM is a high-speed, volatile storage element such as DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by CPU 1 and data used when the programs are executed.

[0029] The auxiliary storage device 3 is a large-capacity, non-volatile storage device such as a magnetic storage device (HDD (Hard Disk Drive)) or a flash memory (SSD (Solid State Drive)), and stores programs to be executed by the CPU 1 and data to be used when the programs are executed. That is, the programs are read from the auxiliary storage device 3, loaded into the memory 2, and executed by the CPU 1.

[0030] The input I / F 5 is an interface device to which devices that receive input from an operator, such as a keyboard 7 and a mouse 8, are connected. The output I / F 6 is an interface device to which devices that output the results of program execution in a format that can be viewed by an operator, such as a display 9 and a printer, are connected.

[0031] The communication I / F 4 is a network interface device that controls communication with other devices in accordance with a predetermined protocol, and may include a serial interface such as a USB (Universal Serial Bus).

[0032] Some or all of the programs executed by the CPU 1 may be provided to the people flow control server 100 via a network from a removable medium (CD-ROM, flash memory, etc.) which is a non-transitory storage medium, or from an external computer equipped with a non-transitory storage device, and stored in a non-volatile auxiliary storage device 3 which is a non-transitory storage medium. For this reason, the people flow control server 100 should preferably have an interface for reading data from removable media.

[0033] The people flow control server 100 is a computer system that is configured on a single physical computer or on multiple logically or physically configured computers, and may operate in separate threads on the same computer, or on a virtual computer built on multiple physical computer resources.

[0034] The CPU 1 includes, for example, a people flow control calculation unit 110, a sensor data processing unit 120, a camera data processing unit 130, a marker / beacon data processing unit 140, and a device control unit 150.

[0035] For example, CPU1 functions as a people flow control calculation unit 110 by operating in accordance with a people flow control calculation program loaded into memory 2, and functions as a sensor data processing unit 120 by operating in accordance with a sensor data processing program loaded into memory 2. The same relationship between programs and functional units applies to other functional units included in CPU1.

[0036] Note that some or all of the functions of the functional units included in the CPU 1 may be realized by hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array).

[0037] The auxiliary storage device 3, for example, the people flow control server 100, stores, for example, venue space information 161, sensor / camera placement information 162, marker / beacon placement information 163, sensor / camera data 164, entrance / exit information 165, ticket reservation information 166, event schedule information 167, area information 168, device control information 169, and people flow information 170.

[0038] In addition, some or all of the information stored in the auxiliary storage device 3 may be stored in the memory 2, or in another system or database connected to the people flow control server 100.

[0039] In this embodiment, the information used by the people flow control system does not depend on the data structure and may be expressed in any data structure. In this embodiment, the information is expressed in a table format, but the information can be stored in any data structure appropriately selected from, for example, a list, a database, or a queue.

[0040] The venue space information 161 is information that is set in advance by an administrator of the people flow control server 100, and is information (e.g., CAD data, map data, etc.) that indicates a layout diagram of the venue 1000 or a map of the venue 1000. Therefore, the venue space information 161 also indicates the area in which the facilities 1001 within the venue 1000 are located.

[0041] 4 is a diagram showing an example of the data configuration of the sensor / camera arrangement information 162. The sensor / camera arrangement information 162 is information that is set in advance by an administrator of the people flow control server 100, etc., and is information about the sensors 400 and cameras 500 arranged within the venue 1000. The sensor / camera arrangement information 162 indicates, for example, the ID, type, position, and detection range of the sensors 400 and cameras 500 arranged within the venue 1000. For example, if the type is camera 500, information indicating the captureable range (e.g., angle of view, etc.) is stored as the detection range of the sensor / camera arrangement information 162.

[0042] 5 is a diagram showing an example of the data configuration of the marker / beacon placement information 163. The marker / beacon placement information 163 is information that is set in advance by an administrator of the people flow control server 100 or the like, and is information about the markers 700 and beacons 800 placed within the venue 1000.

[0043] The marker / beacon placement information 163 indicates, for example, the IDs and types of the markers 700 and beacons 800 placed within the venue 1000. The marker / beacon placement information 163 also holds a flag indicating whether the markers 700 and beacons 800 are fixed to a specific location or attached to a mobile device 600. For fixed markers 700 and beacons 800, the marker / beacon placement information 163 indicates the fixed location, and for markers 700 and beacons 800 attached to a mobile device 600, the marker / beacon placement information 163 indicates the ID of the device 600.

[0044] 6 is a diagram showing an example of the data configuration of the sensor / camera data 164. The sensor / camera data 164 indicates data acquired by the sensor 400 and the camera 500 after being processed by the sensor data processing unit 120 and the camera data processing unit 130. The sensor / camera data 164 indicates, for example, the ID and type of the sensor 400 and the camera 500, as well as the detection time and detection result by the sensor 400 and the camera 500 (for example, a measurement value in the case of a temperature sensor, or an image in the case of the camera 500).

[0045] 7 shows an example of the data configuration of the entry / exit information 165. The entry / exit information 165 is information that the people flow control server 100 receives from the entry / exit gate management system 910, and is information relating to entry into and exit from the venue 1000. The entry / exit information 165 includes, for example, gate setting information 1651 and entry / exit information 1652 for each gate (in FIG. 7, only the entry / exit information 1652 for the gate with gate ID "001" is shown, and the entry / exit information 1652 for the other gates is not shown).

[0046] Gate setting information 1651 holds a gate ID that identifies the gate and information indicating whether the gate is an entrance gate or an exit gate. Entry / exit information 1652 holds information indicating the number of people for each time period (unit time) (the number of people entering if the gate is an entrance gate, and the number of people exiting if the gate is an exit gate). Note that a gate may serve as both an entrance gate and an exit gate. In this case, the entry / exit information 1652 corresponding to that gate stores information indicating the number of people entering and exiting the gate for each time period. From the entry / exit information 165, it is possible to identify the number of people entering the venue 1000, the number of people exiting the venue 1000, and the number of people currently staying at the venue 1000.

