Facility management device and method

Abstract

A facility management device according to the present invention more appropriately manages the operation of a facility having a plurality of unit regions. A building management device (10) is provided with: a movement state acquisition unit (12) that acquires movement state data indicating the movement state of a user in a first unit region within a facility detected by a detection device; a number-of-people data calculation unit (13) that calculates, on the basis of the movement state data, number-of-people data including time information and at least one of the number of people staying in a second unit region within the facility and the number of people entering and exiting the second unit region; a utilization attribute recognition unit (15) that recognizes utilization attributes relating to the use of the second unit region on the basis of the number of people data; and an activity state estimation unit (16) that estimates an activity state in the exclusive unit on the basis of the number of people data and the use attribute, and generates time schedule data indicating the activity state.

Description

Technical Field

[0001] This invention relates to technology for managing facilities such as buildings. Background Technology

[0002] In recent years, diverse work styles have permeated every floor of office buildings (and every tenant residing on that floor). Examples of these styles include hybrid offices that combine in-person and home work, activity-based working, and flexible workstations. Therefore, operating the same equipment on all existing floors is sometimes not suitable for practical situations.

[0003] The equipment in the facility is operated centrally. For example, by centrally controlling the lighting in corridors and the operation of elevators, energy-saving control and efficient equipment operation can be achieved. In such management, the number of users in the facility (e.g., office staff or visitors) increases or decreases over time, so it is necessary to keep track of the number of people present in order to conduct appropriate control.

[0004] Patent document 1 discloses a device for predicting the number of people present, "including: a feature extraction unit 12, which is used to detect a specified time period from the history of the number of people present on the day of prediction and extract the feature of the detected specified time period; and a number of people present prediction unit 13, which is used to predict the number of people present after a specified time based on the difference between the feature extracted by the feature extraction unit 12 and the feature of the specified time period extracted from the current change model."

[0005] Existing technical documents

[0006] Patent documents

[0007] Patent Document 1: Japanese Patent Application Publication No. 2018-26028 Summary of the Invention

[0008] The technical problem that the invention aims to solve

[0009] Here, the building and other facilities include multiple unit areas such as rooms, corridors, and entrances. Furthermore, multiple tenants, including businesses, reside within the facility. The number of people present in the facility is a controllable factor. Therefore, effective control of the equipment is impossible when it is difficult to ascertain the number of people present. Thus, the technical problem of this invention is to more appropriately manage the operation of facilities with multiple unit areas.

[0010] Technical means for solving technical problems

[0011] To address the aforementioned problems, this invention estimates the activity status of a second unit area for each time period based on the movement status related to the movement of users in a first unit area of ​​the facility. Based on this estimation result, facility management can be achieved. More preferably, the activity status in the private unit is estimated using the movement status of the common areas as the movement status.

[0012] More specifically, the facility management device includes: a movement status acquisition unit that acquires movement status data representing the movement status of users in a first unit area within the facility as detected by a detection device; a people data calculation unit that calculates people data based on the movement status data, the 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 also including time information; a utilization attribute identification unit that identifies utilization attributes related to the use of the second unit area based on the people data; and an activity status estimation unit that estimates the activity status in the second unit area based on the people data and the utilization attributes.

[0013] Furthermore, the present invention also includes a facility management method executed by the facility management device, a program for using the facility management device as a computer, and a storage medium for storing the program. Additionally, the present invention also includes a facility management system including the facility management device.

[0014] Invention Effects

[0015] According to the present invention, the operation of a facility comprising multiple unit areas can be more appropriately managed. Attached Figure Description

[0016] Figure 1 This is a diagram illustrating a summary of one embodiment of the present invention.

[0017] Figure 2 This is a functional block diagram of a building management device 10 according to one embodiment of the present invention.

[0018] Figure 3 This is a system structure diagram of a building management system 1 according to one embodiment of the present invention.

[0019] Figure 4 This is a hardware structure diagram of a building management device 10 according to one embodiment of the present invention.

[0020] Figure 5 This is a diagram illustrating the use of attribute data 181 in one embodiment of the present invention.

[0021] Figure 6 This is a diagram illustrating activity event identification rule 182 used in one embodiment of the present invention.

[0022] Figure 7 This is a diagram illustrating the number of people data 184 used in one embodiment of the present invention.

[0023] Figure 8A This is a chart showing the number of people (number of attendees) used in one embodiment of the invention.

[0024] Figure 8B This is a chart showing the number of people (number of people leaving) used in one embodiment of the invention.

[0025] Figure 9A This is a diagram illustrating the use of attribute identification data 185 in one embodiment of the present invention.

[0026] Figure 9B This is a diagram illustrating the use of attribute identification data 185 in one embodiment of the present invention.

[0027] Figure 10 This is a diagram illustrating the timing data 186 used in one embodiment of the invention.

[0028] Figure 11 This is a flowchart illustrating the processing flow in one embodiment of the present invention.

[0029] Figure 12A This is a flowchart illustrating a specific example 1 of the attribute-based identification process (step S5) in one embodiment of the present invention.

[0030] Figure 12B This is a flowchart illustrating a specific example 2 of the attribute identification process (step S5) in one embodiment of the present invention.

[0031] Figure 13 This is a flowchart illustrating the details of the activity event identification process (step S7) in one embodiment of the present invention.

[0032] Figure 14 This is a diagram used to illustrate the identification of activity events in one embodiment of the present invention.

[0033] Figure 15A This is a chart illustrating the relationship between activity events and number of people in one embodiment of the present invention (grouped by time period: morning).

[0034] Figure 15BThis is a chart illustrating the relationship between activity events and number of people in one embodiment of the present invention (grouped by time period: noon).

[0035] Figure 15C This is a chart illustrating the relationship between activity events and number of people in one embodiment of the present invention (grouped by time period: afternoon).

[0036] Figure 16 This is a flowchart illustrating the details of the process for estimating the activity state (step S8) in one embodiment of the present invention.

[0037] Figure 17A This is a graph illustrating the relationship between an event and the number of people present over time, according to one embodiment of the present invention.

[0038] Figure 17B This is a graph showing the relationship between the activity status and the number of people present over time in one embodiment of the present invention. Detailed Implementation

[0039] The following describes embodiments of the present invention. As described above, in the present invention, the activity status of each time period in the second unit area is estimated based on the movement status related to the movement of users in the first unit area. Based on this estimation result, facility management can be achieved. Here, it is preferable to use the movement status of a common area, which is an example of the first unit area, as the movement status to generate the time schedule of a private area, which is an example of the second unit area. Here, the first unit area can be any unit area associated with the second unit area, and can be a unit area other than the common area. For example, it can be another private area. In this embodiment, an office building with multiple tenants is taken as the object. Here, when the facility includes private areas dedicated to tenants and common areas shared by various tenants, it is particularly difficult for the facility manager to grasp the number of people present in the private areas. Therefore, it is preferable to use the movement status of the common areas as the movement status to generate the time schedule of the private areas. In this embodiment, the private areas correspond to floors or rooms or areas such as offices and meeting rooms that constitute floors, while the common areas correspond to corridors, entrances, elevator lobbies, or elevator cars. In addition, air conditioning equipment and lighting equipment in the office rooms and corridors can be used as equipment. In addition, as another example of equipment, various robots such as patrol robots, cleaning robots, and reception robots are also included. Furthermore, equipment can be devices that span multiple floors or are controlled throughout the entire facility, such as energy storage devices in elevators or facilities.

[0040] Furthermore, in this embodiment, as an example of a movement state, the number of people entering, the number of people leaving (entry and exit) and the number of people present are used, but either entry and exit or the number of people present can also be used. Furthermore, a movement state refers to the movement-related actions and states of users such as office workers entering, leaving, or being present. Furthermore, being present is an example of a state of staying within a designated unit area, such as being seated or present. Therefore, the number of people present is an example of the number of people staying. Furthermore, in this embodiment, entry and exit (entry and exit) refers to entering and leaving (entering and leaving) within a unit area, and its object is not limited to a room. Hereinafter, the detailed content of this embodiment will be described in sections: <Overview>, <Structure>, <Information and Data>, and <Processing Flow>.

