Facility management device and method

Abstract

To perform more appropriate management for the operation of a facility having multiple unit areas.SOLUTION: A building management device 10 comprises: a movement state acquisition unit 12 that acquires movement state data indicating a movement state of a user in a first unit area within a facility, which has been detected by a detection device; a number-of-people data calculation unit 13 that calculates number-of-people data including at least one of the number of users staying in a second unit area within the facility and the number of users entering and exiting the second unit area, and further including time information from the movement state data; a usage attribute identification unit 15 that identifies a usage attribute related to a use purpose of the second unit area on the basis of the number-of-people data; and an activity state estimation unit 16 that estimates an activity state in an exclusive use area on the basis of the number-of-people data and the usage attribute, and creates time schedule data indicating the activity state.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a technique for managing facilities such as buildings. [Background technology]

[0002] In recent years, office buildings have seen a wide variety of work styles being adopted on each floor (each tenant on each floor). Examples of work styles include hybrid work, which combines working in the office and working from home, activity-based working, and free-address offices. For this reason, the traditional approach of uniformly operating facilities on each floor may no longer be in line with reality.

[0003] Facility equipment is operated centrally. For example, centralized control of hallway lighting and elevator operation can help achieve energy-saving control and efficient equipment operation. In this type of management, the number of facility users (e.g., office workers and visitors) increases or decreases over time, so in order to perform appropriate control, it is necessary to know the number of people in the room.

[0004] Patent Document 1 discloses a occupancy prediction device that "has a feature extraction unit 12 that detects a specific time period from the history of the number of people occupying the room on the day of prediction and extracts features of the detected specific time period, and an occupancy prediction unit 13 that predicts the number of people occupying the room after a specific time period based on the difference between the features extracted by the feature extraction unit 12 and the features of the specific time period extracted from the current fluctuation model." [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2018-26028 Summary of the Invention [Problem to be solved by the invention]

[0006] A facility such as a building includes multiple unit areas such as rooms, corridors, and entrances. The facility is also occupied by multiple tenants, such as companies. The number of people present in the facility is a control condition for the equipment. Therefore, when it is difficult to grasp the number of people present, it has been difficult to efficiently control the equipment. Therefore, the present invention aims to provide more appropriate management of the operation of a facility having multiple unit areas. [Means for solving the problem]

[0007] To solve the above problem, the present invention estimates the activity status for each time period in a second unit area based on the movement status of users in a first unit area of ​​a facility. Based on this estimation result, facility management can be realized. Here, it is more preferable to use the movement status in the common area as the movement status and estimate the activity status in the private area.

[0008] More specifically, the facility management device has a movement status acquisition unit that acquires movement status data indicating the movement status of users in a first unit area within the facility detected by a detection device, a number of people data calculation unit that calculates number of people data from the movement status data, including at least one of the number of users staying in a second unit area within the facility and the number of users entering and exiting the second unit area, and further including time information, a usage attribute identification unit that identifies usage attributes related to the use of the second unit area based on the number of people data, and an activity status estimation unit that estimates the activity status in the second unit area based on the number of people data and the usage attributes.

[0009] The present invention also includes a facility management method executed by the facility management device, a program for causing the facility management device to function as a computer, and a storage medium for storing the program.Furthermore, the present invention also includes a facility management system including the facility management device. [Effects of the Invention]

[0010] According to the present invention, it is possible to more appropriately manage the operation of a facility having a plurality of unit areas. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram for explaining an overview of one embodiment of the present invention. [Figure 2] 1 is a functional block diagram of a building management device 10 according to an embodiment of the present invention. [Figure 3] 1 is a system configuration diagram of a building management system 1 according to an embodiment of the present invention. [Figure 4] 1 is a hardware configuration diagram of a building management device 10 according to an embodiment of the present invention. [Figure 5] FIG. 10 is a diagram showing usage attribute data 181 used in one embodiment of the present invention. [Figure 6] FIG. 1 illustrates an activity event identification rule 182 used in one embodiment of the present invention. [Figure 7] FIG. 10 is a diagram showing number of people data 184 used in one embodiment of the present invention. [Figure 8A] FIG. 10 is a graph showing number of people data 184 (number of people entering a room) used in one embodiment of the present invention. [Figure 8B] 10 is a diagram showing, in a graph format, number of people data 184 (number of people leaving the room) used in one embodiment of the present invention. FIG. [Figure 9A] FIG. 10 is a diagram showing usage attribute identification data 185 used in one embodiment of the present invention. [Figure 9B] FIG. 10 is a diagram showing usage attribute identification data 185 used in one embodiment of the present invention. [Figure 10] FIG. 10 is a diagram illustrating time schedule data 186 used in one embodiment of the present invention. [Figure 11] 1 is a flowchart showing a processing flow according to an embodiment of the present invention. [Figure 12A]10 is a flowchart showing a specific example 1 of the usage attribute identification process (step S5) in one embodiment of the present invention. [Figure 12B] 10 is a flowchart showing a specific example 2 of the usage attribute identification process (step S5) in one embodiment of the present invention. [Figure 13] 10 is a flowchart for explaining details of the activity event identification process (step S7) in one embodiment of the present invention. [Figure 14] 1 is a graph illustrating the identification of activity events in one embodiment of the present invention. [Figure 15A] 10 is a graph showing the relationship between activity events and number data (group time zone: morning) in one embodiment of the present invention. [Figure 15B] 10 is a graph showing the relationship between activity events and number data (group time period: daytime) in one embodiment of the present invention. [Figure 15C] 10 is a graph showing the relationship between activity events and number data (group time period: afternoon) in one embodiment of the present invention. [Figure 16] 10 is a flowchart for explaining details of the process of estimating an active state (step S8) in one embodiment of the present invention. [Figure 17A] 10 is a graph showing the relationship between activity events and the number of people present in a room over time in one embodiment of the present invention. [Figure 17B] 10 is a graph showing the relationship between activity status and the number of people present in a room over time in one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] An embodiment of the present invention will be described below. As described above, in the present invention, an activity state for each time period in a second unit area is estimated based on the movement state of a user in a first unit area. Facility management can be realized based on this estimation result. It is more preferable to use the movement state in a common area, which is an example of a first unit area, as the movement state to create a time schedule for a private area, which is an example of a second unit area. The first unit area may be any unit area related to the second unit area and may be other than the common area. For example, it may be another private area. In this embodiment, the facility is an office building occupied by multiple tenants. When a facility is composed of private areas used exclusively by tenants and common areas used commonly by all tenants, it is particularly difficult for the facility manager to grasp the number of people in the private areas. For this reason, it is more preferable to use the movement state in the common area as the movement state to create a time schedule for the private area. In this embodiment, the private areas include floors and rooms and areas such as offices and conference rooms that make up the floors, while the common areas include corridors, entrances, elevator halls, and elevator cars. The equipment may include air conditioning equipment and lighting equipment in office rooms and corridors. Other examples of equipment include various robots, such as patrol robots, cleaning robots, and reception robots. The equipment may also be equipment that spans multiple floors or is controlled throughout the entire facility, such as elevators and power storage devices in facilities.

[0013] In this embodiment, the number of people entering, the number of people leaving (number of people entering and leaving), and the number of people present in the room are used as examples of movement states, but either the number of people entering and leaving or the number of people present in the room may be used. The movement state refers to the behavior or state of movement of a user, such as an office worker, such as entering or leaving a room. Presence is an example of a state of staying in a specified unit area, such as being seated or present. Therefore, the number of people present in the room is an example of the number of people staying. In this embodiment, entry and exit (entering and leaving) refers to entry and exit (entering and exiting) in a unit area, and is not limited to a room. Details of this embodiment will be explained below, divided into <Overview>, <Configuration>, <Information and Data>, and <Processing Flow>.

[0014] <Summary> First, an overview of this embodiment will be explained using Fig. 1. Fig. 1 is a diagram for explaining the overview of this embodiment. In Fig. 1, it is assumed that companies A, B, and C are tenants in a building that is the subject of this embodiment. The companies are located as follows: Company A: Office Room 1 on the 1st floor. Company B: Office room 2 on the 1st floor. Company C: All floors on the 2nd floor.

[0015] These three companies carry out their business activities according to the time schedule (timetable) shown in Figure 1. In the case of Company A, the activity events are (1) 7:00, when the locks are unlocked and the equipment is started (the time when the first employee enters), (2) 9:00, when the workday begins, (3) 12:00, when the lunch break begins, (4) 13:00, when the lunch break begins, (5) 17:00, when the workday ends, and (6) 20:00, when the locks are locked and the equipment is stopped. Here, the unlocks and equipment start-up time indicates the time when the entrance to Office Room 1 is unlocked and the equipment is started at Company A. Note that the first employee's entry time can also be used instead. The locks and equipment stop-down time indicates the time when the entrance to Office Room 1 is locked and the equipment is stopped at Company A. Note that the last employee's departure time (the time when the last employee leaves the room) can also be used instead. Furthermore, Company A has a final time for leaving work, which corresponds to the time for locking up and shutting down equipment.

[0016] Because of these activity events, Company A has the following activity states for the time periods between each activity event (inter-event time periods): (1)-(2) pre-work time period, (2)-(3) working hours (morning), (3)-(4) lunch break time period, (4)-(5) working hours (afternoon), (5)-(6) overtime time period, (6)-(1) absent time period the next day. Here, absent time period refers to when no workers are present in Office Room 1.

[0017] As mentioned above, building managers are usually unable to grasp these activity events and activity states. Therefore, the inventors of the present application decided to use the number of people entering and leaving the building, the number of people present in the building, and the time of day, which are the movement states of workers, to estimate the activity events and activity states. For example, if a large number of people enter the building in the morning, it can be assumed that workers are entering the building to go to work. Therefore, this time can be identified as the start of work. Furthermore, the period between the start of work and the start of the lunch break can be estimated as the working hours (morning). In this way, the time schedule (timetable) shown in FIG. 1 can be created.

[0018] Furthermore, activity events can be estimated using the number of people entering and exiting a room, the number of people present and the times, as well as the operation of equipment. For example, by the final locking and unlocking of an entrance / exit door, the unlocking and locking times can be identified as activity events. Also, by the start-up and stop times of equipment such as air conditioning, such as powering on and powering off, the equipment start-up and stop times can be identified as activity events. The time between these can then be estimated as the absence time when the worker is absent. As a result, the time schedule (timetable) shown in Figure 1 can be estimated.