[0047] 8 is a diagram showing an example of the data configuration of the ticket reservation information 166. The ticket reservation information 166 is information that the people flow control server 100 receives from the ticket reservation and sales management system 920, and indicates the sales record of advance admission tickets to the venue 1000. The ticket reservation information 166 indicates, for example, the ticket ID and the time period during which a person can stay at the venue 1000 with that ticket (i.e., the start date and time of the stay period and the end date and time of the stay period).

[0048] 9 is a diagram showing an example of the data configuration of the event schedule information 167. The event schedule information 167 is information that is set in advance by an administrator of the people flow control server 100 or the like, and is information related to events that will be held in the facility 1001. The event schedule information 167 indicates, for example, the ID and type of the event, the date and time (time period) when the event is held, and the average number of attendees (actual results) when the event is held.

[0049] 10 is a diagram showing an example of the data configuration of the area information 168. The area information 168 is information that is set in advance by an administrator of the people flow control server 100 or the like, and is information about the multiple areas into which the venue 1000 is divided. The area information 168 indicates, for example, the area ID, the area of ​​the area, and the facility IDs of the facilities included in the area. For example, if the area is rectangular, the area of ​​the area in the area information 168 is defined by two diagonal vertices.

[0050] 11 is a diagram showing an example of the data configuration of the device control information 169. The device control information 169 indicates, for example, the ID of the device, the type of the device 600, the position of the device 600, the orientation of the device, and other processing by the device 600. Of the device control information 169, the position, orientation, and other processing of the device 600 are information generated by the people flow control calculation unit 110, and the ID and type of the device are information set in advance by an administrator of the people flow control server 100, etc.

[0051] For example, the orientation of device 600 is indicated by the angle from a predetermined reference line. For example, if device 600 is a screen onto which projection mapping is projected, the content displayed on the screen is indicated as the content of other processing by device 600. For device 600 that is simply placed (such as a bench) (and does not perform other processing), the content of other processing does not need to be indicated.

[0052] 12 is a diagram showing an example of the data configuration of the people flow information 170. The people flow information 170 is information generated by the people flow control calculation unit 110. The people flow information 170 indicates, for example, the number of people entering, the area from which people enter, the number of people leaving, the area to which people leave, and the number of people staying in each area for each time period.

[0053] Note that "inflow" refers to, for example, a person who was not in the target area at the start of a time period entering the target area at some point during that time period. "outflow" refers to, for example, a person who was in the target area at some point during that time period but is no longer in the target area at the end of that time period. "Passing" is a concept that includes "inflow" and "outflow." "Staying" refers, for example, to a person being in the target area at both the start and end of the time period. "People flow" is a concept that includes "inflow" and "outflow."

[0054] 13 is a block diagram showing an example of the functional configuration of the people flow control calculation unit 110. The people flow control calculation unit 110 includes, for example, a people flow / congestion prediction simulation unit 111, an equipment placement optimization simulation unit 112, an equipment placement determination unit 113, a people flow / congestion visualization unit 114, an equipment position visualization unit 115, and an abnormality detection unit 116.

[0055] The people flow / congestion prediction simulation unit 111 predicts the degree of congestion in each area for each time period by referring to entrance / exit information 165, ticket reservation information 166, event schedule information 167, and area information 168. The entrance / exit information 165 and ticket reservation information 166 are information relating to the number of people entering the venue 1000 and the maximum number of people who can enter, and the event schedule information 167 is information relating to events that will increase the number of people staying in each area.

[0056] Specifically, for example, a people flow prediction model is pre-stored in the auxiliary storage device 3. When the number of visitors to the venue 1000 in a given time period obtained from entrance / exit information 165, the maximum number of visitors to the venue 1000 in a given time period indicated by ticket reservation information 166, the type of event to be held in a given area in a given time period indicated by event schedule information 167 and area information 168 (or whether or not an event is held, the number of events, etc.), the start time of the event, the end time of the event, the average number of attendees for the event, etc. are input into the people flow prediction model, a predicted people flow in the area in a given time period is output.

[0057] Note that the input variables of the people flow prediction model when predicting the people flow in a certain area after a certain time period may further include the people flow (the number of people entering and the area from which they enter, the number of people leaving and the area to which they exit, and the number of people remaining) in the time period immediately preceding the time period in the area generated by the people flow / congestion visualization unit 114. Also, the people flow prediction model may be defined for each area.

[0058] The predicted pedestrian flow in the area during a certain time period indicates the number of people staying in the area during that time period, the number of people entering the area during that time period and the area from which they are entering, and the number of people leaving the area during that time period and the area to which they are leaving.

[0059] The people flow / congestion prediction simulation unit 111 calculates the predicted congestion level for each area in each time period based on the predicted people flow for each area in each time period. The congestion level is defined, for example, by the weighted sum of the number of people staying, the number of people flowing in from adjacent areas, and the number of people flowing out to adjacent areas. In other words, the congestion level increases as the number of people staying and the number of people flowing in increase, and decreases as the number of people flowing out increase.

[0060] In addition, only the number of people staying may be taken into consideration when calculating the congestion level (i.e., all weights other than the weight applied to the number of people staying may be zero). Also, in calculating the congestion level, the weight applied to the number of people flowing into each adjacent area may include a negative value.

[0061] In calculating the congestion degree, the weight applied to the inflow number of people may be defined for each adjacent area or may be the same for all areas, and the weight applied to the outflow number of people to each adjacent area may be defined for each adjacent area or may be the same for all areas. Furthermore, in calculating the congestion degree, the number of people staying, the number of people entering, and the number of people leaving per unit area of ​​the area may be used instead of the number of people staying, the number of people entering, and the number of people leaving. The people flow prediction model may output both the predicted people flow and the predicted congestion degree, in which case it is not necessary to calculate the predicted congestion degree according to the above definition.