[0041] <Summary>

[0042] First, using Figure 1 This will be used to explain the outline of this implementation method. Figure 1 This is a diagram used to illustrate the general outline of this embodiment. Figure 1 In this embodiment, it is assumed that Company A, Company B, and Company C are tenants residing in the building that is the subject of this implementation. Furthermore, each company's occupancy is as follows.

[0043] Company A: Office 1 on the first floor.

[0044] Company B: Office 2 on the first floor.

[0045] Company C: The entire 2nd floor.

[0046] Moreover, these three companies, according to Figure 1 The schedule shown is for business and other activities. In the case of Company A, as activity events, (1) 7:00 is the time for unlocking and starting equipment (the time when employees first enter), (2) 9:00 is the start time for work, (3) 12:00 is the start time for lunch break, (4) 13:00 is the end time for lunch break, (5) 17:00 is the time for leaving get off work, and (6) 20:00 is the time for locking and stopping equipment. Here, the time for unlocking and starting equipment indicates the time when the entrance to office 1 in Company A is unlocked and the equipment is started. Alternatively, the time when employees first enter can be used instead. In addition, the time for locking and stopping equipment indicates the time when the entrance to office 1 in Company A is locked and the equipment is stopped. Alternatively, the time when employees last leave (the time when the last person leaving get off work leaves) can also be used instead. In addition, Company A has set a latest time to leave work, which corresponds to the time for locking and stopping equipment.

[0047] Because of these events, in Company A, the time intervals between events (inter-event time intervals) represent the following activity states: (1)-(2) pre-work time interval, (2)-(3) office hours (morning), (3)-(4) lunch break time interval, (4)-(5) office hours (afternoon), (5)-(6) overtime time interval, (6)-the next day's (1) no-work time interval. Here, no-work time interval means that there are no office workers in office 1.

[0048] Here, as mentioned above, building management typically lacks access to these activity events and their status. Therefore, to estimate activity events and status, the inventors of this application use the number of people entering and leaving the premises, the number of people present, and the time associated with the movement of office staff. For example, if there are many people entering in the morning, it can be estimated that office staff are entering for work. Therefore, this time can be identified as the work start time. Additionally, the period between the start of work and the start of lunch break can be estimated as the office hours (morning). In this way, a system can be generated... Figure 1 The schedule shown.

[0049] In addition to the number of people entering and exiting, the number of people present, and the time, activity events can also be inferred using equipment actions. For example, by observing the latest locking and unlocking of entrance and exit gates, the unlocking and locking times can be identified as activity events. Furthermore, by observing the power on / off of equipment such as air conditioners, the equipment start-up and stop times can be identified as activity events. Moreover, the periods between these times can be inferred as unoccupied time periods when office staff are not present. As a result, it is possible to infer... Figure 1 The schedule shown.

[0050] In addition, Company B's activity events were identified as (1) 8:30 as the start time, (2) 11:45 as the start time of lunch break, (3) 12:30 as the end time of lunch break, (4) 16:00 as the end time of get off work, (5) 19:00 as the start time of rest, and (6) 19:30 as the end time of rest. Compared with Company A, this does not include the unlocking and equipment start time and the locking and equipment stop time, but adds the rest start time and rest end time. Company B does not set a latest end time, so the unlocking and equipment start time and the locking and equipment stop time are omitted. However, even without setting a latest end time, the departure time of the last person to leave can be identified as an activity event. In addition, Company B sets rest during overtime, so the rest start time and rest end time are identified. These can be identified by the number of people traveling between the office and the rest room. For example, it can be identified by the number of people leaving the office and entering the rest room, and the number of people leaving the rest room and entering the office. In addition, the number of people present in the lounge can be used.

[0051] As a result, the activity status of Company B can be estimated as (1)-(2) office hours (morning), (2)-(3) lunch break, (3)-(4) office hours (afternoon), (4)-(5) overtime hours, (5)-(6) rest hours, and (6)-the next day's (1) overtime hours.

[0052] Furthermore, at Company C, similar activity events with different times than those at Company A can be identified, and the activity status can be inferred based on this. This is because Company C uses a similar office schedule to Company A, differing only in time. Based on the above idea, in this embodiment, activity events are identified, and the activity status is inferred for each of the time intervals between events, which are the time intervals between activity events.

[0053] <Structure>

[0054] Next, according to Figure 1 The idea will illustrate the structure used to identify active events and estimate the state of activity over time intervals between each event. Figure 2 This is a functional block diagram of the building management device 10 according to this embodiment. Below, we will use a building as an example of a facility and describe its management as an example. Furthermore, the building management device 10 is an example of a facility management device, and the present invention can be applied to other facilities such as shopping malls and service areas. Figure 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 utilization attribute recognition unit 15, an activity status estimation unit 16, an instruction generation unit 17, and a storage unit 18 for storing utilization attribute data 181, etc.

[0055] First, the room setting unit 11 sets the rooms that are the objects to be presumed to be active. To this end, the room setting unit 11 receives room designations from users of the building management device 10 and determines (sets) the rooms as the objects to be managed based on these designations. Furthermore, a room is an example of a unit area constituting a facility including a building; a unit area includes floors, zones, rooms, shops, etc. However, the following description will primarily use a room as an example. Therefore, the room setting unit 11 can also be referred to as a unit area setting unit, for example, receiving unit area identification information such as floors, zone names, and room names. Additionally, rooms and zones include offices, meeting rooms, corridors, elevator lobbies, restrooms, lounges, and elevator cars, which constitute floors.

[0056] Furthermore, the room setting unit 11 can assign rooms based on building information representing the configuration of floors, areas, and rooms of the building being managed. The building information is stored in the storage unit 18. Using this building information, the room setting unit 11 can set unit areas according to their configuration locations. For example, it extracts each floor of the building starting from the lower floors, and also extracts the floors and the rooms contained within those floors. Then, the room setting unit 11 sets unit areas representing rooms, etc., according to the extraction order. Furthermore, the building information stores activity events, activity states, etc., for each unit area, and the room setting unit 11 sets unit areas where activity events or activity states change as objects. Additionally, the following will describe this embodiment using an example of using a room as a unit area.

[0057] Furthermore, the movement status acquisition unit 12 acquires movement status data, which represents the movement status detected by the detection device related to the movement of users (hereinafter referred to as office workers) within the building that is under management. Preferably, the movement status includes at least one of office workers entering and leaving rooms and being present in rooms, and the movement status data includes at least one of the number of people entering and leaving and the number of people present. Additionally, as movement status data, data indicating "present" entry and exit can be used.

[0058] Here, the detection device detects the target room, more preferably, the movement status of building users in rooms associated with private areas. These private areas are preferably common areas that can serve as movement paths for private areas, but can also be the private areas themselves. Furthermore, the detection device includes a people sensor such as a human body sensor and an entry / exit device. Additionally, the detection device includes sensors installed in elevators, such as load sensors and elevator control devices for detecting floor stops. Furthermore, an attendance management device for detecting login or logout on PCs used in offices can be used as the detection device. It is also desirable to include the detection or acquisition time in the movement status data. For example, the number of people entering / exiting and the number of people present at each predetermined interval (e.g., 5 to 15 minutes). As a result, the movement status acquisition unit 12 outputs movement status data. More preferably, the detection device detects movement status in common areas of the building. In this case, it is preferable to install the detection device in a common area.