[0019] Company B's activity events are identified as follows: (1) 8:30, work start time; (2) 11:45, lunch break start time; (3) 12:30, lunch break start time; (4) 16:00, work end time; (5) 19:00, break start time; and (6) 19:30, break end time. Compared to Company A, unlocking and equipment startup time and locking and equipment shutdown time are omitted, and break start time and break end time are added. Company B does not have a final time to leave work, so unlocking and equipment startup time and locking and equipment shutdown time are omitted. However, even if a final time to leave work is not set, the departure time of the last person to leave work can be identified as an activity event. Furthermore, Company B has breaks during overtime, so break start time and break end time are identified. These can be identified by the number of people traveling between the office and the break room. For example, they can be identified by the number of people leaving the office and the number of people entering the break room, and the number of people leaving the break room and the number of people entering the office. Furthermore, the number of people in the break room may be used.

[0020] As a result, the activity status of Company B can be estimated as follows: (1)-(2) working hours (morning), (2)-(3) lunch break, (3)-(4) working hours (afternoon), (4)-(5) overtime, (5)-(6) break, (6)-(1) overtime the next day.

[0021] Furthermore, Company C can identify activity events of the same type as those of Company A, but at different times, and estimate the activity status based on this. This is because Company C operates on the same type of work schedule as Company A, but at different times. Based on the above concept, in this embodiment, activity events are identified, and the activity status is estimated for each inter-event time period, which is the time period between activity events.

[0022] <Configuration> Next, a configuration for identifying activity events and estimating activity states for each inter-event time period will be described in accordance with the concept of FIG. 1. FIG. 2 is a functional block diagram of a building management device 10 according to this embodiment. The following description will be given using building management as an example of a facility. The building management device 10 is an example of a facility management device, and the present invention can also be applied to other facilities such as shopping malls and service areas. In FIG. 1, the building management device 10 includes a room setting unit 11, a movement status acquisition unit 12, a number of people data calculation unit 13, a normalization processing unit 14, a usage attribute identification unit 15, an activity status estimation unit 16, a command creation unit 17, and a storage unit 18 that stores usage attribute data 181, etc.

[0023] First, the room setting unit 11 sets a room as a target for estimating the activity state. To this end, the room setting unit 11 receives a room specification from a user of the building management device 10, and identifies (sets) a room to be managed based on this specification. A room is an example of a unit area that constitutes a facility including a building, and unit areas include floors (storeys), areas, rooms, stores, etc. However, the following description will mainly focus on rooms as an example. From the above, the room setting unit 11 can also be expressed as a unit area setting unit, and receives identification information for unit areas such as floors (storeys), area names, and room names. Rooms and areas include offices, conference rooms, corridors, elevator halls, toilets, break rooms, elevator cars, etc., that constitute a floor.

[0024] The room setting unit 11 may also specify a room based on building information indicating the floor, area, and room layout of a building to be managed. The building information is stored in the storage unit 18. When using this building information, the room setting unit 11 may set a unit area according to the layout position. For example, each floor of a building is extracted from the bottom floor, and the floors and rooms included therein are also extracted. Then, the room setting unit 11 sets unit areas indicating rooms, etc. in the order of extraction. Furthermore, the building information stores activity events, activity states, etc. for each unit area, and the room setting unit 11 sets unit areas where changes in activity events or activity states have occurred. Note that, hereinafter, this embodiment will be described using a room as an example of a unit area.

[0025] Furthermore, the movement status acquisition unit 12 acquires movement status data indicating the movement status of users such as office workers (hereinafter simply referred to as "office workers") in the building under management detected by the detection device. The movement status includes at least one of the entry / exit and presence of office workers in a room, and the movement status data preferably includes at least the number of people entering and leaving the room and the number of people present in the room. Furthermore, data indicating "yes" for entry / exit detected sequentially may be used as the movement status data.

[0026] Here, the detection device detects the movement status of building users in a target room, more preferably a room related to a private area. Here, "related to a private area" preferably refers to a common area that can be a movement route for the private area, but may also be the private area itself. The detection device includes a number-of-people sensor device such as a motion sensor and an entrance / exit device. Furthermore, the detection device also includes sensors installed in elevators, such as load sensors and elevator control devices that detect stopping floors. Furthermore, a time attendance management device that detects log-on and log-off on a PC used in an office may also be used as the detection device. It is desirable that the movement status data include the detected or acquired time. For example, the number of people entering and exiting the room and the number of people present at a predetermined interval (e.g., every 5 to 15 minutes) can be used. As a result of the above, movement status data is output from the movement status acquisition unit 12. It is desirable that the detection device detects the movement status in the common areas of a building. In this case, it is more desirable to install the detection device in the common areas.

[0027] Furthermore, the number of people data calculation unit 13 calculates, from the movement status data, number of people data, which is time-series data including time information, indicating at least one of the number of workers present in the room and the number of people entering and leaving the room. Here, it is preferable that the room is a room related to the exclusive area. A room related to the exclusive area includes the exclusive area itself and the movement route to the exclusive area (such as a hallway or elevator). As a result, it becomes possible to use the movement status data and number of people data related to the exclusive area.

[0028] The movement status data may also be used as the number of people data. In this case, the movement status data is time-series data and includes at least one of the number of people present and the number of people entering and leaving the room. Furthermore, the detection device may output the number of people entering and leaving the room or the number of people present, and this may be used as the movement status data or the number of people data. By using such number of people data, it is possible to grasp the number of people in private areas, which is difficult to grasp.

[0029] Furthermore, when the number of people data includes the number of people present in the room, the number of people data calculation unit 13 calculates the number of people present in the room using the following (Equation 1). Number of people in the room (t) = Number of people entering the room (t) - Number of people leaving the room (t) + Number of people in the room (t - △t) · · (Number 1) Here, t is the target time, and t-Δt is the time one cycle (interval) before t (for example, Δt is 5 minutes). Furthermore, it is desirable that the movement status acquisition unit 12 and the number of people data calculation unit 13 perform the process periodically, such as daily or weekly. In this case, the number of people data calculation unit 13 resets the number of people data (for example, the number of people present) to a predetermined value (for example, zero) every cycle. As a result of the above, the number of people data calculation unit 13 outputs the number of people data, which is time-series data. It is desirable that the movement status data is also time-series data, but this is not a limitation. If the movement status data is not time-series data, the number of people data calculation unit 13 can identify time information for converting it into time-series data using the timing at which the movement status data is received.

[0030] Furthermore, the normalization processing unit 14 performs normalization processing on the number of people data. Generally, each room has different characteristics related to the number of people, such as the number of people that can be accommodated, such as capacity and volume. For this reason, using the number of people data as is may reduce the accuracy of processing such as identifying activity events, which will be described later. Therefore, by normalizing the number of people data, for example, within a range from 0 to 1, the number of people data for each room can be processed in a common (uniform) manner. Here, normalization includes dividing the number of people indicated by the number of people data (e.g., the number of people entering, the number of people leaving, and the number of people present) by the maximum value over a predetermined period. The maximum value can be, for example, the average value of the maximum values ​​of the data for each day over the past month. Note that the normalization processing may be omitted if the rooms in a building have a uniform configuration. As a result of the above, the normalized number of people data is output from the normalization processing unit 14.

[0031] Furthermore, the usage attribute identification unit 15 identifies usage attributes related to the purpose of the target room based on the number of people data. Note that the usage attribute identification unit 15 may limit the identification of usage attributes to exclusive areas. This is because common areas can be understood to some extent by the building management side. Furthermore, it is desirable for the usage attribute identification unit 15 to use normalized number of people when identifying usage attributes, but it may also use number of people data calculated by the number of people data calculation unit 13.

[0032] Here, the usage attribute indicates an attribute (type) according to the use of the room. For example, it can be identified as a main office type, a conference room type, a cafeteria type, a lounge type shared by tenants of a building, a lobby type, a parking lot type, or a facility type such as a floor for large equipment. The usage attribute may also be a residential type such as an apartment building, a store type such as a convenience store, or an accommodation type such as a hotel. The usage attribute identification unit 15 also identifies the usage attribute based on the number of people in the room per day and the characteristics of the number of people data.

[0033] Furthermore, as a feature of the number of people data, for example, a feature of a pattern (tendency, degree) of change in the number of people over time for a predetermined period such as one day can be used. In this embodiment, the feature of the number of people data is referred to as usage attribute identification data 185. In this embodiment, the usage attribute identification data 185 is stored in the storage unit 18. As a result of the above, usage attribute data indicating the usage attribute of the room is output from the usage attribute identification unit 15. Note that this usage attribute is stored in the storage unit 18 as usage attribute data 181. Note that details of the usage attribute data 181 and the usage attribute identification data 185 will be described in <Information and Data>.

[0034] Furthermore, the activity state estimation unit 16 estimates the activity state for each inter-event time period in the room based on the number of people data and the usage attributes, and creates time schedule data indicating this activity state. Here, the inter-event time period refers to the time period between adjacent activity events, which will be described later. The activity state also indicates the type of activity, such as work, in the room. For example, office-type activity states include working hours, lunch break hours, and overtime hours.

[0035] To create the time schedule data, the activity state estimation unit 16 has a time period classification unit 161, an activity event identification unit 162, and a time schedule data creation unit 163. These will be described below. The number of people data used here is the number of people entering (t), the number of people leaving (t), and the number of people present (t) at time t, or the number of people entering (t) and the number of people leaving (t) at time t.

[0036] First, the time slot classification unit 161 classifies the number of people data into group time slots that make up a predetermined period based on the time information of the number of people data. That is, the time slot classification unit 161 assigns each number of people data to a group time slot. For example, the time slot classification unit 161 classifies one day, which is a predetermined period, into data for three group time slots: morning, daytime, and afternoon. As an example, morning can be classified as 00:00 to 11:00, daytime as 11:00-14:00, and afternoon as 14:00-24:00. By classifying in this way, it becomes possible to identify the next activity event using the characteristics of the activity event for each group time slot.

[0037] Furthermore, the activity event identification unit 162 applies the activity event identification rules 182 to the usage attributes, group time period, and number of people data to identify the activity event of the room according to the number of people data. In this case, the activity event identification rules 182 indicate the characteristics of the number of people data. For example, they indicate the characteristics indicated by the results of a comparison between number of people data (such as a size comparison) or the characteristics in a time series (such as maximum values, local maximum values, or the tendency or degree of change). Details of the activity event identification rules 182 will be explained in <Information and Data>.