[0062] The people flow / congestion prediction simulation unit 111 outputs the predicted people flow for each time period in each area to the equipment placement optimization simulation unit 112. In addition, the people flow / congestion prediction simulation unit 111 outputs the predicted people flow and predicted congestion degree for each time period in each area to the anomaly detection unit 116.

[0063] The equipment placement optimization simulation unit 112 executes an optimization simulation of the placement of the device 600 based on the predicted people flow in each time period in each area. Specifically, for example, an equipment placement optimization model is pre-stored in the auxiliary storage device 3. When the predicted people flow in the area in the time period (the number of people staying in the area in the time period, the number of people flowing into the area in the time period and the area from which they flow in, and the number of people flowing out of the area in the time period and the area to which they flow in) is input to the equipment placement optimization model, the optimal placement, orientation, and other processing contents of the device 600 are output as simulation results.

[0064] The equipment placement optimization simulation unit 112 outputs the simulation results to the equipment placement determination unit 113. The equipment placement determination unit 113 determines the placement, orientation, and other processing contents of the equipment 600 according to the input simulation results and / or a venue mode, which will be described later, and stores the determined contents in the equipment control information 169. The equipment placement determination unit 113 also instructs each of the equipment 600 about the determined placement, orientation, and other processing contents of the equipment 600.

[0065] The people flow / congestion visualization unit 114 calculates the number of people entering and their origin areas, the number of people leaving and their destination areas, and the number of people staying in each area indicated by the area information 168, during the current time period, based on the values ​​of the human presence sensor included in the sensor / camera data 164 and the images captured by the camera 500 (the number of people, their direction, and their movement speed indicated by the values ​​of the human presence sensor and the images captured by the camera 500), as well as the positions and detection ranges of the human presence sensors and the cameras 500 indicated by the sensor / camera placement information 162. Furthermore, the people flow / congestion visualization unit 114 calculates the degree of congestion during the current time period for each area, based on the number of people entering and their origin areas, the number of people leaving and their destination areas, and the number of people staying in each area during the current time period.

[0066] The people flow / congestion visualization unit 114 stores the calculated information in people flow information 170, and further outputs the people flow and congestion level for the current time period in each area to the anomaly detection unit 116. The people flow / congestion visualization unit 114 also generates data for displaying a screen that visualizes the calculated people flow and / or congestion level, and displays it on the display 9 or the like. The people flow / congestion visualization unit 114 also outputs values ​​of sensors 400 that measure the environment, such as temperature, humidity, and illuminance, included in the sensor / camera data 164 in each area indicated by the area information 168 to the anomaly detection unit 116. Details of the processing by the people flow / congestion visualization unit 114 will be described later using FIG. 14.

[0067] The device position visualization unit 115 calculates the position of the device 600 based on the image captured by the camera 500 included in the sensor / camera data 164, the position and detection range of the camera 500 indicated in the sensor / camera arrangement information 162, and the positions of the markers and beacons included in the marker / beacon arrangement information 163, and outputs the calculated information to the abnormality detection unit 116. The device position visualization unit 115 also generates data for displaying a screen that visualizes the position of the device 600, and displays it on the display 9 or the like. Details of the processing by the device position visualization unit 115 will be described later using FIG. 15.

[0068] The anomaly detection unit 116 compares, for each area, the congestion degree for the current time period output from the people flow / congestion visualization unit 114 with the predicted congestion degree for the current time period output from the people flow / congestion prediction simulation unit 111. The anomaly detection unit 116 determines that an anomaly has occurred in an area where, for example, the difference between the predicted congestion degree for the current time period output from the people flow / congestion prediction simulation unit 111 and the congestion degree for the current time period output from the people flow / congestion visualization unit 114 is equal to or greater than a first predetermined value (an area where the actual congestion degree is significantly higher than the predicted congestion degree). Alternatively, the anomaly detection unit 116 may determine that an anomaly has occurred in an area where, for example, the congestion degree for the current time period output from the people flow / congestion visualization unit 114 is equal to or greater than a second predetermined value (an area where the actual congestion degree is extremely high).

[0069] For example, when the device 600 blocks a passage from area A to area B, the predicted congestion level of area A may be significantly higher than the actual congestion level of area A, or the actual congestion level of area A may be extremely high, due to the location of the device 600. In this way, when the anomaly detection unit 116 receives information from the device location visualization unit 115 indicating that a specific device 600 is located at a specific location, it may determine that no anomaly has occurred even if the predicted congestion level of an area (area A in this example) previously associated with the specific location is significantly higher than the actual congestion level of the area, or the actual congestion level of the area is extremely high.

[0070] When the anomaly detection unit 116 determines that there is an area where the predicted congestion level and the actual congestion level deviate by a certain amount or more, it updates the parameters of the people flow prediction model so that the predicted congestion level approaches the actual congestion level. For example, the anomaly detection unit 116 determines that an area where the predicted congestion level and the actual congestion level deviate by a certain amount or more is an area where the difference between the predicted congestion level for the current time period output from the people flow / congestion prediction simulation unit 111 and the congestion level for the current time period output from the people flow / congestion visualization unit 114 is greater than or equal to a third predetermined value and less than or equal to a fourth predetermined value (the fourth predetermined value is less than or equal to a first predetermined value). In other words, the difference between the predicted congestion level and the actual congestion level when the parameters of the people flow prediction model are updated is smaller than the difference between the predicted congestion level and the actual congestion level when it is determined that an abnormality has occurred (it is not determined that an abnormality has occurred when the parameters are updated). Furthermore, when the anomaly detection unit 116 determines that there is an area (an area where the actual congestion level is higher than a certain level) where the congestion level for the current time period output from the people flow / congestion visualization unit 114 is equal to or greater than a fifth predetermined value and equal to or less than a sixth predetermined value (the sixth predetermined value is equal to or less than a second predetermined value), the anomaly detection unit 116 may update the parameters of the people flow prediction model so that the predicted congestion level approaches the actual congestion level. In other words, the actual congestion level when the parameters of the people flow prediction model are updated is smaller than the actual congestion level when it is determined that an abnormality has occurred (it is not determined that an abnormality has occurred when the parameters are updated). Details of the people flow / congestion prediction simulation process will be described later using FIG. 16.