[0059] Furthermore, the personnel data calculation unit 13 calculates personnel data based on movement status data. This personnel data is time-series data representing at least one of the number of office workers present in the room and the number of people entering and leaving the room, and includes time information. Here, the room is preferably a room related to the dedicated department. Rooms related to the dedicated department include the dedicated department itself and the movement paths leading to the dedicated department (corridors, elevators, etc.). As a result, movement status data and personnel data related to the dedicated department can be used.

[0060] Furthermore, movement status data can be used as people-to-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. Additionally, the detection device can output the number of people entering and leaving or the number of people present and use it as movement status data or people-to-people data. By using such people-to-people data, it is possible to grasp the number of people in proprietary areas that are difficult to track.

[0061] Furthermore, when the number of people present is included in the headcount data, the headcount data calculation unit 13 calculates the number of people present using the following (mathematical formula 1).

[0062] Number of people present (t) = Number of people entering (t) - Number of people leaving (t) + Number of people present (t - Δt)... (Mathematical formula 1)

[0063] Here, t is the object time, and t-Δt is the time before one period (interval) of t (e.g., Δt is 5 minutes). Furthermore, it is preferable that the motion status acquisition unit 12 and the people data calculation unit 13 perform the processing periodically, such as daily or weekly. In this case, the people data calculation unit 13 resets the people data (e.g., the number of people present) to a predetermined value (e.g., zero) in each period. As a result, people data as time-series data is output from the people data calculation unit 13. Additionally, motion status data is preferably time-series data, but is not limited to it. If the motion status data is not time-series data, the people data calculation unit 13 can determine the time information used to form the time-series data by using the timing of receiving the motion status data.

[0064] Furthermore, the normalization processing unit 14 performs normalization processing on the number of people data. Generally, the number of people each room can accommodate, such as its capacity and volume, varies. Therefore, if the number of people data is used directly, the accuracy of processing such as the identification of activity events described later may be reduced. Therefore, for example, by normalizing the number of people data within a range of 0 to 1, the number of people data for each room can be processed in a common (uniform) manner. This normalization includes dividing the number of people represented by the number of people data (e.g., number of people entering, number of people leaving, number of people present) by the maximum value within a specified period. For the maximum value, the average of the maximum values ​​of each day over the past month can be used. Additionally, if the rooms constituting the building have a uniform structure, the normalization processing can be omitted. The above results in normalized number of people data being output from the normalization processing unit 14.

[0065] Furthermore, the attribute recognition unit 15 identifies usage attributes related to the purpose of the target room based on the number of people. Additionally, the attribute recognition unit 15 can limit the identification of usage attributes to private rooms. This is because shared rooms can also be managed by the building management to some extent. Furthermore, it is preferable to use a normalized number of people when identifying usage attributes using the attribute recognition unit 15, but the number of people calculated by the number of people calculation unit 13 can also be used.

[0066] Here, the attribute is used to represent the type of room based on its purpose. For example, it can be identified as a main type such as office, meeting room, cafeteria, shared lounge for building tenants, lobby, parking lot, or equipment-dedicated floor for large equipment. Additionally, the attribute can be a residential type such as an apartment, a shop type such as a convenience store, or a lodging facility type such as a hotel. Furthermore, the attribute identification unit 15 identifies the usage attribute based on the characteristics of the number of people present in the room throughout the day and the amount of data on that number.

[0067] Furthermore, as a feature of the number of people data, for example, a pattern (trend, degree) of the change in the number of people over a specified period of time, such as a day, can be used. In this embodiment, the feature of the number of people data is referred to as utilization attribute identification data 185. In this embodiment, utilization attribute identification data 185 is stored in the storage unit 18. As a result of the above, utilization attribute identification data representing the utilization attributes of the room is output from the utilization attribute identification unit 15. In addition, this utilization attribute is stored in the storage unit 18 as utilization attribute data 181. Furthermore, details of utilization attribute data 181 and utilization attribute identification data 185 will be described in the <Information and Data> section.

[0068] Furthermore, the activity status estimation unit 16 estimates the activity status of each time period between events in the room based on the number of people and by utilizing attributes, and generates time schedule data representing that activity status. Here, the time period between events refers to the time period between adjacent events in the activity events described later. In addition, the activity status represents the type of activity in the room, such as business activities; for example, as an office-type activity status, office hours, lunch break hours, and overtime hours can be listed.

[0069] Furthermore, in order to generate schedule data, the activity status estimation unit 16 includes a time period classification unit 161, an activity event identification unit 162, and a schedule data generation unit 163. These components will be explained below. Additionally, the number of attendees here is either the number of attendees entering (t), the number of attendees leaving (t), or the number of attendees present (t) at time t, or the number of attendees entering (t) and the number of attendees leaving (t) at time t.

[0070] First, the time period classification unit 161 classifies the number of people data according to the time information of the data, categorizing it into grouped time periods that constitute a specified period. That is, the time period classification unit 161 assigns each piece of data to a grouped time period. For example, the time period classification unit 161 classifies a day as a specified period into three grouped time periods: morning, noon, and afternoon. As an example, it can be classified as morning from 00:00 to 11:00, noon from 11:00 to 14:00, and afternoon from 14:00 to 24:00. Through such classification, the characteristics of the activities in each grouped time period can be used to identify the next activity event.

[0071] Furthermore, the activity event identification unit 162 applies activity event identification rules 182 to the data using attributes, grouped time periods, and number of people, and identifies activity events in the room corresponding to the number of people data. At this time, activity event identification rules 182 show the characteristics of the number of people data. For example, it shows the characteristics shown by comparing the number of people data with each other (size comparison, etc.) and the characteristics on the time series (maximum value, extreme value, trend of change, degree, etc.). Details of activity event identification rules 182 will be explained in the <Information and Data> section.

[0072] The processing of the activity event identification unit 162 will be explained below. For example, when the conditions of the floor attribute being office type, the time period being morning, and the number of people entering being greater than the number of people leaving being met, and the time information of the number of people entering being the maximum is identified as the working time. Here, the time information of the people data is the time element in the time series data, such as showing the detected time, the generated time, or the acquired time. Furthermore, when the conditions of the utilization attribute also being office type, the grouped time period being noon, and the number of people leaving being greater than the number of people entering being met, and the time information of the number of people leaving being the maximum is identified as the lunch break start time. In this way, based on the utilization attribute, the people data of this grouped time period can be used to identify activity events in the room.

[0073] Furthermore, the time scheduling data generation unit 163 estimates the activity status of the time period between each event based on the identified activity events. Then, the time scheduling data generation unit 163 generates time scheduling data representing the activity status. In addition, the time scheduling data includes at least the activity status, and more preferably, it also includes the activity events.

[0074] To this end, the time schedule data generation unit 163 uses the times of each identified activity event to estimate the time schedule of the target room's activity status on the timeline. For example, in the case of using the attribute "office type," based on the identified start time and lunch break start time, the activity status of the time period between these two events is estimated to be morning office work. By performing this estimation in a chain for each activity event, time schedule data representing the activity status within a specified period of day can be generated. The time schedule data generated in this way represents the activity status of each time period between events. Therefore, the time schedule data also divides time periods for each activity status. As a result, the activity status estimation unit 16 outputs the time schedule data.

[0075] Furthermore, the instruction generation unit 17 generates instructions for managing the building, which is the object of management, based on the generated schedule data. These instructions include control instructions for equipment and management operation instructions for managing and operating the building. Therefore, the instruction generation unit 17 includes a building equipment control unit 171 and a building management operation unit 172. Alternatively, only one of the building equipment control unit 171 and the building management operation unit 172 may be provided, or both may be omitted.

[0076] First, the building equipment control unit 171 generates control instructions based on time-scheduling data for controlling equipment such as air conditioning, lighting, elevators, and robots related to the target room. Here, room-related equipment includes equipment installed in or circulating within the corresponding room, as well as equipment used by office workers in that room. An example of equipment used by office workers is an elevator used for entering and exiting the room.