[0038] The processing of the activity event identification unit 162 will be described below. For example, the time information of the number of people data, which satisfies the conditions that the floor attribute is office type, the time period is morning, the number of people entering the room is greater than the number of people leaving the room, and the number of people entering the room is the largest, is identified as the start time of work. Here, the time information of the number of people data is a time element in time-series data, and indicates, for example, the detected time, the created time, or the acquired time. Furthermore, the time information of the number of people data, which satisfies the conditions that the usage attribute is also office type, the group time period is daytime, the number of people leaving the room is greater than the number of people entering the room, and the number of people leaving the room is the largest, is identified as the start time of lunch break. In this way, the activity event of a room can be identified based on the usage attribute and using the number of people data for that group time period.

[0039] Furthermore, the time schedule data creation unit 163 estimates the activity state for each inter-event time period according to the identified activity event. Then, the time schedule data creation unit 163 creates time schedule data indicating the activity state. Note that the time schedule data includes at least the activity state, and more preferably also includes the activity event.

[0040] For this purpose, the time schedule data creation unit 163 uses the time of each identified activity event to estimate a time schedule of the activity state on the time axis for the target room. For example, if the usage attribute is office type, the activity state is estimated to be morning work during the inter-event time period, which is the time period between the identified start time of work and the start time of lunch break. By repeatedly performing such estimation for each activity event, time schedule data indicating the activity state for a predetermined period, such as one day, can be created. The time schedule data created in this way indicates the activity status for each inter-event time period. Therefore, the time schedule data also divides time periods into activity states. As a result of the above, time schedule data is output from the activity state estimation unit 16.

[0041] Furthermore, the command creation unit 17 creates commands for managing the building to be managed in accordance with the created time schedule data. These management commands include control commands for the equipment and management operation commands for managing and operating the building. For this reason, the command creation unit 17 includes a building equipment control unit 171 and a building management operation unit 172. Note that only one of the building equipment control unit 171 and the building management operation unit 172 may be provided, or both may be omitted.

[0042] First, the building equipment control unit 171 creates control commands to control the equipment related to the target room, such as air conditioning, lighting, elevators, and robots, based on the time schedule data. Here, the equipment related to the room includes not only the equipment installed or patrolling in the room, but also the equipment used by the workers in the room. An example of the equipment used by the workers is the elevator used to enter and exit the room.

[0043] The building equipment control unit 171 then outputs control commands to each control device that controls the equipment. The control commands include commands to control the air conditioning and lighting in a comfort-oriented operating mode for the target room during office hours according to the time schedule, and to control the lighting in an energy-saving operating mode during other active hours. Furthermore, it is desirable to control the air conditioning and lighting to be turned off when no one is present. In this way, by using the time schedule data for each room, the equipment in that room can be appropriately controlled according to its activity status, enabling efficient operation.

[0044] Furthermore, the building management operation unit 172 generates management and operation commands for managing and operating the building, such as security, cleaning, and cafeteria operation, based on the time schedule data. The building management and operation unit 172 then outputs the management and operation commands to various management devices. The management and operation commands are used by the various management devices to manage security patrol plans, cleaning schedules, and cafeteria operation time schedules for each room in the building. For example, a management and operation command may be generated to clean the common hallways and restrooms on the target floor during office hours when there are few people coming and going, or to adaptively adjust the number of meals prepared in the cafeteria according to the lunch break on each floor. This enables efficient cleaning during times when there are few people and efficient adjustment of the number of meals according to changes in the number of people during the lunch break. In this way, the building management device 10 in this embodiment manages and operates the building in cooperation with control devices, management devices, and the like. Therefore, the following describes the building management system 1, which includes the building management device 10 as well as control devices and the like.

[0045] 3 is a system configuration diagram of a building management system 1 according to this embodiment. In the building management system 1, a building management device 10 is connected to other devices, including control devices and management devices, to which the above-mentioned commands are output, via a common line 80 of an information network in the building. Furthermore, various pieces of equipment are connected to the control devices, which the control devices control in accordance with the control commands. For this control, for example, the control devices output control signals to the equipment in accordance with the control commands.

[0046] These will be described below. First, elevator equipment 20, a group of building patrol robots 30, and equipment groups 40 on each floor are installed and operated in a building. First, the elevator equipment 20 has a car, a drive unit, and the like, and transports people and luggage. An elevator group control device 21, which is an example of a control device, is connected to the elevator equipment 20 and controls its operation. The elevator group control device 21 receives elevator group control commands, which are an example of control commands, from the building management device 10, and controls the elevator equipment 20 in accordance with these commands. The elevator group control device 21 also outputs operation data, including the load and stopping floors from the elevator equipment 20, to the building management device 10. Therefore, the operation data itself, or at least a part of it, can be treated as movement status data.

[0047] The group of building patrol robots 30 is a group of robots that autonomously patrol a building (facility) and provide security and guidance. However, this does not necessarily have to be a group, and a single robot may be used. The group of building patrol robots 30 is connected to a robot group controller 31, which is an example of a controller, that controls operation. The robot group controller 31 receives robot group control commands, which are an example of control commands, from the building management device 10, and controls the group of building patrol robots 30 in accordance with these commands. Note that a human presence sensor may be provided in the group of building patrol robots 30, and movement operation data corresponding to the detection results may be output from the group of building patrol robots 30 to the building management device 10 via the robot group controller 31. The group of building patrol robots 30 and the robot group controller 31 may also be configured as an integrated unit.

[0048] Furthermore, the equipment group 40 on each floor is equipment installed on the respective floors of the building. In FIG. 3, the equipment group 40 on each floor is exemplified by air conditioning equipment 401, lighting equipment 402, entrance / exit devices 403, human presence sensors 404, and building management terminal devices 405. Note that the sub-numbers in the symbols in the figure indicate the floors (floors). Note that, although FIG. 3 shows the equipment on each floor, these may be provided for each room, or may not be provided in unit areas such as some rooms. Each piece of equipment and the control device that controls it will be described below.

[0049] First, air conditioning equipment 401-X is air conditioning equipment installed on the Xth floor. An air conditioning equipment control device 41, which is an example of a control device, is connected to the air conditioning equipment 401-X and controls its operation. The air conditioning equipment control device 41 receives air conditioning control commands, which are an example of control commands, from the building management device 10, and controls the air conditioning equipment 401, such as the air conditioning equipment 401-X and 401-Y, in accordance with the commands. Note that a human presence sensor may be provided in the air conditioning equipment 401, and movement behavior data corresponding to the detection results may be output from the air conditioning equipment 401 to the building management device 10 via the air conditioning equipment control device 41.

[0050] Lighting equipment 402-X is lighting equipment installed on the Xth floor. A lighting equipment control device 42, which is an example of a control device, is connected to the lighting equipment 402-X and controls its operation. The lighting equipment control device 42 receives an air conditioning control command, which is an example of a control command, from the building management device 10, and controls lighting equipment 402 such as lighting equipment 402-X and 402-Y in accordance with the command. Note that a human presence sensor may be provided in the lighting equipment 402, and movement behavior data corresponding to the detection result may be output from the lighting equipment 402 to the building management device 10 via the lighting equipment control device 42.

[0051] Furthermore, the entrance / exit device 403-X is an entrance / exit device installed on the Xth floor. This entrance / exit device 403-X may be installed on a room door or as a gate. The entrance / exit device 403-X is connected to the entrance / exit management device 43. The entrance / exit management device 43 may output the number of people entering and exiting, which is an example of movement behavior data based on the detection results of the entrance / exit devices 403 such as the entrance / exit devices 403-X and 403-Y, to the building management device 10. The entrance / exit management device 43 may also function as a control device. That is, near the start of work, the entrance of people is predicted based on operation data from the elevator group control device 21, and the entrance / exit management device 43 performs control such as unlocking the entrance / exit device 403 accordingly.

[0052] Furthermore, the human presence sensor 404-X is a human presence sensor installed on the Xth floor. The human presence sensor 404-X may be installed near an entrance or exit of a room, or may be installed near a desk or other office location. The human presence sensor 404-X is connected to the human presence sensor management device 44. The human presence sensor management device 44 may output to the building management device 10 the number of people present in the room, which is an example of movement behavior data based on the detection results of the human presence sensors 404, such as the human presence sensors 404-X and 404-Y.

[0053] Furthermore, the building management terminal devices 405, such as the building management terminal devices 405-X and 405-Y, are terminal devices used by building managers and tenant staff, and can be realized by PCs, tablets, etc. For this purpose, the building management terminal devices 405 output information about the facilities in the building and each tenant, as well as building operation rules, to the building management apparatus 10. Furthermore, the building management terminal devices 405 receive instructions for processing in this embodiment, such as creating time schedule data and setting rooms, from the manager, etc. Furthermore, the building management terminal devices 405 receive and display information and data handled by the building management apparatus 10, such as time schedule data, in addition to operation information indicating the operating status of the facilities.

[0054] The building management terminal device 405 may be installed outside the building and connected to the building management device 10 via a wide area network such as the Internet. In this case, the building management terminal device 405 may be used to manage multiple buildings. Furthermore, the building management device 10 may be installed in multiple buildings, i.e., in a facility. In this case, the building management device 10 can be realized by so-called cloud computing. Furthermore, the building management terminal device 405 may be limited to use by the building management side.

[0055] Furthermore, the building security operation device 50, the building cleaning operation device 60, and the building cafeteria operation device 70 are devices for managing security operations, cleaning operations, and cafeteria operations performed by security guards within the building, respectively. Therefore, they receive security operation information, cleaning operation information, and cafeteria operation information as management and operation commands from the building management device 10. As a result, appropriate operations such as security can be performed according to the number of people occupying a room. For example, security and cleaning operations can be performed during times when the number of people occupying the corresponding room is low. Furthermore, the opening hours of the cafeteria can be set to comprehensively cover each tenant's lunch break. In the example of FIG. 1, the cafeteria opens from 11:45 at the latest to 1:00 at the earliest. Furthermore, ingredients can be prepared and the number of people cooked can be determined based on the number of people occupying the cafeteria. The building security operation device 50, the building cleaning operation device 60, and the building cafeteria operation device 70 may each be a subsystem or system including these devices.

[0056] Furthermore, the equipment may be a PC used by the workers, and the control device may be an attendance management device. For example, when it is near the end of the working day or when the equipment is locked or shut down, the attendance management device receives a control command to display a message on the PC encouraging employees to clock out. In response to this, the attendance management device controls the PC to display the corresponding message. Furthermore, the attendance management device may receive logon / off status from the PC and count the number of people present. As a result, this can be used as movement status data.