[0071] When the abnormality detection unit 116 determines that an abnormality has occurred in an area where the actual congestion level is significantly higher than the predicted congestion level, it outputs an abnormality alert including the flow of people in each area during the current time period (or the flow of people only in the area where the abnormality has occurred) to the equipment placement optimization simulation unit 112.

[0072] The equipment placement optimization simulation unit 112 generates a simulation result according to the people flow indicated by the abnormality alert and the equipment placement optimization model, and outputs the simulation result to the equipment placement determination unit 113. The equipment placement determination unit 113 determines the placement, orientation, and other processing contents of the equipment 600 in the event of an abnormality according to the simulation result, and stores the determined placement, orientation, and other processing contents of the equipment 600 in the event of an abnormality in the equipment control information 169. The equipment placement determination unit 113 also instructs each equipment 600 on the determined placement, orientation, and other processing contents of the equipment 600 in the event of an abnormality.

[0073] Furthermore, the abnormality detection unit 116 determines that an abnormality has occurred in an area where it has determined that the values ​​of the sensor 400 that measures the environment, such as temperature, humidity, and illuminance, output from the people flow / congestion visualization unit 114 are outside a predetermined range (for example, an area where the temperature is 30 degrees or higher and the humidity is 70% or higher).

[0074] When the anomaly detection unit 116 determines that an anomaly exists in an area where the values ​​of the sensor 400 that measures the environment such as temperature, humidity, and illuminance output from the people flow / congestion visualization unit 114 are outside a predetermined range, the anomaly detection unit 116 outputs an anomaly alert indicating the anomaly to the equipment placement optimization simulation unit 112.

[0075] The equipment placement optimization simulation unit 112, which has received the abnormality alert indicating the abnormality, determines the placement, orientation, and other processing details of the equipment 600 to deal with the abnormality (for example, placing a sunshade in an area where the temperature is 30 degrees or higher and the humidity is 70% or higher), and stores the results in the equipment control information 169. The equipment placement determination unit 113 also instructs each equipment 600 on the placement, orientation, and other processing details of the equipment 600 in the determined abnormality. Details of the processing by the equipment placement determination unit 113 will be described later using FIG. 18.

[0076] 14 is a flowchart showing an example of the people flow / congestion visualization process. The people flow / congestion visualization unit 114 determines whether at least one of the human sensor value included in the sensor / camera data 164 and the image captured by the camera 500 has been updated (S1501).

[0077] If the people flow / congestion visualization unit 114 determines that at least one of them has been updated (S1501: Yes), it reads the values ​​of the human presence sensors and the images captured by the cameras 500 for the updated time period included in the sensor / camera data 164 (S1502). Also, in step S1502, the people flow / congestion visualization unit 114 reads the positions and detection ranges of the human presence sensors and the cameras 500 indicated by the sensor / camera placement information 162, and each area indicated by the area information 168.

[0078] The people flow / congestion visualization unit 114 extracts the movement of people in the relevant time period from the values ​​of the human presence sensor and the images captured by the camera 500 included in the sensor / camera data 164 read in step S1502, and the positions and detection ranges of the human presence sensor and the camera 500 indicated by the sensor / camera placement information 162 (S1503).From the extracted movement of people, the people flow / congestion visualization unit 114 calculates the number of people entering and the area from which they entered, the number of people leaving and the area to which they entered, and the number of people staying in the current time period for each area indicated by the area information 168, and stores these in the people flow information 170 (S1504).

[0079] The people flow / congestion visualization unit 114 calculates the congestion level for each area in the current time period from the number of people entering each area and the area from which they enter, the number of people leaving each area and the area to which they leave, and the number of people staying in each area in the current time period, generates visualization information for visualizing the congestion level by area (S1505), and outputs the visualization information to the display 9 (S1506). In the visualization information displayed on the display 9 in step S1506, for example, the congestion level may be shown numerically on each area displayed on a map, or a heat map of the congestion level may be shown on each area displayed on a map.

[0080] The people flow / congestion visualization unit 114 determines whether an instruction to end the people flow / congestion visualization process has been received (S1507). The instruction to end may be input by an administrator of the people flow control server 100 using an input device such as the keyboard 7 or the mouse 8, or may be issued at a predetermined time.

[0081] If the people flow / congestion visualization unit 114 determines that it has received an instruction to end the people flow / congestion visualization process (S1507: Yes), it saves the visualization summary (for example, the log of the visualization information output in step S1506) in the auxiliary storage device 3 (S1508) and ends the people flow / congestion visualization process. If the people flow / congestion visualization unit 114 determines that it has not received an instruction to end the people flow / congestion visualization process (S1507: No), it returns to step S1501.

[0082] If the people flow / congestion visualization unit 114 determines that both the human sensor value included in the sensor / camera data 164 and the image captured by the camera 500 have not been updated (S1501: No), it transitions to step S1507.

[0083] 15 is a flowchart showing an example of the device position visualization process. The device position visualization unit 115 determines whether the image captured by the camera 500 included in the sensor camera data 164 has been updated (S1601).

[0084] If the device position visualization unit 115 determines that the image has been updated (S1601: Yes), it reads the image captured by the camera 500 included in the sensor / camera data 164 during the updated time period (S1602). Also, in step S1602, the device position visualization unit 115 reads the position and detection range of the camera 500 indicated by the sensor / camera arrangement information 162.