[0077] Then, the building equipment control unit 171 outputs control commands to each control device of the control equipment. Furthermore, as control commands, examples include commands that, for a specific room, control the air conditioning and lighting in a comfort-oriented operating mode during office hours, and in an energy-saving operating mode during other active periods. Moreover, it is preferable to control the air conditioning and lighting to stop during periods of inactivity. In this way, by using the time schedule data of each room, equipment control for that room can be appropriately implemented according to its activity status, achieving efficient operation.

[0078] Furthermore, the building management and operation unit 172 generates management operation instructions based on time schedule data to implement building management and operation, such as security, cleaning, and cafeteria operation related to target rooms. Then, the building management and operation unit 172 outputs the management operation instructions to various management devices. These instructions are applied to the security patrol plans, cleaning arrangements, and cafeteria operation schedules for each room in the building within the various management devices. For example, management operation instructions are generated to implement cleaning of corridors and restrooms in common areas of target floors during office hours with fewer people entering and exiting, and to adaptively adjust the amount of food prepared in the cafeteria according to the lunch break times of each floor. This allows for efficient cleaning during periods with fewer people, or effective adjustment of the amount of food prepared according to changes in the number of people during lunch break. As described above, the building management device 10 in this embodiment cooperates with control devices, management devices, etc., to manage and operate the building. Therefore, the building management system 1, which includes control devices, etc., in addition to the building management device 10, will be described below.

[0079] Figure 3This is a system structure diagram of the building management system 1 in this embodiment. In the building management system 1, the building management device 10 is connected to other devices, including the control device and management device, which are the output destinations of the aforementioned instructions, via the public line 80 of the building's information network. Furthermore, various devices controlled by the control device according to control instructions are connected to the control device. To perform this control, for example, the control device outputs control signals to the devices according to the control instructions.

[0080] The following describes these contents. First, as equipment, elevator equipment 20, a group of circulating robots 30 within the building, and equipment groups 40 on each floor are installed and operate within the building. Elevator equipment 20 includes a car, drive unit, etc., and transports people and goods. Furthermore, as an example of a control device, an elevator group control device 21 for controlling operation is connected to elevator equipment 20. Elevator group control device 21 receives an elevator group control command, as an example of a control command, from building management device 10, and controls elevator equipment 20 according to the elevator group control command. In addition, elevator group control device 21 outputs operating data, including load, stopping floors, etc., from elevator equipment 20 to building management device 10. Therefore, the operating data itself, or at least a portion thereof, can be considered as movement state data.

[0081] Furthermore, the building-inbound roving robot group 30 is a robot group that autonomously roams within the building (facility) and performs security and guidance functions. However, this does not necessarily require a group; it can also be a single robot. Additionally, as an example of a control device, a robot group control device 31 for controlling operation is connected to the building-inbound roving robot group 30. The robot group control device 31 receives robot group control commands, as an example of control commands, from the building management device 10 and controls the building-inbound roving robot group 30 according to these commands. Furthermore, human body sensors can be installed in the building-inbound roving robot group 30, and the robot group control device 31 outputs motion data corresponding to the detection results from the building-inbound roving robot group 30 to the building management device 10. Moreover, the building-inbound roving robot group 30 and the robot group control device 31 can be integrated into one unit.

[0082] In addition, the equipment groups 40 on each floor are equipment installed on separate floors of the building. Furthermore, in Figure 3 In the diagram, examples of equipment groups 40 for each floor include air conditioning equipment 401, lighting equipment 402, access control 403, human body sensor 404, and building management terminal device 405. Additionally, the branch numbers in the figures indicate floors. Furthermore, although in Figure 3The document describes the equipment on each floor. This equipment may be installed in each room or in a specific area, such as a section of rooms. The following will describe each piece of equipment and the control devices used to control it.

[0083] First, air conditioning unit 401-X is an air conditioning unit installed on floor X. Furthermore, an air conditioning unit control device 41, serving as an example of a control device, is connected to air conditioning unit 401-X. Air conditioning unit control device 41 receives an air conditioning control command, serving as an example of a control command, from building management device 10, and controls air conditioning units 401 such as air conditioning units 401-X and 401-Y according to the air conditioning control command. Additionally, a human body sensor can be installed in air conditioning unit 401, and the air conditioning unit control device 41 can output motion data corresponding to the detection result from air conditioning unit 401 to building management device 10.

[0084] Furthermore, lighting equipment 402-X is a lighting equipment installed on the X floor. Also, a lighting equipment control device 42, serving as an example of a control device, is connected to lighting equipment 402-X for controlling its operation. The lighting equipment control device 42 receives a lighting control command, serving as 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, according to the lighting control command. Additionally, a human body sensor can be installed in the lighting equipment 402, and the lighting equipment control device 42 can output motion data corresponding to the detection result from the lighting equipment 402 to the building management device 10.

[0085] Furthermore, the entry / exit device 403-X is an entry / exit device installed on the X floor. This entry / exit device 403-X is installed on the door of the room or serves as a gate. The entry / exit management device 43 is connected to the entry / exit device 403-X. The entry / exit management device 43 can output the number of people entering / exiting, as an example of movement data corresponding to the detection results of entry / exit devices 403 such as entry / exit devices 403-X and 403-Y, to the building management device 10. Additionally, the entry / exit management device 43 can also be used as a control device. That is, near working hours, based on operating data from the elevator group control device 21, it predicts the entry of people and, based on this, performs controls such as unlocking the entry / exit device 403.

[0086] In addition, human body sensor 404-X is a human body sensor installed on floor X. Human body sensor 404-X can be installed near room entrances or near office spaces such as desks. Human body sensor management device 44 is connected to human body sensor 404-X. Human body sensor management device 44 can output the number of people present, as an example of movement data corresponding to the detection results of human body sensors 404 such as human body sensors 404-X and 404-Y, to building management device 10.

[0087] Furthermore, building management terminal devices 405, such as 405-X and 405-Y, are terminal devices used by building managers or tenant supervisors, and can be implemented using PCs, tablets, etc. For this purpose, the building management terminal devices 405 output the operating rules of the equipment in the building and each tenant to the building management device 10. In addition, the building management terminal devices 405 receive instructions from managers or others regarding the generation of schedule data or the setting of rooms, as per this embodiment. Furthermore, in addition to operating information indicating the operating status of equipment, the building management terminal devices 405 also receive and display information and data processed by the building management device 10, such as schedule data.

[0088] Additionally, the building management terminal 405 can be located 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 405 can be used to manage multiple buildings. Furthermore, the building management device 10 can be located within multiple buildings or facilities. In this case, the building management device 10 can be implemented through cloud computing. Moreover, the building management terminal 405 can be used by the building management in a limited manner.

[0089] In addition, the building security operation device 50, the building cleaning operation device 60, and the building cafeteria operation device 70 are respectively used by the building's security personnel for security operations, cleaning operations, and cafeteria operations. Therefore, they receive security operation information, cleaning operation information, and cafeteria operation information from the building management device 10 as management operation instructions. As a result, security operations can be appropriately performed based on the number of people present. For example, security or cleaning can be performed during periods when there are fewer people present in the corresponding rooms. Furthermore, for the cafeteria, operating hours can be set to fully cover the lunch break periods of all tenants. Figure 1In this example, the cafeteria is allowed to operate between 11:45 AM and 1:00 PM. Additionally, ingredients can be prepared and cooked according to the number of people present. Furthermore, the building security operation system 50, the building cleaning operation system 60, and the building cafeteria operation system 70 can each be a subsystem or system comprising these devices.

[0090] Furthermore, PCs used by office staff can be used as equipment, and attendance management devices can be used as their control devices. For example, if it is nearing the end of the workday or the time for locking up and stopping equipment, the attendance management device receives a control command to display a message reminding employees to leave the workday on the PC. Upon receiving this control command, the attendance management device takes control to display the corresponding message on the PC. Additionally, the attendance management device can receive login / logout status from the PC and measure the number of people present. This data can then be used as movement status data.