[0057] Next, an example implementation of the building management device 10 that performs the main processing of this embodiment will be described. FIG. 4 is a hardware configuration diagram of the building management device 10 in this embodiment. In this embodiment, the building management device 10 can be realized by a server, which is an example of a computer, in particular, by a cloud. As shown in FIG. 4, the building management device 10 has a processing device 101, a communication device 102, a memory 103, and a secondary storage device 104, which are connected to each other via a communication path.

[0058] First, the processing device 101 can be realized by a processor such as a CPU, and executes calculations in accordance with a building management program 105 stored in a secondary storage device 104, which will be described later. The building management program 105 will be described later. The communication device 102 has a function for communicating with other devices via a common line 80. It corresponds to an input unit and an output unit not shown in FIG. 2. In other words, the building management device 10 may be provided with an input unit that accepts input from users of the building management device 10 and an output unit that outputs information. In this case, these can be realized by an input device such as a keyboard or a display device such as a display screen.

[0059] 2. The memory 103 and the secondary storage device 104 store the building management program 105 and information used for processing by the processing device 101. The secondary storage device 104 can be implemented as a storage device, and stores the building management program 105, usage attribute data 181, activity event identification rules 182, movement status data 183, number of people data 184, usage attribute identification data 185, and time schedule data 186. These will be explained later in the section "Information and Data." The secondary storage device 104 may be implemented as various storage media such as an external hard disk drive (HDD), solid state drive (SSD), or memory card, or may be implemented as a device separate from the building management device 10, such as a file server.

[0060] Here, the building management program 105 is composed of a room setting module 106, a movement status acquisition module 107, a number of people data calculation module 108, a normalization processing module 109, a usage attribute identification module 110, an activity status estimation module 111, and a command creation module 112. Note that each of these modules may be realized as an individual program or a partial combination.

[0061] The configuration shown in FIG. 1, which performs the same functions as each module, is as follows: Room setting module 106: Room setting module 11 Moving state acquisition module 107: Moving state acquisition unit 12 Number of people data calculation module 108: Number of people data calculation unit 13 Normalization processing module 109: normalization processing unit 14 Usage attribute identification module 110: Usage attribute identification unit 15 Activity state estimation module 111: Activity state estimation unit 16 Command creation module 112: Command creation unit 17 Therefore, the processing device 101 executes the processes of the room setting unit 11, movement status acquisition unit 12, number of people data calculation unit 13, normalization processing unit 14, usage attribute identification unit 15, activity status estimation unit 16, and command creation unit 17 in accordance with the building management program 105. Note that each of these modules may be configured as an individual program. Furthermore, the activity status estimation module 111 may be configured as a time period classification module, an activity event identification module, and a time schedule data creation module. These correspond to the time period classification unit 161, the activity event identification unit 162, and the time schedule data creation unit 163, respectively. This concludes the description of the configuration of this embodiment, and next we will explain the information and data used in this embodiment.

[0062] <Information and Data> First, FIG. 5 is a diagram showing usage attribute data 181 used in this embodiment. The usage attribute data 181 stores, for each room, a usage attribute indicating an attribute (type) according to the use of the room. For example, for the B3 floor, "unmanned" indicates that there is usually no one in that room (floor). The usage attribute data 181 can also be treated as building management data that identifies rooms that make up the building to be managed by using the items of its own room. The building management data may be configured as information separate from the usage attribute data 181, with rooms associated with each building. The building management data preferably includes tenants or room capacities.

[0063] FIG. 6 is a diagram showing an activity event identification rule 182 used in this embodiment. The activity event identification rule 182 indicates rules for identifying activity events in each room. For this reason, as shown in FIG. 6, the activity event identification rule 182 stores time period conditions and activity event identification rules for each activity event. First, an activity event occurs periodically and indicates a characteristic activity of a tenant at any time. Examples include the start time of work and the start time of a lunch break, as mentioned above. As such, an activity event basically occurs at predetermined intervals, such as one day, but may occur multiple times within a predetermined period. For example, in a tenant that employs multiple work schedules, such as short-time work and regular work, the end time of each schedule can be managed as an activity event.

[0064] The time period condition is an example of a time condition for the number of people data for identifying an activity event. In this embodiment, group time periods (morning, afternoon, afternoon) constituting a predetermined period are used. The identification rule is a rule for identifying an activity event based on the number of people data, and indicates characteristics of the number of people data, particularly characteristics indicated by a comparison result of the number of people present and the number of people entering and leaving, and characteristics in a time series. For example, if the target number of people data is morning data, and (1) the number of people present is 0, and (2) the first person to enter the room is 1 and the number of people present is greater than 0 for a predetermined time or longer, the activity event identification rule 182 corresponds to record "No. 1." As a result, the activity event is determined to be "entry of the first worker." In this way, by using the activity event identification rule 182, an activity event can be identified for the number of people data. For example, an activity event is identified based on the comparison result of the number of people present and the number of people entering and leaving, which are the number of people data, as in (2). Although the time period condition is used in this embodiment, it may be omitted.

[0065] Furthermore, in the example of FIG. 6, the activity event identification unit 162 can identify activity events as follows. As the start time of No. 3, a time can be identified where the group time zone is in the morning, the number of people entering is greater than the number of people leaving, and the number of people entering is at its maximum (it can be a peak value such as a maximum value). Note that the start time can also be the time when the number of people present is at its maximum and the number of people entering approaches zero. Here, "the number of people entering approaches zero" means that the number of people present decreases over time and reaches a predetermined number, such as one or two.

[0066] Furthermore, for No. 4, the start time of the lunch break, the group time period is in the afternoon, after the start of work, the time when the number of people leaving is greater than the number of people entering, and the time when the number of people leaving is at its maximum can be identified. For No. 5, the end time of the lunch break, the group time period is in the morning, after the start of the lunch break, the time when the number of people entering is greater than the number of people leaving, and the last time during that time when the number of people entering reaches its peak can be identified. Furthermore, for No. 6, the end time of the workday, the group time period is in the afternoon, after the end of the lunch break, the time when the number of people leaving is greater than the number of people entering, and the time when the number of people leaving is at its maximum (or a peak value such as a maximum value) can be identified.

[0067] In these examples, the comparison of the number of people entering and leaving the room may be detected as a time period. Furthermore, the classification results (time) of adjacent activity events are used for No. 3 to No. 6. For this reason, it is desirable for the activity event classification unit 162 to classify the number of people data or activity events in chronological order.

[0068] 7 is a diagram showing number of people data 184 used in this embodiment. The number of people data 184 is time-series data that indicates at least one of the number of people in a target room and the number of people entering and leaving the room, and includes time information. Here, it is preferable to use a private room as the room for the number of people data, but this is not limitative.

[0069] For this reason, the number of people data 184 stores the date, time, number of people entering the room, number of people leaving the room, and number of people present for each room (unit area). Here, the date and time indicate the time when the number of people data 184 was detected, acquired, etc. For this reason, the date and time do not need to be separated, and the unit is not limited to minutes as shown in the figure. The number of people entering the room, number of people leaving the room, and number of people present respectively indicate the number of people in the corresponding room at the time (date, time).

[0070] 8A and 8B are diagrams showing the number of people data 184 used in this embodiment in a graph format. First, FIG. 8A is a diagram showing the number of people entering the room in a graph format, among the number of people data 184. According to FIG. 8A, it can be seen that the number of people entering the room peaks from 08:00 to around 09:00 and around 13:00. This is because many people come to work from 08:00 to around 09:00 as work begins, and many people return to the office from outside around 13:00 as lunch break ends.

[0071] FIG. 8B is a graph showing the number of people leaving the room from the number of people data 184. FIG. 8B shows that the number of people leaving the room peaks around 12:00 and just before 18:00. This is because around 12:00 is the start of the lunch break, so many people go out for lunch, and around 18:00 is the end of work, so many people leave work. In this way, by analyzing the number of people data 184, which is time-series data, it is possible to identify activity events occurring in the room in question.

[0072] The identification of activity events as described here is performed assuming that the room is used as a so-called office. In this way, to identify activity events with higher accuracy, it is desirable to identify a usage attribute for the room's usage. This is because the number of people data 184 shows a characteristic trend depending on the usage attribute. Therefore, in this embodiment, the usage attribute is identified from the trend and characteristics of the number of people data 184. The following describes the usage attribute identification data 185 used to identify this usage attribute.

[0073] 9A and 9B are diagrams illustrating usage attribute identification data 185 used in this embodiment. Here, two usage attribute identification data 185 are described, but these may be used together, or only one of them may be used. First, FIG. 9A illustrates usage attribute identification data 185-1 when the usage attribute type is divided into two types, office type and non-office type. In the usage attribute identification data 185-1, a usage attribute identification data pattern is stored for each usage attribute type. Here, the usage attribute identification data pattern is an example of a feature of the number of people data (time-series data) for identifying the usage attribute, and other types of features may also be used. In the example of FIG. 9A, the data pattern of the number of people present is shown as the usage attribute identification data pattern for the office type. However, any data pattern for the number of people data 184, such as the number of people entering and exiting, may be used. Furthermore, FIG. 9A illustrates a data pattern for the office type and "other" for the non-office type, but data patterns may be described for the non-office type or both.

[0074] FIG. 9B also shows usage attribute identification data 185-2, which further subdivides usage attributes than the usage attribute identification data 185-1 in FIG. 9A. That is, in addition to office type, the usage attribute identification data 185-2 includes lobby type, cafeteria type, shared space / lounge type, parking lot type, and unmanned type as usage attribute types. The usage attribute identification data 185-2 stores, for each usage attribute type, the characteristics of the number of people data (time-series data) used to identify the usage attribute. Furthermore, the characteristics of the number of people data (time-series data) are recorded, including the characteristics of the number of people present and the number of people entering and leaving the room (number of people in the room and number of people leaving). Note that this classification into the characteristics of the number of people present and the number of people entering and leaving the room is merely an example, and either one of these may be used, or the number of people entering and leaving the room may be separated. Furthermore, at least some of the number of people present, the number of people entering, the number of people leaving, and the number of people entering and leaving may also be used.

[0075] 10 is a diagram showing time schedule data 186 used in this embodiment. The time schedule data 186 indicates a room schedule, and preferably includes at least one of an activity event and an activity status. Based on this, the building's equipment is controlled and the building is managed and operated.