[0085] The device position visualization unit 115 detects the position of each device 600 placed in the venue 1000 during the relevant time period based on the image taken by the camera 500 included in the sensor / camera data 164 read in step S1602 and the position and detection range of the camera 500 indicated by the sensor / camera placement information 162 (S1603).

[0086] The device position visualization unit 115 detects the orientation of each device 600 based on the image captured by the camera 500 included in the sensor / camera data 164 read in step S1602 and the position and detection range of the camera 500 indicated by the sensor / camera placement information 162 (S1604).

[0087] The device position visualization unit 115 generates visualization information for visualizing the position of each device 600 detected in step S1603 and the orientation of each device 600 detected in step S1604 (S1605), and outputs the visualization information to the display 9 (S1606). In the visualization information displayed on the display 9 in step S1606, the position and orientation of each device 600 are drawn on a map, for example.

[0088] The device location visualization unit 115 determines whether an instruction to end the device location visualization process has been received (S1607). The instruction to end the process may be input by an administrator of the people flow control server 100 using an input device such as the keyboard 7 or the mouse 8, or may be issued at a predetermined time.

[0089] If the device location visualization unit 115 determines that it has received an instruction to end the device location visualization process (S1607: Yes), it saves the visualization summary (for example, a log of the visualization information output in step S1606) in the auxiliary storage device 3 (S1608) and ends the device location visualization process. If the device location visualization unit 115 determines that it has not received an instruction to end the device location visualization process (S1607: No), it returns to step S1601.

[0090] If the device position visualization unit 115 determines that the image captured by the camera 500 included in the sensor camera data 164 has not been updated (S1601: No), the process proceeds to step S1607.

[0091] FIG. 16 is a flowchart showing an example of the people flow and congestion prediction simulation process. The people flow and congestion prediction simulation unit 111 sets initial parameters for the people flow prediction model (S1701). For example, a social force model is used as the people flow prediction model. The initial parameters are determined, for example, by input from the administrator of the people flow control server 100 to an input device such as the keyboard 7 or mouse 8.

[0092] The people flow / congestion prediction simulation unit 111 determines whether the time to update the people flow prediction value has arrived (for example, the update time arrives at every predetermined unit time) (S1702). If the people flow / congestion prediction simulation unit 111 determines that the time to update the people flow prediction value has not arrived (S1702: No), the process proceeds to step S1706, which will be described later.

[0093] When the people flow / congestion prediction simulation unit 111 determines that the time has come to update the people flow prediction value (S1702: Yes), the anomaly detection unit 116 determines whether to update the parameters of the people flow prediction model (S1703). Specifically, as described above, when the anomaly detection unit 116 determines that there is an area where the predicted congestion level and the actual congestion level deviate by more than a certain amount, or when the anomaly detection unit 116 determines that there is an area where the actual congestion level is higher than a certain amount, the anomaly detection unit 116 determines to update the parameters of the people flow prediction model, and when there is no such area, the anomaly detection unit 116 determines not to update the parameters of the people flow prediction model.

[0094] If the anomaly detection unit 116 determines that the parameters of the people flow prediction model should be updated (S1703: Yes), it updates the parameters of the people flow prediction model using the method described above (S1704).If the anomaly detection unit 116 determines that the parameters of the people flow prediction model should not be updated (S1703: No), it proceeds to step S1705.

[0095] The people flow / congestion prediction simulation unit 111 inputs the people flow (the number of people entering and the area from which they enter, the number of people leaving and the area to which they leave, and the number of people staying) for the immediately preceding time period generated by the people flow / congestion visualization unit 114 and external information (for example, the number of visitors to the venue 1000 for the relevant time period obtained from the entrance / exit information 165, the maximum number of visitors to the venue 1000 for the relevant time period indicated by the ticket reservation information 166, the type of event to be held in the relevant area for the relevant time period indicated by the event schedule information 167 and the area information 168 (or whether or not there is an event, the number of events, etc.), the start time of the event, the end time of the event, and the average number of attendees for the event, etc.) into a people flow prediction model, and predicts people flow (S1705).

[0096] As described above, the predicted people flow and the predicted congestion degree calculated from the predicted people flow are output to the anomaly detection unit 116. Meanwhile, it is desirable that the people flow / congestion prediction simulation unit 111 does not output the predicted people flow to the equipment placement optimization simulation unit 112 until the estimation performance of the people flow prediction model improves (for example, until it is first determined in step S1703 that the parameters will not be updated), and after it is determined that the estimation performance of the people flow prediction model has improved, it outputs the predicted people flow to the equipment placement optimization simulation unit 112 and executes optimization indicated by the simulation results by the equipment placement optimization simulation unit 112. In other words, after the estimation performance of the people flow prediction model improves, it is determined whether an anomaly has occurred based on the predicted congestion degree and the actual congestion degree that also takes into account the effect of optimization indicated by the simulation results by the equipment placement optimization simulation unit 112.

[0097] The people flow / congestion prediction simulation unit 111 determines whether an instruction to end the people flow / congestion prediction simulation process has been received (S1706). The instruction to end the process is input, for example, by an administrator of the people flow control server 100 via an input device such as the keyboard 7 or the mouse 8.

[0098] If the people flow / congestion prediction simulation unit 111 determines that it has received an instruction to end the people flow / congestion prediction simulation process (S1706: Yes), it ends the people flow / congestion prediction simulation process. If the people flow / congestion prediction simulation unit 111 determines that it has not received an instruction to end the people flow / congestion prediction simulation process (S1706: No), it returns to step S1702.

[0099] 17 is a flowchart showing an example of the device placement determination process. The device placement determination unit 113 determines the current venue mode (S1801). The venue mode includes, for example, opening hours, during business hours, closing hours, outside business hours, and when an emergency occurs. The venue mode is determined, for example, by input from an input device such as the keyboard 7 or mouse 8 by the administrator of the people flow control server 100. Furthermore, for example, the venue modes of opening hours, during business hours, closing hours, and outside business hours may be determined by the device placement determination unit 113 according to predetermined business hours, and when an emergency occurs in the venue mode may be determined by input from the administrator of the people flow control server 100 by input from an input device such as the keyboard 7 or mouse 8.