[0091] Next, an embodiment of the building management device 10 that performs the main processing of this embodiment will be described. Figure 4 This is a hardware structure diagram of the building management device 10 according to this embodiment. In this embodiment, the building management device 10 can be implemented in a server, which is an example of a computer, and particularly in the cloud. Moreover, as... Figure 4 As shown, the building management device 10 includes a processing device 101, a communication device 102, a memory 103, and an auxiliary storage device 104, which are interconnected through a communication path.

[0092] First, the processing device 101 can be implemented by a processor such as a CPU, and performs calculations according to the building management program 105 stored in the auxiliary storage device 104 (described later). The building management program 105 will be described below. Furthermore, the communication device 102 has the function of communicating with other devices via the public line 80. It corresponds to... Figure 2 The input and output sections are not shown in the diagram. Specifically, the building management device 10 may be equipped with an input section for receiving input from users of the building management device 10 or an output section for outputting information. In this case, these can be implemented using input devices such as a keyboard or display devices such as a monitor.

[0093] Furthermore, memory 103 and auxiliary storage device 104 correspond to Figure 2The storage unit 18. Furthermore, in the memory 103, the building management program 105 stored in the auxiliary storage device 104 and the information used for processing in the processing device 101 are expanded. In addition, the auxiliary storage device 104 can be implemented as a so-called memory, storing the building management program 105, utilization attribute data 181, activity event identification rules 182, movement status data 183, number of people data 184, utilization attribute identification data 185, and schedule data 186. These will be explained later in the <Information and Data> section. Furthermore, the auxiliary storage device 104 can be implemented using various storage media such as external HDDs (hard disk drives), SSDs (solid state drives), and memory cards, or it can be implemented as a separate device from the building management device 10, like a file server.

[0094] Here, the building management program 105 includes a room setting module 106, a movement status acquisition module 107, a people data calculation module 108, a normalization processing module 109, an attribute recognition module 110, an activity status estimation module 111, and an instruction generation module 112. Furthermore, each of these modules can be implemented through a separate program or a combination of some of them.

[0095] In addition, it is used to perform the same functions as the individual modules. Figure 1 The structure shown is as follows.

[0096] Room setting module 106: Room setting section 11;

[0097] Motion status acquisition module 107: Motion status acquisition unit 12;

[0098] Personnel data calculation module 108: Personnel data calculation unit 13;

[0099] Normalization processing module 109: Normalization processing unit 14;

[0100] Using attribute recognition module 110: Using attribute recognition unit 15;

[0101] Activity status estimation module 111: Activity status estimation unit 16;

[0102] Instruction generation module 112: Instruction generation unit 17.

[0103] Therefore, the processing device 101 executes the processing of the room setting unit 11, the movement status acquisition unit 12, the number of people data calculation unit 13, the normalization processing unit 14, the attribute recognition unit 15, the activity status estimation unit 16, and the instruction generation unit 17 according to the building management program 105. Furthermore, each of the above modules can be configured as a separate program. In addition, the activity status estimation module 111 may include a time period classification module, an activity event recognition module, and a time schedule data generation module. These correspond to the time period classification unit 161, the activity event recognition unit 162, and the time schedule data generation unit 163, respectively. This concludes the description of the structure of this embodiment; next, the information and data used in this embodiment will be described.

[0104] <Information and Data>

[0105] first, Figure 5 This diagram illustrates the utilization attribute data 181 used in this embodiment. In the utilization attribute data 181, utilization attributes representing attributes (types) based on the room's purpose are stored for each room. For example, for floor B3, "unoccupied" means that the room (floor) is usually unoccupied. Furthermore, the utilization attribute data 181 can also be processed as building management data for identifying the rooms constituting the building as a management object by using items specific to each room. Additionally, building management data can be constructed for each building by associating it with rooms, as information different from the utilization attribute data 181. Moreover, it is preferable that the building management data includes the number of tenants or room occupancy.

[0106] also, Figure 6 This is a diagram illustrating the activity event identification rule 182 used in this embodiment. Activity event identification rule 182 represents the rules used to identify activity events in each room. Therefore, as... Figure 6 As shown, activity event identification rule 182 stores time period conditions and activity event identification rules for each activity event. First, an activity event represents a tenant's characteristic activity that occurs periodically and at any time. For example, it includes the aforementioned work hours and lunch break start times. Thus, activity events typically occur within a defined period, such as every other day, but can also occur multiple times within a defined period. For example, in tenants employing multiple office systems such as short-time work and full-time work, their respective off-get off work times can be managed as activity events.

[0107] Furthermore, the time period condition is an example of a temporal condition in the number of people data used to identify an event. In this embodiment, grouped time periods (morning, noon, afternoon) constituting a specified period are used. Moreover, the identification rule is a rule used to identify an event based on the number of people data, and represents the characteristics of the number of people data, particularly the characteristics represented by the comparison results of the number of people present and the number of people entering and leaving, and the characteristics in the time series. For example, if the number of people data being considered is morning data, and (1) the number of people present is 0, and (2) the initial number of people entering is 1 and the number of people present remains >0 for a specified period of time, the event identification rule 182 corresponds to the record "Number 1". As a result, the event is determined to be "the first office worker to enter". Thus, by using the event identification rule 182, an event can be identified based on the number of people data. For example, as in (2), the event is identified based on the comparison results of the number of people present and the number of people entering and leaving, which are the number of people data. In addition, the time period condition is used in this embodiment, but the time period condition can also be omitted.

[0108] In addition, Figure 6 In the example, the event recognition unit 162 can recognize event events as follows: The time when the grouped time period is morning, the number of attendees > the number of attendees leaving, and the number of attendees reaches its maximum value (or a peak value, etc.) can be identified as the start time for number 3. Alternatively, the start time can be the time when the number of attendees is at its maximum and the number of attendees is close to zero. Here, "the number of attendees close to zero" means that the number of attendees decreases over time, reaching a predetermined number such as 1 or 2 people.

[0109] Furthermore, the time when the number of people leaving is greater than the number of people entering, and the number of people leaving reaches its maximum, can be identified as the lunch break start time (number 4) after the lunch break start time (grouping time period is noon and after the start of the workday), and the time when the number of people entering is greater than the number of people leaving, and the number of people entering shows a peak during this time, can be identified as the lunch break end time (number 5). Finally, the time when the number of people leaving is greater than the number of people entering, and the number of people leaving reaches its maximum (or a peak value, etc.), can be identified as the workday end time (number 6) after the lunch break end time (grouping time period is afternoon and after the end of the lunch break), and the time when the number of people leaving reaches its maximum (or a very high value, etc.), can be identified as the workday exit time (number 6).

[0110] Furthermore, the comparison between the number of people entering and leaving in these examples can be detected as a time period. And, from number 3 to number 6, the identification results (time) of adjacent event events are used respectively. Therefore, it is preferable that the event identification unit 162 performs event identification based on the number of people data in a time sequence from morning to evening.

[0111] Figure 7This is a diagram illustrating the people data 184 used in this embodiment. People data 184 is time-series data representing at least one of the number of people present in the target room and the number of people entering and leaving the room, and includes time information. Here, it is desirable to use the room of the dedicated department as the room for people data, but it is not limited to this.

[0112] Therefore, for each room (unit area), the date, time, number of people entering, number of people leaving, and number of people present are stored in the people data 184. Here, the date and time represent the time when the people data 184 is detected, acquired, etc. Therefore, the date and time can be disregarded, and the unit is not limited to minutes as shown in the figure. The number of people entering, leaving, and present represent the number of people in the corresponding room at the given time (date and time).

[0113] also, Figure 8A and Figure 8B This is a chart showing the number of people (184) used in this embodiment. First, Figure 8A This is a graph showing the number of people entering from the 184 attendees data. According to... Figure 8A It can be seen that the number of people entering the venue peaks around 08:00-09:00 and around 13:00. This is because 08:00-09:00 is the working hours, when there are more people going to work, and around 13:00 is the end of the lunch break, when more people return to the company from their outings.