[0076] For this purpose, as shown in Fig. 10, the time schedule data 186 stores the estimated time / inter-event time period for each activity event or activity state occurring in the room. The time schedule data 186 may use either the activity event or the activity state alone. In this case, it is preferable to indicate the activity state.

[0077] Furthermore, activity events are estimated to occur at certain times, and activity states are estimated to occur in inter-event time periods. Therefore, in the example of FIG. 10, time / inter-event time periods are provided as time elements, indicating either time or inter-event time periods. Here, since activity states in this embodiment occur between activity events, inter-event time periods are used, which are the time periods between them. However, this is merely an example, and other time elements, such as time periods, may also be used. Note that, since the time schedule data 186 is created for each room, an item for identifying the room may be provided, or the time schedule data 186 may be handled separately for each room.

[0078] In this embodiment, movement status data 183 is also used, which indicates log data relating to the movements of building users, such as office workers, detected by various detection devices. As described above, the movement status data 183 may be used as number of people data 184, or the movement status data 183 may be data without time information. In the latter case, the time at which the movement status data 183 is received by the building management device 10 can be treated as time information. This concludes the explanation of <Information and Data>, and next we will explain the <Processing Flow>.

[0079] <Processing flow> The processing flow of this embodiment will be described below. The processing entity will be described using the components of FIGS. 2 and 3. First, FIG. 11 is a flowchart showing the processing flow of this embodiment. In step S1, the room setting unit 11 sets a target room in response to a user's instruction, etc., of the building management device 10. To this end, the room setting unit 11 extracts a room from the usage attribute data 181 used as building management data. The room setting unit 11 may automatically set the target room according to a preset rule. For example, the target room may be set periodically. This enables the time schedule data 186 to be updated periodically, such as quarterly or annually, in accordance with transfers and organizational restructuring. Furthermore, in step S1, multiple rooms, for example, all rooms constituting the building, may be set as targets. Furthermore, in step S1, a room related to a private area may be extracted as the target room.

[0080] In step S2, the movement status acquisition unit 12 acquires movement status data of the worker related to the target room. Here, the worker refers to a user of the building, including visitors to the room and workers in other rooms. For this purpose, a detection device such as the human presence sensor 404-X detects the movement status, and the movement status acquisition unit 12 receives movement status data indicating the movement status detected by the detection device. This movement status data does not have to be time-series data as described above. Note that the movement status acquisition unit 12 may receive the movement status data by either so-called pull-type distribution or push-type distribution.

[0081] In step S3, the number of people data calculation unit 13 calculates the number of people data in the target room from the acquired movement state data. The calculation method has already been described, so details will be omitted, but the movement state data may be used as the number of people data.

[0082] Furthermore, in step S4, the normalization processing unit 14 performs normalization processing on the calculated number of people data. This is performed to uniformly handle number of people data for rooms with different number-related characteristics. Then, the normalization processing unit 14 stores the normalized number of people data as number of people data 184 in the storage unit 18. Note that if the target building is made up of uniform rooms, step S4 may be skipped. In this case, the number of people data calculation unit 13 stores the calculated number of people data in the storage unit 18 as number of people data 184.

[0083] Furthermore, in step S5, the usage attribute identification unit 15 identifies a usage attribute related to the purpose of the target room based on the normalized number of people data. At this time, the usage attribute identification unit 15 uses usage attribute identification data 185. Furthermore, the usage attribute identification unit 15 stores the identified usage attribute in the storage unit 18 as usage attribute data 181. Here, two specific examples of the usage attribute identification process in step S5 will be described in detail with reference to FIGS. 12A and 12B.

[0084] 12A is a flowchart showing a specific example 1 of the usage attribute identification process (step S5) in this embodiment. First, specific example 1 is an example of identifying usage attributes according to the usage form of a building to be managed. Here, an example will be described in which the usage form is determined to be an office building or a building with various usage forms.

[0085] First, in step S51, the usage attribute identification unit 15 extracts a target room from the rooms set in step S1. Then, in step S52, the usage attribute identification unit 15 reads the number of people data 184 in the room extracted in step S51 from the storage unit 18. Then, in step S53, the usage attribute identification unit 15 determines the usage type of the target building. In this embodiment, it is determined whether the target building is an office-type building or a diverse-use building. Here, an office-type building is a building where rooms are mainly used as offices. A diverse-use building is a building where rooms with various usage types are mixed. This can be determined using building management data. If the result of this determination is that the target building is an office-type building, the process proceeds to step S54. If the target building is a diverse-use building, the process proceeds to step S55.

[0086] Then, in step S54, the usage attribute identification unit 15 uses the usage attribute identification data 185-1 to identify whether the target room is an office type or a non-office type. To do this, the usage attribute identification unit 15 determines which of the usage attribute types in the usage attribute identification data 185-1 the number of people data 184 read in step S52 corresponds to. For example, the usage attribute identification unit 15 first compares the office type usage attribute data pattern with the number of people data 184. As a result, if these are similar or match, the usage attribute identification unit 15 determines that the usage attribute of the target room is an office type. On the other hand, if they are not similar or match, the usage attribute identification unit 15 determines that the usage attribute of the target room is a non-office type.

[0087] In step S55, the usage attribute identification unit 15 uses the usage attribute identification data 185-2 to identify the usage attribute of the target room. To this end, the usage attribute identification unit 15 determines which of the usage attribute types in the usage attribute identification data 185-2 the number of people data 184 read in step S52 corresponds to.

[0088] For this purpose, the usage attribute identification unit 15 extracts the characteristics of the read number of people data 184. Then, the usage attribute identification unit 15 compares these characteristics with the number of people data characteristics for identifying the usage attribute of the usage attribute identification data 185-2. As a result, the usage attribute identification unit 15 identifies the usage attribute type of the number of people data characteristics that are similar or match the usage attribute identification data 185-2.

[0089] Furthermore, in step S56, the usage attribute identification unit 15 uses the building management data to determine whether the identification of usage attributes for each target room has been completed. To this end, the usage attribute identification unit 15 determines whether identification has been completed for each room set in step S1. The usage attribute identification unit 15 may also use the building management data to determine whether identification has been completed for the building to be managed. As a result, if the identification of each room has been completed, the process proceeds to step S57. If the identification has not been completed, the process returns to step S51, and the subsequent processing is performed for the other rooms. Then, in step S57, the usage attribute identification unit 15 stores the usage attributes identified in steps S54 and S55 as usage attribute data 181.

[0090] This concludes the explanation of Specific Example 1, and next we will explain Specific Example 2. In Specific Example 2, the usage attribute is identified using both the usage attribute identification data 185-1 and the usage attribute identification data 185-2. The details will be explained below with reference to Fig. 12B.

[0091] First, steps S51 and S52 are executed in the same manner as in Specific Example 1. Then, in step S54, the usage attribute identification unit 15 uses the usage attribute identification data 185-1 to identify whether the target room is an office type or a non-office type, as in Specific Example 1. As a result, if the target room is a non-office type, the process proceeds to step S55. If the target room is an office type, the process proceeds to step S56.

[0092] Furthermore, in steps S55 to S57, the same processing as in Specific Example 1 is performed. As a result, it is possible to identify the usage attribute of each room without taking into account the usage pattern of the building to be managed. This concludes the description of the usage attribute identification processing in step S5, but it may be performed in other modes. For example, the usage attribute identification unit 15 may perform step S55 for a room identified as a non-office type in step S54 of Specific Example 1. Alternatively, step S54 may be skipped and step S55 may be performed regardless of whether the room is an office type or a non-office type. Furthermore, step S54 may be performed to identify the usage attribute as an office type or a non-office type.

[0093] Next, returning to FIG. 11, the processing from step S6 onwards will be described. In steps S6 to S8, the activity state estimation unit 16 executes the activity state estimation processing. This will be described in detail below. First, in step S6, the time period classification unit 161 of the activity state estimation unit 16 allocates the normalized number of people data to one of three group time periods: morning, noon, and afternoon. In other words, the data is classified into one of the group time periods. Here, one day is an example of a periodic predetermined period in which activity events occur periodically. Furthermore, morning, noon, and afternoon are examples of group time periods that make up the predetermined period. Therefore, periods other than one day, morning, noon, and afternoon may be used.

[0094] In step S7, the activity event identification unit 162 identifies an activity event in the target room by applying the activity event identification rule 182 to the usage attributes of the target room, the group time periods classified in step S6, and the normalized number of people data. Furthermore, in step S8, the time schedule data creation unit 163 estimates the activity state for each inter-event time period according to the identified activity event, and creates time schedule data 186 indicating the activity state. An example of the activity event identification in step S7 will be described below.

[0095] 13 is a flowchart for explaining the details of the activity event identification process (step S7) in this embodiment. Note that the following steps S6 and S8 will also be mentioned here. First, as described above, in step S6, the time period classification unit 161 of the activity state estimation unit 16 allocates the normalized number of people data to one of the three group time periods of morning, noon, and afternoon for one day. In other words, the time period classification unit 161 of the activity state estimation unit 16 classifies the normalized number of people data into each group time period.

[0096] Furthermore, steps S701 to S713 corresponding to step S7 are executed. First, in step S701, the activity event identification unit 162 extracts any one of the number of people data from the number of people data classified in step S6. At this time, it is desirable that the time period classification unit 161 extracts the data in chronological order, but this order is not limited to this. Furthermore, in step S702, the activity event identification unit 162 determines whether the use attribute of the target room is an office type. To this end, the activity event identification unit 162 makes a determination using the use attribute data 181 that is the identification result of step S5. As a result, if the room is an office type (Yes), the process proceeds to step S703. If the room is not an office type (No), the process proceeds to step S712. Here, cases where the room is not an office type include a determination of a non-office type as in step S54 of FIG. 12A, as well as examples listed in FIG. 5, such as an unmanned type.

[0097] In step S703, the activity event identification unit 162 determines the group time period of the number of people data extracted in step S701. In this embodiment, it determines whether the time information of the number of people data corresponds to morning, daytime, or afternoon. As a result, if it is morning, the process proceeds to step S704. If it is daytime, the process proceeds to step S707. If it is afternoon, the process proceeds to step S709.

[0098] First, in step S704, the activity event identification unit 162 identifies the entry time of the first worker (entrant) into the target room, i.e., the first entry time, from the number of people data extracted in step S701. To do this, the activity event identification unit 162 determines the time (the time in FIG. 7, hereinafter referred to as "time") indicated by the first time information when the number of people entering the room is one or more from the extracted number of people data as the entry time of the first worker. Note that the extracted number of people data here is the number of people data for which the time is in the morning.