[0100] When the device placement determination unit 113 determines that the current venue mode is the opening time (S1801: opening time), it determines to place each device 600 in a fixed position that makes it easier for people entering through the entrance / exit of the venue 1000 to proceed to the back of the venue 1000 (for example, place the device 600 so that the width of a predetermined passageway leading from the entrance of the venue 1000 to the back (for example, the direction of travel in the passageway is determined by indicating the direction from the entrance to the back with an arrow or the like displayed on a screen, which is an example of the device 600) is wider than the width of a predetermined passageway leading from the back to the entrance (for example, the direction of travel in the passageway is determined by indicating the direction from the back to the entrance with an arrow or the like displayed on a screen, which is an example of the device 600) (S1802).

[0101] If the device placement determination unit 113 determines that the current venue mode is within business hours (S1801: within business hours), it determines the placement, orientation, and other processing contents of each device 600 in accordance with instructions from the device placement optimization simulation unit 112 (S1803).

[0102] When the device placement determination unit 113 determines that the current venue mode is closing time (S1801: closing time), it determines to place each device 600 in a fixed position that makes it easy for people at the back of the venue 1000 to proceed to the entrance / exit (for example, place the devices 600 so that the width of a specified passage from the back of the venue 1000 to the entrance is wider than the width of a specified passage from the entrance to the back) (S1804).

[0103] When the device placement determination unit 113 determines that the current venue mode is outside business hours (S1801: outside business hours), it determines to place each device 600 in a position where the cleaning robot can easily clean (for example, by placing each device 600 in a predetermined position that avoids the predetermined cleaning range of the cleaning robot, or by folding the movable part of a device 600 such as an extendable pole to avoid collision with the cleaning robot) (S1805). Specifically, for example, by placing each device 600 close to one side of a predetermined passage in the venue 1000, it becomes easier for the cleaning robot to clean, which in turn increases the efficiency of the cleaning work and shortens the time required for cleaning.

[0104] When the device placement determination unit 113 determines that the current venue mode is in an emergency state (S1801: emergency state), it determines to place each device 600 in a fixed position along a wall within the venue 1000 (for example, along the outer wall of the venue 1000 or along the outer wall of the facility (the positions of the outer wall of the venue 1000 or the outer wall of the facility are specified by the venue space information 161)) for emergency vehicles and evacuation (S1806). Examples of emergency situations include the occurrence of a fire or terrorist attack. In such cases, moving the devices 600 closer to the wall makes it easier for people and emergency vehicles to move.

[0105] In addition, the device placement determination unit 113 may place the device 600 in a position to guide people away from the location where an incident occurs in an emergency (the location where an incident occurs is specified, for example, in step S1801 together with the specification of the venue mode) (for example, by placing a partition pole or fence to restrict people from moving toward the location where the incident occurred), or in the event of an earthquake occurring as an emergency, may place the device 600 to guide people to a location where they can avoid the device 600 tipping over or objects falling from the facility 1001 (for example, by placing a partition pole or fence to restrict people from moving around a specified device 600 or around a specified facility 1001).

[0106] The equipment placement determination unit 113 determines whether an instruction to end the equipment placement determination process has been received (S1807). The instruction to end the process may be input by an administrator of the people flow control server 100 using an input device such as the keyboard 7 or the mouse 8, or may be issued at a predetermined time.

[0107] If the equipment placement determination unit 113 determines that it has received an instruction to end the equipment placement determination process (S1807: Yes), it ends the equipment placement determination process. Note that the equipment placement determination unit 113 may place each device 600 in a predetermined initial position before ending the equipment placement determination process, or may place each device 600 in a position at the start of business. If the equipment placement determination unit 113 determines that it has not received an instruction to end the equipment placement determination process (S1807: No), it returns to step S1801.

[0108] 18A, 18B, and 18C are explanatory diagrams showing examples of the placement of the device 600 optimized by the device placement optimization simulation unit 112. The examples of Figures 18A, 18B, and 18C explain examples in which the placement of the device 600 in a target area such as a passageway or a plaza is optimized.

[0109] 18A, the predicted number of people flowing into the area adjacent to the target area is large (for example, equal to or greater than a predetermined value), so the simulation results by the equipment placement optimization simulation unit 112 indicate that partitions 650 will be placed in passageways, plazas, etc. within the target area. By placing partitions 650 within the target area, the width of the passageways within the target area is narrowed, making it possible to control the flow of people into the adjacent area.

[0110] 18B, since the predicted number of people entering the area adjacent to the target area is large (for example, equal to or greater than a predetermined value), the equipment placement optimization simulation unit 112 outputs as a simulation result that benches 660 should be placed so that it is difficult for people to pass through the target area. Note that in the situations of FIGS. 18A and 18B, both partitions 650 (placement in FIG. 18A) and benches 660 (placement in FIG. 18B) may be placed.

[0111] In the example of Figure 18C, since the predicted number of people entering the area adjacent to the target area is large (for example, above a predetermined value), the equipment placement optimization simulation unit 112 outputs as a simulation result that bench 660 will be placed so as to make it easier for people to gather in the target area (for example, in an area including the center of the target area).

[0112] Although not shown in Figures 18A, 18B, and 18C, when the congestion level in a specific area (for example, an area including a facility 1001 such as an event venue) is high (for example, the predicted congestion level in the specific area and the actual congestion level differ by more than a certain amount, or the congestion level in the specific area is higher than a certain amount), the device placement optimization simulation unit 112 outputs as a simulation result a recommendation to control the device 600 to increase the number of people staying in other areas (i.e., to guide people to the other areas).