[0114] also, Figure 8B This is a chart showing the number of people leaving from the 184 attendees data. According to... Figure 8B The number of people leaving peaked around 12:00 and before 18:00. This is because around 12:00, more people go out for lunch due to the start of their lunch break, and around 18:00, more people leave work due to the end of the workday. In this way, by analyzing the 184 people data points as time-series data, it is possible to identify the activity events that occurred in the corresponding rooms.

[0115] The identification of activity events described here assumes that the room is used as an office. Therefore, to more accurately identify activity events, it is desirable to identify utilization attributes related to the room's use. This is because the number of people data 184 exhibits characteristic trends based on utilization attributes. Therefore, in this embodiment, utilization attributes are identified based on the trends and characteristics of the number of people data 184. Hereinafter, utilization attribute identification data 185 used to identify this utilization attribute will be described.

[0116] Figure 9A and Figure 9BThis is a diagram illustrating the attribute identification data 185 used in this embodiment. Here, two sets of attribute identification data 185 will be described, but these two sets of attribute identification data 185 can be used in combination, or only one of them can be used. First, Figure 9A This illustrates utilization attribute identification data 185-1 when utilization attribute types are categorized into office-type and non-office-type. Furthermore, utilization attribute identification data 185-1 stores utilization attribute identification data patterns for each utilization attribute type. Here, the utilization attribute identification data pattern is an example of a feature used to identify the number of people using the utilization attribute (time series data); other forms of features can be used. Figure 9A In the example, the data pattern for identifying data using attributes, as an office type, shows a data pattern for the number of people present, but it could be a data pattern for data about the number of people entering and leaving, such as 184. Furthermore, although in Figure 9A The document shows an office-type data model, while a non-office-type data model is set to the other data models. However, a data model can be described for either the non-office-type or both.

[0117] also, Figure 9B It shows the relationship with Figure 9A Compared to the utilization attribute identification data 185-1, the utilization attribute identification data 185-2 further subdivides the utilization attributes. Specifically, in utilization attribute identification data 185-2, in addition to office type, lobby type, cafeteria type, shared space / lounge type, parking lot type, and unmanned type are also set as utilization attribute types. Furthermore, in utilization attribute identification data 185-2, for each utilization attribute type, features of the people data (time series data) used to identify the utilization attribute are stored. Based on this, regarding the features of the people data (time series data), the features of the number of people present and the number of people entering and leaving (number of people entering the room, number of people leaving the room) are recorded. This division of the features of the number of people present and the number of people entering and leaving is an example; either one can be used, or the number of people entering and leaving can be distinguished. Moreover, at least a portion of the number of people present, the number of people entering, the number of people leaving, and the number of people entering and leaving can be used.

[0118] also, Figure 10 This is a diagram illustrating the time schedule data 186 used in this embodiment. The time schedule data 186 represents the room arrangement, preferably including at least one of activity events and activity status. Then, based on this, equipment control and management operations within the target building are performed.

[0119] Therefore, such as Figure 10As shown, in the time schedule data 186, for each activity event or activity state occurring in the corresponding room, the estimated time of occurrence / time interval between events is stored. In the time schedule data 186, either the activity event or the activity state can be used. In this case, it is preferable to show the activity state.

[0120] Furthermore, the activity event is presumed to occur at a certain time, and the activity state is presumed to occur within the time interval between events. Therefore, in Figure 10 In this example, the time / inter-event time period, representing a time or a time interval between events, is set as a time element. Here, since the activity state in this embodiment occurs between activity events, the inter-event time period, which is a time interval between activity events, is used. However, this is just an example, and other time elements such as time periods can be used. Furthermore, since the schedule data 186 is generated for each room, items for identifying rooms can be set, or the schedule data 186 can be processed by dividing it into sections for each room.

[0121] Furthermore, in this embodiment, movement status data 183 is also used, which represents log data related to the movement of building users such as office workers detected by various detection devices. As described above, movement status data 183 can be used as people data 184, or movement status data 183 can be data without time information. In the latter case, the time when the building management device 10 receives the movement status data 183 can be processed as time information. This concludes the description of <Information and Data>; the <Processing Flow> will be described next.

[0122] <Processing Flow>

[0123] The processing flow of this embodiment will be described below. At this time, the following will be used... Figure 2 and Figure 3 The constituent elements are used to explain the processing subject. First, Figure 11 This is a flowchart illustrating the processing flow of this embodiment. In step S1, the room setting unit 11 sets the target rooms based on user specifications from the building management device 10. For this purpose, the room setting unit 11 extracts rooms from the utilization attribute data 181 used as building management data. Furthermore, the room setting unit 11 can automatically set the target rooms according to preset rules. For example, target rooms can be set every other period. Thus, the time arrangement data 186 can be updated according to predetermined periods such as quarterly or annually, based on relocation and organizational restructuring. In addition, in step S1, multiple rooms, such as all rooms constituting the building, can be set as targets. Furthermore, in step S1, rooms related to proprietary departments can also be extracted as target rooms.

[0124] Furthermore, in step S2, the movement status acquisition unit 12 acquires movement status data regarding the office workers in the target room. Here, office workers refer to users of the building, including room visitors and office workers in other rooms. For this purpose, a detection device such as a human body sensor 404-X detects movement status, and the movement status acquisition unit 12 receives movement status data representing the movement status detected by the detection device. This movement status data may not be time-series data as described above. Additionally, the movement status acquisition unit 12 can receive movement status data through so-called pull-type or push-type data distribution.

[0125] Furthermore, in step S3, the people data calculation unit 13 calculates the number of people in the target room based on the acquired movement status data. Since this calculation method has already been described, its details are omitted, but the movement status data can be used as the people data.

[0126] Furthermore, in step S4, the normalization processing unit 14 normalizes the calculated number of people data. This is performed to unify the number of people data for rooms with different characteristics related to the number of people. Then, the normalization processing unit 14 stores the normalized number of people data as number of people data 184 in the storage unit 18. Alternatively, if the building being targeted consists of uniform rooms, step S4 can be skipped. In this case, the number of people data calculation unit 13 stores the calculated number of people data as number of people data 184 in the storage unit 18.

[0127] Furthermore, in step S5, the attribute recognition unit 15 identifies utilization attributes related to the purpose of the target room based on normalized people data. At this time, the attribute recognition unit 15 uses utilization attribute recognition data 185. Additionally, the attribute recognition unit 15 stores the identified utilization attributes as utilization attribute data 181 in the storage unit 18. Here, we will use... Figure 12A and Figure 12B Two specific examples of attribute-based identification processing in step S5 are explained in detail.

[0128] Figure 12A This is a flowchart illustrating a specific example 1 of the utilization attribute identification process (step S5) of this embodiment. First, specific example 1 is an example of identifying utilization attributes corresponding to the utilization type of the building that is the management object. Here, as the utilization type, an example is used to illustrate whether the building is determined to be an office building or a building with multiple utilization types.

[0129] First, in step S51, the attribute recognition unit 15 extracts the rooms designated as objects from the rooms set in step S1. Furthermore, in step S52, the attribute recognition unit 15 reads the number of people 184 in the rooms extracted in step S51 from the storage unit 18. Furthermore, in step S53, the attribute recognition unit 15 determines the utilization type of the target building. In this embodiment, it determines whether the building is an office-type building or a multi-utilization type building. Here, an office-type building refers to a building where the rooms are primarily used for office work. A multi-utilization type building refers to a building where rooms with various utilization types are mixed together. This can be determined using building management data. If the result of this determination is an office-type building, the process proceeds to step S54. If the building is a multi-utilization type building, the process proceeds to step S55.