[0099] In step S705, the activity event identification unit 162 uses the extracted number-of-people data to identify a time (time in FIG. 7) when the number of people in the room exceeds a preset threshold. In this step, the activity event identification unit 162 may identify a time when the ratio of the number of people in the room to the capacity exceeds a predetermined value. The capacity included in the building management data can be used as this capacity, but the maximum number of people in the room for a predetermined period (e.g., one day or morning) in the past number-of-people data 184 may also be used as this capacity. It is desirable to use a representative value, such as the average value over multiple predetermined periods, as this maximum value. Then, in step S706, the activity event identification unit 162 identifies the time identified in step S705 as the start time of work, which is an activity event. Note that the threshold value may be, for example, 5% of the capacity, and this threshold can be used to identify work time before work starts or after work ends as an activity event.

[0100] It is desirable that the building equipment control unit 171 generates control commands corresponding to the pre-work hours and the post-work hours. For example, the building equipment control unit 171 generates control commands to activate or increase the output of equipment such as the air conditioning equipment 401 and the lighting equipment 402 during the pre-work hours and outputs them to the respective control devices. The building equipment control unit 171 also generates control commands to stop or decrease the output of the air conditioning equipment 401 and the lighting equipment 402 during the post-work hours and outputs them to the respective control devices. As with the pre-work hours and the post-work hours, control commands may also be generated for the start and end of work, the end and start of lunch breaks, the entry time of the first worker, and the exit time of the last worker. These may be executed during at least one time period (activity event). In other words, a control command is generated to activate or increase the output of equipment during an activity event in which the number of people present increases, and to stop or decrease the output of equipment during an activity event in which the number of people present decreases.

[0101] Steps S705 and S706 may be executed as follows. First, in step S705, the activity event identification unit 162 identifies the time when the number of people entering the number of people data is at a maximum or at a maximum value in the morning. Then, in step S706, the activity event identification unit 162 sets the identified time as the start time of work. Note that in step S706, the activity event identification unit 162 may set the start time as the time identified in step S705 plus a predetermined time, such as five minutes. Furthermore, instead of the number of people entering the room, the difference between the number of people entering and the number of people leaving the room or the number of people leaving the room may be used. When the number of people leaving the room is used, the minimum or minimum value of the number of people leaving the room is used instead of the maximum or maximum value described above.

[0102] Although the present embodiment uses the number of people entering the target room, it is also possible to use the number of people in the movement route to the target room, such as the hallway or elevator. Even when using these, the time when the number of people in the movement route exceeds a threshold can be used. Furthermore, the activity event identification unit 162 may accumulate the number of people in each time period along the movement route and treat this as the number of people in the target room. When using the movement route in this way, it is desirable to add a time longer than the time (5 minutes) added to the target room to take into account the movement time. As described above, activity events in the morning group time slot are identified. Next, the identification of activity events in the daytime group time slot will be described. Note that the predetermined time to be added can be adjusted depending on the situation. For example, if the identified activity events vary greatly from day to day, it is desirable to extend the time by 10 minutes or more. This also applies to the processes described below (both adding and reducing the predetermined time).

[0103] In step S707, the activity event identification unit 162 uses the extracted number-of-people data to identify the start time of the lunch break. To do this, the activity event identification unit 162 uses the number of people present and the number of people leaving the extracted number-of-people data. For example, the activity event identification unit 162 identifies, as the start time of the lunch break, the time when the number of people present falls below a preset threshold. The activity event identification unit 162 may also identify, as the start time of the lunch break, the time when the ratio of the number of people present to the capacity falls below a predetermined value. The capacity included in the building management data can be used as this capacity, but the minimum value of the number of people present during a predetermined period (e.g., daytime) in the past number-of-people data 184 may also be used as this capacity. It is desirable to use a representative value, such as the average value over multiple predetermined periods, as this minimum value. Furthermore, the activity event identification unit 162 may also identify, as the start time of the lunch break, the time when the number of people present begins to decrease, i.e., the time when the number of people leaving exceeds the number of people entering.

[0104] This step may be performed as follows. First, the activity event identification unit 162 identifies the time when the number of people leaving the room in the number-of-people data is at a maximum or a maximum value in the daytime. The activity event identification unit 162 determines the identified time as the start time of the lunch break. The activity event identification unit 162 may also determine the start time of the lunch break as the identified time minus a predetermined time, such as five minutes. Furthermore, instead of the number of people leaving the room, the difference between the number of people leaving and the number of people entering the room or the number of people entering the room may be used. When the number of people entering the room is used, the minimum or minimum value of the number of people entering the room is used instead of the maximum or maximum value described above. Furthermore, the activity event identification unit 162 may identify, as the end time of the lunch break, the time when the number of people present begins to increase, that is, the time when the number of people entering the room exceeds the number of people leaving the room.

[0105] Furthermore, in this embodiment, the number of people leaving the target room is used, but the number of people present along the movement route from the target room, for example, the number of people present in a hallway or elevator, may also be used. Even when these are used, the time when the number of people present along the movement route exceeds a threshold may also be used. Furthermore, the activity event identification unit 162 may accumulate the number of people present at each time along the movement route and treat this as the number of people present in the target room. In this way, when the movement route is used, it is desirable that the time to be subtracted from the identified time be longer than the time to be subtracted in the target room (5 minutes), taking into account the movement time.

[0106] In step S708, the activity event identification unit 162 uses the extracted number of people data to identify the time (time in FIG. 7) when the number of people in the room exceeds a preset threshold as the end of the lunch break. This step can be processed in the same way as steps S705 and S706, but the thresholds may be different. In this case, it is desirable to set the respective thresholds based on the trends of past number of people data.

[0107] Furthermore, in this step, similarly to steps S705 and S706, the activity event identification unit 162 may identify the time when the ratio to the capacity exceeds a predetermined value as the lunch break end time. Furthermore, this step may be executed as follows. First, the activity event identification unit 162 identifies the time when the number of people entering the room in the number-of-people data shows a local maximum or a maximum value in the daytime as the lunch break end time. Note that in this step, the activity event identification unit 162 may set the lunch break end time as the identified time plus a predetermined time, such as five minutes. Furthermore, instead of the number of people entering the room, the difference between the number of people entering and the number of people leaving the room or the number of people leaving the room may be used. When the number of people leaving the room is used, the minimum or minimum value of the number of people leaving the room is used instead of the maximum or maximum value described above.

[0108] Furthermore, in this embodiment, the number of people entering the target room is used, but the number of people present along the movement route to the target room, for example, the number of people present in the hallway or elevator, may also be used. Even when these are used, the time when the number of people present along the movement route exceeds a threshold may also be used. Furthermore, the activity event identification unit 162 may accumulate the number of people present at each time along the movement route and treat this as the number of people present in the target room. In this way, when the movement route is used, it is desirable that the time added to the time identified in step S705 be longer than the time (5 minutes) added to the target room, taking into account the movement time. In this way, activity events in the daytime group time slot are identified. Next, identification of activity events in the afternoon group time slot will be described.

[0109] First, in step S709, the activity event identification unit 162 identifies the end of workday time using the extracted number-of-people data. To this end, the activity event identification unit 162 identifies the time when the number of people leaving the room in the number-of-people data is at a maximum or a maximum value in the afternoon. The activity event identification unit 162 then determines the identified time as the end of workday time. In this step, the activity event identification unit 162 may determine the end of workday time by subtracting a predetermined time, such as five minutes, from the time identified in step S709. Furthermore, instead of the number of people leaving the room, the difference between the number of people entering and the number of people leaving the room or the number of people entering the room may be used. When the number of people entering the room is used, the minimum or minimum value of the number of people entering the room is used instead of the maximum or maximum value described above. Furthermore, the activity event identification unit 162 may identify, as the end of workday time, the time when the number of people present begins to decrease, that is, the time when the number of people leaving the room exceeds the number of people entering the room.

[0110] Furthermore, in this embodiment, the number of people leaving the target room is used, but the number of people present along the movement route to the target room, for example, the number of people present in the hallway or elevator, can also be used. When using these, the time when the number of people present along the movement route exceeds a threshold can be used. Furthermore, the activity event identification unit 162 can accumulate the number of people present at each time along the movement route and treat this as the number of people present in the target room. In this way, when using the movement route, it is desirable that the time to be subtracted from the time identified in step S710 be longer than the time to be subtracted for the target room (5 minutes), taking into account the movement time.

[0111] In step S710, the activity event identification unit 162 uses the extracted number-of-people data to identify a time when the number of people present in the room is equal to or less than a preset threshold. In this step, the activity event identification unit 162 may identify a time when the ratio of the number of people present to the room capacity is equal to or less than a predetermined value.

[0112] Note that step S710 may be executed as follows. The result can be used to determine the end of work time in step S709. That is, if the threshold value does not fall below a certain time after the end of work time identified in step S709, the activity event identification unit 162 identifies the time when the threshold value falls below the threshold value as the end of work time. Also, if the threshold value falls below the threshold value within the certain time, the activity event identification unit 162 determines the end of work time identified in step S709. Note that this step may be omitted.

[0113] Furthermore, in step S711, the activity event identification unit 162 identifies the time of exit of the last worker (entrant) from the target room, i.e., the last exit time, from the number of people data extracted in step S701. For this purpose, the activity event identification unit 162 determines the last time (time in FIG. 7) when one or more people have left the room from the extracted number of people data as the exit time of the last worker. As a result, activity events in the afternoon group time slot are identified. Therefore, activity events in the morning, midday, and afternoon group time slots are identified.

[0114] The identification of activity events in steps S704 to S711 executed as described above will be described using the graphs in Fig. 14, Fig. 15A, Fig. 15B, and Fig. 15C. First, Fig. 14 is a graph for explaining the identification of activity events in this embodiment. Fig. 14 shows the number of people entering and leaving the room by time (number of people data). Fig. 14 also shows the number of people entering and leaving the room by time (solid line: number of people entering, dashed line: number of people leaving). Above that, the group time periods of morning, noon, and afternoon are shown.

[0115] First, the processing for the morning group time period in steps S704 to S706 will be described. First, in the morning, the first number of people entering the room is measured around 6:00. In step S704, this time is identified as the time when the first worker entered the room. Next, just after 8:00, the maximum number of people entering the room in the morning is shown. Also, at this time, the number of people present, calculated using equation 1 using the difference between the number of people entering and the number of people leaving the room, also shows the maximum value.