[0113] Specifically, for example, the device placement optimization simulation unit 112 outputs as simulation results the installation of a kiosk in the other area, increasing the volume of the speakers that play the voices calling out to the kiosk installed in the other area, extending the duration of an event being held in the other area (for example, placing speakers or screens in the event venue and outputting voices or displays instructing the event staff to extend the event duration, or extending the operating time of the device 600 installed in the event venue and used for the event), and / or placing in the specific area a screen that advertises the event being held in the other area, etc.

[0114] Furthermore, the equipment placement optimization simulation unit 112 may determine the movement speed of the device 600 when changing the position of the device 600 in the equipment placement optimization. Specifically, for example, the movement speed may also be output from the equipment placement optimization model, or the equipment placement optimization simulation unit 112 may determine the movement speed to a predetermined value. In particular, for example, if the movement speed is determined to be low (for example, a speed equal to or lower than a predetermined value, such as 1 km / h or less), it is possible to gradually control the flow of people without giving passersby a sense of oppression or fear.

[0115] Furthermore, the equipment layout optimization simulation unit 112 may determine a movement path of the device 600 when changing the position of the device 600 in the equipment layout optimization. Specifically, for example, the movement path is also output from the equipment layout optimization model. The equipment layout optimization simulation unit 112 determines the movement path of the device 600 so as not to block the path of people flowing in from an inflow source area where the predicted inflow number is large (for example, above a predetermined threshold) or the path of people flowing out to an outflow destination area where the predicted outflow number is large (for example, by determining the movement path of the device 600 so as to move the bench slowly (for example, at a speed equal to or less than a predetermined value) toward a wall (the position of the wall is specified by the venue space information 161) while facing parallel to the flow of people (for example, the average direction of people)). This reduces the possibility of collisions between people and the device 600 and enables natural people flow control.

[0116] 19 is a block diagram showing an example of the functional configuration of the device 600. The device 600 includes, for example, a communication unit 601, a device calculation unit 602, a sensor data processing unit 603, a position control unit 604, an orientation control unit 605, an other function control unit 606, a sensor communication unit 607, a drive unit 608, a drive unit 609, a drive unit 610, and one or more sensors 611.

[0117] The device 600 also includes one or more wheels 612 that are operated by driving unit 608, one or more movable units 613 (for determining the orientation of the device 600) that are operated by driving unit 609, and one or more other functional units 614 that are operated (or perform processing) by driving unit 610.

[0118] The communication unit 601 communicates with the people flow control server 100. The device calculation unit 602 outputs the position, orientation, and other processing details of the device 600 indicated by the instruction received from the device placement determination unit 113 of the people flow control server 100 to the position control unit 604, orientation control unit 605, and other function control unit 606, respectively.

[0119] The position control unit 604 drives the drive unit 608 to operate the wheels 612 so that the device 600 is positioned at the position output by the device calculation unit 602. The orientation control unit 605 drives the drive unit 609 to operate the movable unit 613 so that the device 600 faces the orientation output by the device calculation unit 602. The other function control unit 606 drives the drive unit 610 so that the other function unit 614 executes the processing indicated by the other processing content output by the device calculation unit 602.

[0120] The sensor communication unit 607 transmits the sensor data received from the sensor 611 to the sensor data processing unit 603. The sensor data processing unit 603 processes the received sensor data and outputs it to the device calculation unit 602. The device calculation unit 602 transmits the sensor data received via the communication unit 601 to the people flow control server 100.

[0121] 19 (i.e., device 600 that cannot operate autonomously). Also, venue 1000 may include device 600 that does not include sensor data processing unit 603, sensor communication unit 607, and sensor 611. Also, venue 1000 may include device 600 that does not autonomously execute at least one of position, orientation, and other processes.

[0122] As described above, the people flow control server 100 of this embodiment predicts the people flow and congestion level in the venue 1000 having multiple facilities 1001, etc., and optimizes the placement, orientation, and processing (e.g., display content) of the device 600 based on the prediction results. This makes it possible to suppress congestion and excessive people flow in specific areas within the venue 1000 without a guide, robot, etc. directly instructing or guiding passersby in the direction they should go within the venue 1000 using gestures, voice announcements, etc. (i.e., without making passersby aware that people flow control is being performed), and ultimately makes it possible to naturally maintain a state in which passersby can move safely and smoothly within the venue 1000.

[0123] Furthermore, when the people flow control server 100 of this embodiment determines that there is a discrepancy between the predicted congestion level and the actual congestion level, it updates the parameters of the people flow prediction model, thereby enabling re-optimization of the placement, orientation, and processing of the device 600 based on more accurate people flow predictions.

[0124] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0125] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0126] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0127] 1 CPU, 2 memory, 3 auxiliary storage device, 4 communication I / F, 5 input, 6 output I / F, 100 people flow control server, 110 people flow control calculation unit, 111 people flow and congestion prediction simulation unit, 112 equipment placement optimization simulation unit, 113 equipment placement determination unit, 114 people flow and congestion visualization unit, 115 equipment position visualization unit, 116 anomaly detection unit, 150 equipment control unit, 161 venue space information, 162 sensor and camera placement information, 163 marker and beacon placement information, 164 sensor and camera data, 165 entrance and exit information, 166 ticket reservation information, 167 event schedule information, 168 area information, 169 equipment control information, 170 people flow information

Claims

1. A system for controlling people flow in a venue including multiple areas, comprising: a server; at least one of a sensor and a camera that acquires values ​​indicating people flow that indicate the inflow and outflow of people and the accumulation of people between the plurality of areas; and a device that can be placed in any of the plurality of areas and controls the people flow, The server Number of people information indicating the number of people entering the venue; event information indicating events to be held in each of the plurality of areas; predicting the flow of people for each of the plurality of areas based on the number of people information and the event information; executing a device placement optimization process that determines at least one of the placement and orientation of the devices and the processes performed by the devices in the plurality of areas based on the predicted pedestrian flow and values ​​acquired by at least one of the sensors and the cameras; The system, wherein the device operates at a speed equal to or less than a predetermined value based on the result of the device placement optimization process.