[0130] Then, in step S54, the attribute recognition unit 15 uses the attribute recognition data 185-1 to identify whether the target room is an office type or a non-office type. To do this, the attribute recognition unit 15 determines which usage attribute type in the attribute recognition data 185-1 corresponds to the number of people data 184 read in step S52. For example, the attribute recognition unit 15 first compares the usage attribute data pattern of office type with the number of people data 184. As a result, if they are similar or consistent, the attribute recognition unit 15 determines that the usage attribute of the target room is office type. Furthermore, if they are dissimilar or inconsistent, the attribute recognition unit 15 determines that the usage attribute of the target room is non-office type.

[0131] Furthermore, in step S55, the attribute recognition unit 15 uses the attribute recognition data 185-2 to identify the usage attributes of the target room. To this end, the attribute recognition unit 15 determines which usage attribute type in the attribute recognition data 185-2 corresponds to the number of people data 184 read in step S52.

[0132] To this end, the attribute recognition unit 15 extracts features from the read number of people data 184. Then, the attribute recognition unit 15 compares these features with the features of the number of people data used to identify utilization attributes in the attribute recognition data 185-2. As a result, the attribute recognition unit 15 determines the utilization attribute type of similar or consistent number of people data features from the attribute recognition data 185-2.

[0133] Furthermore, in step S56, the attribute recognition unit 15 uses building management data to determine whether the identification of the utilization attributes of each room as the target has been completed. To this end, the attribute recognition unit 15 determines whether each room set in step S1 has been identified. Additionally, the attribute recognition unit 15 can use building management data to determine whether the entire building as the management target has been identified. As a result, if the identification of each room is completed, the process proceeds to step S57. If not, the process returns to step S51, and subsequent processing is performed on the other rooms. Then, in step S57, the attribute recognition unit 15 stores the utilization attributes, which are the identification results from steps S54 and S55, as utilization attribute data 181.

[0134] This concludes the explanation of Specific Example 1. Next, we will explain Specific Example 2. In Specific Example 2, we will use both attribute identification data 185-1 and attribute identification data 185-2 to identify the exploited attribute. The following will follow... Figure 12B Provide its detailed content.

[0135] First, steps S51 and S52 are performed in the same manner as in Specific Example 1. Then, in step S54, the attribute recognition unit 15 uses attribute recognition data 185-1 to identify whether the object room is an office type or a non-office type, in the same manner as in Specific Example 1. As a result, in the case of a non-office type, the process proceeds to step S55. Furthermore, in the case of an office type, the process proceeds to step S56.

[0136] Furthermore, in steps S55 to S57, the processing is performed in the same manner as in Specific Example 1. As a result, the utilization attributes of each room can be identified even without considering the building's utilization pattern as the management object. This concludes the explanation of the utilization attribute identification process in step S5, but this process can be performed in other ways. For example, in step S54 of Specific Example 1, the utilization attribute identification unit 15 can perform step S55 for rooms identified as non-office type. Alternatively, step S54 can be skipped, and step S55 can be performed regardless of whether the room is office type or non-office type. Furthermore, step S54 can be performed to identify office type / non-office type and utilization attributes.

[0137] Next, return to Figure 11The processing after step S6 will be explained. In steps S6 to S8, the activity state estimation unit 16 performs the activity state estimation process. A detailed explanation follows. First, in step S6, the time period classification unit 161 of the activity state estimation unit 16 assigns the normalized number of people data to any one of the three time periods of a day: morning, noon, and afternoon. That is, it classifies the data into which time period group it belongs. Here, "a day" is an example of a periodic, predetermined period in which activity events occur periodically. Furthermore, morning, noon, and afternoon are examples of time periods constituting a predetermined period. Therefore, a day or a time other than morning, noon, and afternoon can also be used.

[0138] Furthermore, in step S7, the activity event identification unit 162 identifies activity events in the target room by applying activity event identification rules 182 to the room's utilization attributes, the grouped time periods classified in step S6, and the normalized number of people data. In addition, in step S8, the time schedule data generation unit 163 estimates the activity status of each time period between events based on the identified activity events and generates time schedule data 186 representing the activity status. An example of activity event identification in step S7 will be described below.

[0139] Figure 13 This is a flowchart illustrating the detailed process of identifying and processing activity events (step S7) in this embodiment. Steps S6 and S8, which precede and follow this step, are also mentioned here. First, as described above, in step S6, the time period classification unit 161 of the activity state estimation unit 16 assigns the normalized number of people data to any one of the three time periods of the day: morning, noon, and afternoon. That is, the time period classification unit 161 of the activity state estimation unit 16 classifies the normalized number of people data into each time period.

[0140] Furthermore, steps S701 to S713, corresponding to step S7, are executed. First, in step S701, the activity event recognition unit 162 extracts arbitrary number of people data from the people data categorized in step S6. At this time, it is preferable that the time period classification unit 161 extracts the data in chronological order, but it is not limited to this order. Furthermore, in step S702, the activity event recognition unit 162 determines whether the utilization attribute of the target room is office type. For this purpose, the activity event recognition unit 162 makes this determination using the utilization attribute data 181, which is the recognition result of step S5. As a result, if it is an office type (yes), the process proceeds to step S703. If it is not an office type (no), the process proceeds to step S712. Here, the case of not being an office type includes, in addition to, other cases such as... Figure 12A Step S54 determines that the type is not an office type, including unmanned types, etc. Figure 5 Examples are listed below.

[0141] Furthermore, in step S703, the activity event recognition unit 162 determines the time period of the grouping of the people data extracted in step S701. In this embodiment, it determines whether the time information of the people data corresponds to morning, noon, or afternoon. As a result, if it is morning, the process proceeds to step S704. If it is noon, the process proceeds to step S707. If it is afternoon, the process proceeds to step S709.

[0142] First, in step S704, the activity event recognition unit 162 identifies the entry time of the first office worker (entrant) in the target room from the people data extracted in step S701, i.e., the earliest entry time. To this end, the activity event recognition unit 162 selects the time represented by the earliest entry time information from the extracted people data where there are more than one person entering. Figure 7 The time (hereinafter referred to as "Time") is set as the arrival time of the first office worker. Additionally, the headcount data extracted here refers to the headcount for the morning period.

[0143] Furthermore, in step S705, the activity event recognition unit 162 uses the extracted people data to identify the time when the number of people present exceeds a preset threshold. Figure 7 (The time of the event). In this step, the event identification unit 162 can identify times when the proportion of the number of people present relative to the staffing quota exceeds a predetermined value. This staffing quota can be the quota included in the building management data, or it can be the maximum number of people present during a predetermined period (e.g., a day or morning) of past attendance data 184. Preferably, a representative value such as the average of multiple predetermined periods is used as this maximum value. Then, in step S706, the event identification unit 162 identifies the time identified in step S705 as the working hours of the event. Alternatively, for example, the threshold could be 5% relative to the staffing quota, which can be used to identify working hours before or after work as the event.

[0144] Furthermore, the building equipment control unit 171 preferably generates control commands corresponding to the pre-work and post-get off work hours. For example, the building equipment control unit 171 generates control commands to start or increase the output of equipment such as air conditioning unit 401 and lighting unit 402 during the pre-get off work hours and outputs these control commands to the respective control devices. Additionally, the building equipment control unit 171 generates control commands to stop or reduce the output of equipment such as air conditioning unit 401 and lighting unit 402 during the post-work hours and outputs these control commands to the respective control devices. Similar to the pre-work and post-get off work hours, control commands can also be generated during work hours, lunch break times, the first employee's arrival time, or the last employee's departure time. These can be executed at least once per time (activity event). That is, during activity events where the number of people present increases, control commands to start or increase the output of equipment are generated; during activity events where the number of people present decreases, control commands to stop or reduce the output of equipment are generated.