[0116] Therefore, in steps S705 and S706, this time is identified as the start of work. Note that in the morning, the number of people entering the room exceeds the number of people present in the room from 8:00 to 11:30 (the entire morning), and the number of people present in the room also increases. Therefore, it can be seen that workers are present in the target room after the start of work. Therefore, the activity event identification unit 162 can estimate that no particular activity events have occurred after the start of work.

[0117] Next, the processing for the daytime group time period in steps S707 and S708 will be described. First, from 11:30, which is the start of the daytime period, to 12:00, the number of people leaving the room exceeds the number of people entering. Then, at 12:00, the number of people leaving the room reaches its maximum, and the number of people present falls below capacity. As a result, 12:00 is identified as the start time of the lunch break.

[0118] Additionally, at around 12:45, the number of people entering the room reaches the latest peak value (extreme value), and the number of people present exceeds the capacity. As a result, 12:45 is identified as the end of the lunch break. The latest peak value (extreme value) and time factors can also be used to identify activity events. This will be explained later. Note that the latest peak value here can also be recognized as the maximum value during the day. Furthermore, the latest refers to the busiest time of the day.

[0119] Finally, the processing for the afternoon group time period in steps S709 to S711 will be described. First, the maximum number of people leaving in the afternoon, that is, the latest peak value, is observed around 17:30. Therefore, in step S709, this time is identified as the end of work. Furthermore, from 16:00 to 20:00, the number of people leaving exceeds the number of people entering, and the number of people present decreases. As a result, in step S710, the time during this period when the number of people present falls below a preset threshold is identified. Furthermore, the last number of people leaving is measured around 21:00. In step S711, this time is identified as the time when the last worker left the room.

[0120] In the above steps S704 to S711, the activity event identification unit 162 estimates an activity event using extreme values ​​(peak values) related to the number of people data, such as maximum and minimum values. If multiple such extreme values ​​exist in a group time period, the activity event can be estimated as in (1) or (2) below. (1) The maximum value in the group time period is selected and used. (2) The order data of the activity event is stored in the storage unit 18, and the activity event is identified by comparing it with this. Hereinafter, (2) will be explained.

[0121] First, the storage unit 18 stores sequence data indicating a standard sequence in which activity events occur. Then, when the activity event identification unit 162 detects multiple peak values, it identifies activity events according to the sequence indicated by the sequence data. For example, assume that the sequence data is: work start time (peak value of number of people entering the room) - work start time - lunch break start time (peak value of number of people leaving the room) - lunch break end time (peak value of number of people entering the room). In this case, the activity event identification unit 162 identifies the time indicating the first detected peak value of number of people entering the room as the work start time.

[0122] In this embodiment, the data is classified into group time periods in step S703, and activity events are identified using the results. However, this step may be skipped and identification may be performed using sequence data. Furthermore, this embodiment may be performed using a learning function. For example, the activity event identification unit 162 learns the relationship between the characteristics of the number of people data, such as the increase / decrease trends in the number of people present or entering a room, and the activity events, and identifies the activity events according to these trends. This concludes the explanation of FIG. 14.

[0123] 15A, 15B, and 15C are graphs showing the relationship between activity events and number of people data in this embodiment. These graphs use the example of identifying an office-type room, with the vertical axis representing the number of people leaving the room and the horizontal axis representing the number of people entering the room, and each dot representing the number of people data extracted in step S701.

[0124] First, Figure 15A shows the relationship between activity events and headcount data for group time periods in the morning. Since Figure 15A shows headcount data for the morning, there are many people coming to work, and the overall number of people entering exceeds the number of people leaving, with more dots plotted below the dashed line than above. Groups of headcount data with a greater number of people entering are surrounded by dashed circles. Of these, the time information (element) of the headcount data with the largest number of people entering is identified as the start of work.

[0125] Next, Figure 15B shows the relationship between activity events and headcount data for group time periods during the day. Figure 15B shows daytime headcount data, which includes people going out for lunch and wearing their headphones when returning home, so the overall number of people entering and leaving the room is roughly the same. For this reason, the dots in Figure 15B are plotted at roughly the same level above and below the dashed line. In Figure 15B, the first group of headcount data, which includes a higher number of people leaving the room, and the second group of headcount data, which includes a higher number of people entering the room, are surrounded by dashed circles.

[0126] In the first group, the time information (element) of the number of people data with the largest number of people leaving the room is identified as the lunch break start time. In the second group, the time information (element) of the number of people data with the largest number of people entering the room is identified as the lunch break end time.

[0127] Finally, FIG. 15C shows the relationship between activity events and headcount data for groups in the afternoon time slot. Since FIG. 15C shows headcount data for the afternoon, there are many people leaving work, and the overall number of people leaving exceeds the number of people entering, with more dots plotted above the dashed line than below. Groups of headcount data with a greater number of people leaving are surrounded by dashed circles. Of these, the time information (element) of the headcount data with the largest number of people leaving is identified as the end of work. Note that FIGS. 15A to 15C may be displayed, for example, on the building management terminal device 405 as the processing results of this embodiment. This concludes the explanation up to step S711, and we return to FIG. 13 to explain step S712 and beyond.

[0128] In step S712, the activity event identification unit 162 identifies an activity event for a target room that is a non-office type. At this time, the activity event identification unit 162 uses the number of people data extracted in step S701, but identifies an activity event different from an office type. For example, in the case of an unmanned room, the activity event identification unit 162 identifies the work start time and the work end time based on the presence or absence of people in the room. In other words, the time when the first person entered the room is identified as the work start time, and the time when the last person entered is identified as the work end time.

[0129] In step S713, the activity event identification unit 162 determines whether processing of the number of people data classified in step S6 is complete. As a result, if it is completed (Yes), the process proceeds to step S8. If it is not completed (No), the process proceeds to step S701, where the next number of people data is extracted and subsequent processing is executed. Then, the activity event identification unit 162 stores each activity event identified in step S7 in the memory unit 18.

[0130] Then, in step S8, the time schedule data creation unit 163 estimates the activity state for each inter-event time period according to the activity event identified in step S7, and creates time schedule data 186 indicating at least the activity state. Details of step S8 will be explained below. FIG. 16 is a flowchart for explaining details of the activity state estimation process (step S8) in this embodiment. First, in step S801, the time schedule data creation unit 163 determines whether the usage attribute of the target room is office type. As a result, if it is office type (Yes), the process proceeds to step S802. If it is not office type (No), the process proceeds to step S806.

[0131] In step S802, the time schedule data creation unit 163 sets the "first worker's entry time," which is the activity event identified in step S704. That is, the "first worker's entry time" and the time are recorded in the time schedule data 186 shown in Fig. 10 (first record in Fig. 10).

[0132] Furthermore, in step S803, the time schedule data creation unit 163 sets "the time when the number of people in the room before work starts exceeds a threshold value," which is the activity event identified in step S705. This step can be realized by recording it in the time schedule data 186, just like step S802. The same applies to the following steps. Furthermore, in step S804, the time schedule data creation unit 163 sets "the start time of work," which is the activity event identified in step S706.

[0133] Then, in step S805, the time schedule data creation unit 163 determines the time period between the "time before work starts when the number of people in the room exceeds the threshold" set in step S803 and the "starting time" set in step S804 as an inter-event time period. The time schedule data creation unit 163 then estimates and sets the "time period before work starts" as the activity state of this inter-event time period. In this way, in this embodiment, the time schedule data creation unit 163 extracts activity events that are adjacent in chronological order.

[0134] Furthermore, the time schedule data creation unit 163 estimates the activity state during the inter-event time period, which is the time period between these activity events, in accordance with these activity events. This activity state indicates the content of the activity of the tenant in the room, and is estimated, for example, in accordance with the activity state estimation rules stored in the storage unit 18. This indicates the correspondence between two activity events and the activity state. The time schedule data creation unit 163 then records the estimated activity state in the time schedule data 186 shown in FIG. 10. The same process is performed in the following activity state setting steps.

[0135] Furthermore, in step S806, the time schedule data creation unit 163 sets "lunch break start time", which is the activity event identified in step S707. Then, in step S807, the time schedule data creation unit 163 estimates "morning work hours" as the activity state of the inter-event time period between the "work start time" set in step S804 and the "lunch break start time" set in step S806. This can be estimated in the same way as in step S805. The same applies to the following activity state estimation steps.

[0136] Furthermore, in step S808, the time schedule data creation unit 163 sets "lunch break end time," which is the activity event identified in step S708. Then, in step S809, the time schedule data creation unit 163 estimates "lunch break time zone" as the activity state of the inter-event time zone between the "lunch break start time" set in step S806 and the "lunch break end time" set in step S808. In response to this, the time schedule data creation unit 163 sets this activity state.

[0137] Furthermore, in step S810, the time schedule data creation unit 163 sets "end of work time," which is the activity event identified in step S709. Then, in step S811, the time schedule data creation unit 163 estimates "afternoon work time period" as the activity state of the inter-event time period between "end of lunch break" set in step S808 and "end of work time" set in step S810. In response to this, the time schedule data creation unit 163 sets this activity state.

[0138] Furthermore, in step S812, the time schedule data creation unit 163 sets "the time when the number of people in the room is below the threshold value," which is the activity event identified in step S710. Then, in step S813, the time schedule data creation unit 163 estimates "overtime hours" as the activity state for the inter-event time period between "end of work" set in step S810 and "the time when the number of people in the room is below the threshold value" set in step S12. In response to this, the time schedule data creation unit 163 sets this activity state.

[0139] Furthermore, in step S814, the time schedule data creation unit 163 sets "last worker's departure time," which is the activity event identified in step S711. Here, "last worker's departure time" is the last activity event of the day. Therefore, in step S815, the time schedule data creation unit 163 creates time schedule data 186 (FIG. 10) including the activity events and activity states set in steps S802 to S814. Note that the fact that "last worker's departure time" is the last activity event of the day can be determined, for example, by using the above-mentioned sequence data.

[0140] In step S816, the time schedule data creation unit 163 creates time schedule data 186 for the non-office type room according to the usage attribute. At this time, the time schedule data 186 may be created by estimating the activity events and the activity states in the time periods between the events, similar to the case of the office type room (similar to steps S802 to S815).