2. 10. The system of claim 1, The server a people flow prediction model that outputs a predicted people flow when information including the number of people information and the event information is input; Calculating an actual flow of people in each of the plurality of areas based on values ​​acquired by at least one of the sensor and the camera; predicting a pedestrian flow for each of the plurality of areas based on the number of people information, the event information, and the pedestrian flow prediction model; predicting a congestion level in each of the plurality of areas based on the predicted pedestrian flow; calculating an actual congestion degree in each of the plurality of areas based on the calculated actual pedestrian flow; If it is determined that there is an area where the calculated congestion level and the predicted congestion level diverge based on predetermined conditions, the system updates the parameters of the people flow prediction model so that the calculated congestion level and the predicted congestion level in that area become closer.

3. 10. The system of claim 1, an environmental sensor for measuring an environment in each of the plurality of areas; the device includes a first device that changes the environment; The system further comprises: a server that determines at least one of a location and an orientation of the first device when the server determines in the device location optimization process that the value acquired by the environmental sensor satisfies a predetermined condition.

4. 10. The system of claim 1, the plurality of areas include a first area and a second area adjacent to each other, the device includes a second device for controlling the direction of pedestrian traffic; The system wherein the server decides to place the second device in the first area if it determines in the device placement optimization process that the number of people flowing from the first area to the second area in the predicted pedestrian flow is greater than or equal to a predetermined value.

5. 10. The system of claim 1, the plurality of areas include a first area and a second area adjacent to each other, the apparatus includes a third device for a person to stay; The system wherein the server decides to place the third device in the first area if it determines in the device placement optimization process that the number of people flowing from the first area to the second area in the predicted pedestrian flow is greater than or equal to a predetermined value.

6. 6. The system of claim 5, the third device includes a bench; The system wherein the server decides to place the bench in the first area if, in the equipment placement optimization process, the number of people flowing from the first area to the second area in the predicted pedestrian flow is equal to or greater than a predetermined value, and if, by referring to the event information, it determines that an event will be held in the first area.

7. 10. The system of claim 1, The server In the device layout optimization process, Accepts the designation of a venue mode included in multiple venue modes, The system determines at least one of a location and orientation of the device and an operation by the device based on the specified venue mode.

8. 8. The system of claim 7, the plurality of venue modes include opening hours and closing hours; The venue is defined with a first passageway leading from the entrance to the back of the venue and a second passageway leading from the back of the venue to the entrance, The server In the device layout optimization process, When the opening time is specified as the venue mode, determining that the device is disposed so that the width of the first passage is wider than the width of the second passage; When the closing time is specified as the venue mode, The system determines to position the device such that a path width of the second path is wider than a path width of the first path.

9. 8. The system of claim 7, the plurality of venue modes includes after hours; Cleaning robots are deployed in the venue, The server In the device layout optimization process, When the venue mode is set to outside business hours, The system determines to place the device at a predetermined location that avoids a predetermined cleaning area of ​​the cleaning robot.

10. 8. The system of claim 7, the plurality of venue modes includes an emergency mode, The server In the device layout optimization process, When the emergency situation is designated as the venue mode, The system determines placement of the devices in fixed positions along the walls of the venue.

11. 8. The system of claim 7, the plurality of venue modes includes an emergency mode, the device includes a second device for controlling the direction of pedestrian traffic; The server In the device layout optimization process, When the emergency situation is designated as the venue mode, The system determines to deploy the second device to restrict movement of people to the location of the incident, around a specified device included in the device, or around a specified facility located within the venue.

12. 10. The system of claim 1, the device includes a kiosk and a speaker; The server Calculating an actual flow of people in a specific area based on values ​​acquired by at least one of the sensor and the camera; Calculating the congestion level of the specific area based on the calculated actual pedestrian flow; If it is determined that the congestion level of the specific area is high based on a predetermined condition, The system determines, in the device placement optimization process, to place the kiosk and the speaker in an area different from the specific area, and to increase the volume of the voice output by the speaker calling to the kiosk.

13. 10. The system of claim 1, the devices include a fourth device relating to an event taking place in an area different from the specific area; The server Calculating an actual flow of people in the specific area based on values ​​acquired by at least one of the sensor and the camera; Calculating the congestion level of the specific area based on the calculated actual pedestrian flow; If it is determined that the congestion level of the specific area is high based on a predetermined condition, The system determines, in the device placement optimization process, to have the fourth device execute a process related to extending the duration of an event taking place in the different area.

14. 10. The system of claim 1, the device includes a screen; The server Calculating an actual flow of people in a specific area based on values ​​acquired by at least one of the sensor and the camera; Calculating the congestion level of the specific area based on the calculated actual pedestrian flow; If it is determined that the congestion level of the specific area is high based on a predetermined condition, The system determines, in the device placement optimization process, that the screen is placed in the specific area and that a display relating to an event taking place in an area different from the specific area is displayed on the screen.

15. A people flow control method using a system for controlling people flow in a venue including a plurality of areas, The system includes a server, at least one of a sensor and a camera that acquires a value indicating a people flow that indicates the inflow and outflow of people and the accumulation of people between the plurality of areas, and a device that can be placed in any of the plurality of areas and controls the people flow, The server Number of people information indicating the number of people entering the venue; event information indicating events to be held in each of the plurality of areas; The people flow control method includes: the server predicts the flow of people for each of the plurality of areas based on the number of people information and the event information; the server executes a device placement optimization process to determine at least one of a placement and orientation of the devices and a process by the devices in the plurality of areas based on the predicted pedestrian flow and values ​​acquired by at least one of the sensors and the camera; The people flow control method, wherein the device operates at a speed equal to or less than a predetermined value based on the result of the device placement optimization process.

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