[0141] The relationship between the activity events and the activity states set in steps S802 to S815 executed as described above will be described using the graphs in FIGS. 17A and 17B. First, FIG. 17A is a graph showing the relationship between the activity events and the number of people present over time in this embodiment. While FIG. 14 shows the relationship between the activity events and the number of people entering and leaving the room, here the number of people present is used. FIG. 17A shows that each activity event is identified in chronological order according to the change in the increase or decrease in the number of people present. Specifically, the following are identified: "the time when the first worker enters the room," "the time when the number of people present before work begins exceeds the threshold," "the start of work," "the start of lunch break," "the end of lunch break," "the end of work," "the time when the number of people present after work ends is below the threshold," and "the time when the last worker leaves the room." In this way, the activity event identification unit 162 can identify activity events according to the change in the increase or decrease in the number of people present.

[0142] 17B is a graph showing the relationship between activity status and the number of people in a room over time in this embodiment. In FIG. 17B, time and the number of people in a room are shown on the same scale as in FIG. 17A. In FIG. 17B, the activity status of the inter-event time period between adjacent activity events in FIG. 17A is shown. Specifically, the "pre-work time period," "morning work time period," "lunch break time period," "afternoon work time period," and "overtime time period" are shown.

[0143] Here, the "pre-work time period" is from the "time when the first worker enters the room" or "the time when the number of people in the room before work starts exceeds a threshold" to the "work start time." The "morning work time period" is from the "work start time" to the "lunch break start time." The "lunch break time period" is from the "lunch break start time" to the "lunch break end time." The "afternoon work time period" is from the "lunch break end time" to the "work end time." The "overtime work time period" is from the "work end time" to the "time when the number of people in the room after work ends is below a threshold" or "the time when the last worker leaves the room."

[0144] Furthermore, the above-described activity events and activity states are merely examples, and are not limited thereto. At least some of them may be used, or other activity events and activity states may be added. In particular, for activity events, two or more activity events are identified in order to estimate the activity state. In this case, more preferably, "start time of work," "start time of lunch break," "end time of lunch break," and "end time of work" are identified. In this case, the activity states of "morning work hours," "lunch break hours," and "afternoon work hours" are estimated. Note that Figures 17A and 17B may be displayed, for example, on the building management terminal device 405 as the processing results of this embodiment.

[0145] This concludes the description of the estimation of activity states and the creation of the time schedule data 186. As described above, the time schedule data 186 is created at a predetermined cycle and stored in the storage unit 18. This data can then be used to realize daily building management. In the following, the processing for this building management will be described by returning to FIG. 11 and subsequent processing (step S9 and subsequent steps). Note that steps S8 and S9 may be executed continuously, or steps up to S8 may be executed once, and steps S9 and subsequent steps may be executed during daily management. Here, "continuously" means, for example, that steps up to S8 are executed at a predetermined time (e.g., 4:00 every morning) using past movement states, such as up to the previous day, and steps S9 and subsequent steps are executed on the current day.

[0146] In step S9, the command creation unit 17 creates commands for managing the building to be managed, according to the time schedule data 186 created in step S8. In this embodiment, control commands and management operation commands for controlling equipment are used as the commands. For this reason, the command creation unit 17 has a building equipment control unit 171 and a building management operation unit 172. First, the building equipment control unit 171 creates a control command according to the time schedule data 186 for the target room, and outputs the control command to each control device that controls the equipment.

[0147] For example, during morning and afternoon working hours when there are many people in the room, a control command is generated and output to control the room in an operation mode that prioritizes comfort, while during times when there are fewer people in the room, such as during the lunch break, a command is generated and output to control the room in an operation mode that prioritizes energy conservation.

[0148] The building management operation unit 172 also creates a management operation command in accordance with the created time schedule data 186 and outputs it to a management device such as the building security operation device 50. The management operation command may also be output to the building management terminal device 405. As a result, each of these devices displays the management operation command, allowing the user to confirm the displayed content. The management operation command may include the time schedule data 186 itself. As a result, the building manager or the like can formulate a policy for operating the building.

[0149] According to the above embodiment, time schedule data for a room such as a private area can be created with high accuracy based on the movement state. As a result, building owners and managers can improve the operation of the building. In addition, appropriate advice and guidance can be provided to these owners and managers. For example, a consulting company, which is a third party other than the owner or manager, can analyze the operation of the building and provide advice. This concludes the description of this embodiment, but the present invention is not limited to this.

[0150] For example, a facility may include areas other than buildings, and it is possible to estimate the activity state for various areas other than rooms. Furthermore, predetermined time schedule data (plan) may be prepared. In this case, the activity state estimation unit 16 may compare the time schedule data 186 estimated in this embodiment with the time schedule data 186, analyze the deviation, and manage whether activities (particularly work) are being carried out as scheduled. [Explanation of symbols]

[0151] 1... Building management system, 10... Building management device, 11... Room setting unit, 12... Movement state acquisition unit, 13... Number of people data calculation unit, 14... Normalization processing unit, 15... Usage attribute identification unit, 16... Activity state estimation unit, 17... Command creation unit, 18... Memory unit, 181... Usage attribute data, 182... Activity event identification rule, 183... Movement state data, 184... Number of people data, 185... Usage attribute identification data, 186... Time schedule data, 20... Elevator equipment, 21... Elevator group control device, 30...building patrol robot group, 31...robot group control device, 40...equipment group on each floor, 41...air conditioning equipment control device, 42...lighting equipment control device, 43...entrance / exit management device, 44...human presence sensor management device, 401...air conditioning equipment, 402...lighting equipment, 403...entrance / exit device, 404...human presence sensor, 405...building management terminal device, 50...building security operation device, 60...building cleaning operation device, 70...building cafeteria operation device, 80...common line

Claims

1. In the facility management device, a movement status acquisition unit that acquires movement status data indicating a movement status of a user in a first unit area within the facility, the movement status data being detected by the detection device; a number-of-people data calculation unit that calculates, from the movement status data, number-of-people data including at least one of the number of people staying in a second unit area within the facility and the number of people entering and leaving the second unit area, and further including time information; a usage attribute identification unit that identifies a usage attribute related to the use of the second unit area based on the number of people data; The facility management device further comprises an activity state estimation unit that estimates an activity state in the second unit area based on the number of people data and the usage attributes.

2. The facility management device according to claim 1, a schedule data creation unit that creates a time schedule indicating the activity state; a command creation unit that creates commands for managing the facility in accordance with time schedule information.

3. 3. The facility management device according to claim 2, The facility management device includes an equipment control unit, wherein the command creation unit outputs a control command to a control device that controls equipment in the facility.

4. 3. The facility management device according to claim 2, The facilities management device has an output unit that outputs the time schedule.

5. The facility management device according to claim 1, The facility has private areas and common areas, the first unit area is a unit area related to the exclusive portion, The second unit area is the exclusive portion. Facility management equipment.

6. The facility management device according to claim 1, The detection device is a facility management device installed in an elevator within the facility.

7. The facility management device according to claim 1, The activity state estimation unit The facility management device has an activity event identification unit that identifies activity events that occur within a predetermined period of time according to the number of people data.

8. The facility management device according to claim 7, the activity event identification unit identifies a plurality of activity events; A facility management device having a time schedule data creation unit that estimates activity states for each inter-event time period, which is a time period between adjacent activity events.

9. 9. The facility management device according to claim 8, the activity state estimation unit further includes a time period classification unit that classifies the calculated number of people data into group time periods that constitute a predetermined period, The activity event identification unit applies the usage attribute, the group time period, and the calculated number of people data to an activity event identification rule that indicates characteristics of the number of people data, thereby identifying the activity event.

10. 10. The facility management device according to claim 9, the activity event identification rule includes an identification rule that indicates a feature indicated by a comparison result of the number of people data for each time period condition that indicates the group time period, The activity event identification unit is a facility management device that identifies the activity event based on a comparison result of the calculated number of people data.

11. 10. The facility management device according to claim 9, the predetermined period is one day, The time zone classification unit classifies the number of people data for one day into morning, noon, and afternoon.

12. The facility management device according to claim 11, the facility has a plurality of rooms; When the usage attribute identification unit classifies the usage attribute of the second unit area into an office type, The activity event identification unit is a facility management device that identifies the multiple activity events as the time the first worker enters the room, the time when the number of people in the room exceeds a threshold before work starts, the start time of work, the start time of lunch break, the end time of lunch break, the end time of work, the time when the number of people in the room is below a threshold after work ends, and the time when the last worker leaves the room.

13. The facility management device according to claim 12, The activity event identification unit The group time zone is in the morning, the number of people entering is greater than the number of people leaving, and the time when the number of people entering reaches a peak value such as a maximum value is the start time. The group time period is daytime, after the start time, when the number of people leaving is greater than the number of people entering, and when the number of people leaving is at its maximum, the lunch break start time is the time The group time period is in the morning, after the start of the lunch break, when the number of people entering is greater than the number of people leaving, and the last time during that time when the number of people entering shows a peak value is the end of the lunch break. The facility management device identifies at least one of the following: the group time period is in the afternoon, after the end of lunch break, when the number of people entering is greater than the number of people entering, and the time when the number of people leaving is at its peak is the end of work.

14. The facility management device according to claim 12, Further, an equipment control unit for controlling equipment of the facility is provided, the activity event identification unit identifies, as the plurality of activity events, the entry time of the first worker, the time when the number of people in the room exceeds a threshold value before the start of work, the start time of work, the start time of a lunch break, the end time of a lunch break, the end time of work, the time when the number of people in the room is equal to or less than a threshold value after the end of work, and the departure time of the last worker; The equipment control unit creating a control command to start up the equipment or increase output at at least one of the time when the first worker enters the room, the time when the number of people in the room before the start of work exceeds a threshold, the start of work, and the end of the lunch break; A facilities management device that generates a control command to stop the equipment or reduce its output at at least one of the start time of the lunch break, the end time of work, the time when the number of people present in the room is below a threshold value after the end of work, and the time when the last worker leaves the room.

15. The facility management device according to claim 7, The activity event identification unit is a facility management device that identifies the activity event by comparing the number of people data with sequence data that indicates a standard sequence in which the activity event occurs.

16. A facilities management method executed by a facilities management device, a movement state acquisition unit acquires movement state data indicating a movement state of the user in a first unit area within the facility, the movement state data being detected by the detection device; a number-of-people data calculation unit calculates, from the movement state data, number-of-people data including at least one of the number of people staying in a second unit area within the facility and the number of people entering and leaving the second unit area, and further including time information; a usage attribute identification unit that identifies a usage attribute related to the purpose of the second unit area based on the number of people data; An activity state estimation unit estimates an activity state in the second unit area based on the number of people data and the usage attributes.

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