[0145] Furthermore, steps S705 and S706 can be performed as follows. First, in step S705, the activity event recognition unit 162 identifies the time when the number of people entering the venue is at its maximum or the maximum value in the morning from the data representing the number of people present. Then, in step S706, the activity event recognition unit 162 sets the identified time as the start time. Additionally, in step S706, the activity event recognition unit 162 can use the time identified in step S705 plus a predetermined time such as 5 minutes as the start time. Furthermore, the difference between the number of people entering and leaving, or the number of people leaving, can be used instead of the number of people entering. When using the number of people leaving, the minimum or minimum value of the number of people leaving is used instead of the maximum or maximum value mentioned above.

[0146] Furthermore, in this embodiment, while the number of people entering the target room is used, the number of people on the movement path to the target room, such as the number of people in the corridor or elevator, can also be used. Even when using these, the time when the number of people on the movement path exceeds a threshold can be used. In addition, the activity event identification unit 162 can accumulate the number of people at each time point in the movement path and process it as the number of people on the target room. Therefore, when using the movement path, the time identified in step S705 should also take into account the movement time, and is preferably longer than the time added to the target room (5 minutes). Thus, the activity event is identified as an activity event in the morning group time period. Next, the identification of activity events in the noon group time period will be explained. In addition, the added time can be adjusted according to the situation. For example, if the identified activity events vary greatly between different dates, it is preferable to extend the time by 10 minutes, etc. This is also the same in the processing (addition and reduction of the prescribed time) described later.

[0147] Furthermore, in step S707, the activity event recognition unit 162 uses the extracted people data to identify the lunch break start time. For this purpose, the activity event recognition unit 162 uses the number of people present and the number of people leaving from the extracted people data. For example, the activity event recognition unit 162 identifies the time when the number of people present is below a preset threshold as the lunch break start time. Additionally, the activity event recognition unit 162 can identify the time when the ratio of the number of people present to the staffing quota is below a predetermined value. This staffing quota can be the quota included in the building management data, but it can also be the minimum number of people present during a predetermined period (e.g., noon) in the past people data 184. As this minimum value, it is preferable to use a representative value such as the average of multiple predetermined periods. Furthermore, the activity event recognition unit 162 can identify 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, as the lunch break start time.

[0148] Alternatively, this step can be performed as follows: First, the event recognition unit 162 identifies the time when the number of departing participants in the attendance data reaches its maximum or the maximum value at noon. The event recognition unit 162 sets the identified time as the lunch break start time. Alternatively, the event recognition unit 162 can use the time obtained by subtracting a predetermined time, such as 5 minutes, from the identified time as the lunch break start time. Furthermore, the difference between the number of departing participants and the number of attending participants, or the number of attending participants, can be used instead of the number of departing participants. When using the number of attending participants, the minimum or minimum value of the number of attending participants is used instead of the maximum or maximum value mentioned above. Additionally, the event recognition unit 162 can identify the time when the number of people present begins to increase, i.e., the time when the number of attending participants exceeds the number of departing participants, as the lunch break end time.

[0149] Furthermore, while this embodiment uses the number of people leaving the target room, the number of people present along the movement path from the target room, such as a corridor or elevator, can also be used. Even when using these, the time when the number of people present along the movement path exceeds a threshold can be used. Additionally, the activity event recognition unit 162 can accumulate the number of people present at each time point along the movement path and process it as the number of people present in the target room. Therefore, when using the movement path, the time subtracted from the recognized time should also take into account the movement time, and preferably be longer than the time subtracted from the target room (5 minutes).

[0150] Furthermore, in step S708, the activity event recognition unit 162 uses the extracted people data to identify the time when the number of people present exceeds a preset threshold. Figure 7 The time at which the lunch break ends is determined. This step can be processed in the same way as steps S705 and S706, but the thresholds can be changed for each. In this case, the individual thresholds are preferably determined based on the trend of past headcount data.

[0151] Furthermore, in this step, similar to steps S705 and S706, the activity event recognition unit 162 can identify the time when the ratio relative to the number of attendees exceeds a predetermined value as the lunch break end time. Alternatively, this step can be performed as follows: First, the activity event recognition unit 162 identifies the time when the number of attendees in the attendance data reaches its maximum or the maximum value at noon as the lunch break end time. In this step, the activity event recognition unit 162 can also use the time obtained by adding a predetermined time, such as 5 minutes, to the identified time as the lunch break end time. Furthermore, the difference between the number of attendees and the number of attendees leaving, or the number of attendees leaving, can be used instead of the number of attendees entering. When using the number of attendees leaving, the minimum or minimum value of the number of attendees leaving is used instead of the maximum or maximum value mentioned above.

[0152] Furthermore, in this embodiment, although the number of people entering the target room is used, the number of people present along the movement path to the target room, such as in a corridor or elevator, can also be used. Even when using these, the time when the number of people present along the movement path exceeds a threshold can be used. Furthermore, the activity event recognition unit 162 can accumulate the number of people present at each time point along the movement path and process it as the number of people present in the target room. Therefore, when using the movement path, the time added to the time identified in step S705 also takes into account the movement time, and is preferably longer than the time added to the target room (5 minutes). Thus, activity events in the noon group time period are recognized. Next, the recognition of activity events in the afternoon group time period will be explained.

[0153] First, in step S709, the activity event recognition unit 162 uses the extracted people data to identify the end-of-get off work time. For this purpose, the activity event recognition unit 162 identifies the time when the number of people leaving the people data reaches its maximum or the maximum value in the afternoon. Then, the activity event recognition unit 162 sets the identified time as the end-of-get off work time. Alternatively, in this step, the activity event recognition unit 162 can use the time obtained by subtracting a predetermined time, such as 5 minutes, from the time identified in step S709 as the end-of-get off work time. Furthermore, the difference between the number of people entering and leaving, or the number of people entering, can be used instead of the number of people leaving. When using the number of people entering, the minimum or minimum value of the number of people entering is used instead of the maximum or maximum value mentioned above. Additionally, the activity event recognition unit 162 can identify the time when the number of people present begins to decrease, that is, the time when the number of people leaving exceeds the number of people entering, as the end-of-get off work time.

[0154] Furthermore, in this embodiment, while the number of people leaving the target room is used, the number of people present along the movement path to the target room, such as in a corridor or elevator, can also be used. Even when using these, the time when the number of people present along the movement path exceeds a threshold can be used. Additionally, the activity event recognition unit 162 can accumulate the number of people present at each point in time along the movement path and process it as the number of people present in the target room. Therefore, when using the movement path, the time subtracted from the time identified in step S710 should also take into account the movement time, and is preferably longer than the time subtracted from the target room (5 minutes).

[0155] Furthermore, in step S710, the event recognition unit 162 uses the extracted people data to identify times when the number of people present is below a preset threshold. In this step, the event recognition unit 162 can identify times when the ratio of the number of people present to the capacity is below a predetermined value.

[0156] Alternatively, step S710 can be performed as follows. This result can be used for determining the off-get off work time in step S709. That is, if the off-get off work time identified in step S709 does not reach below a threshold after a certain period of time, the activity event recognition unit 162 identifies the time below the threshold as the off-get off work time. Furthermore, if the time reaches below the threshold within a certain period of time, the activity event recognition unit 162 determines the off-get off work time identified in step S709. Alternatively, this step can be omitted.

[0157] Furthermore, in step S711, the activity event recognition unit 162 identifies the departure time of the last office worker (entrant) leaving the target room from the people data extracted in step S701, i.e., the last departure time. For this purpose, the activity event recognition unit 162 selects the time when more than one person leaves the room from the extracted people data and the last departure time is... Figure 7The time (i.e., the departure time of the last office worker) is set as the time when the last office worker leaves. This allows for the identification of activity events within the afternoon time slots. Similarly, it allows for the identification of activity events within the morning, noon, and afternoon time slots.

[0158] will use Figure 14 , Figure 15A , Figure 15B and Figure 15C The diagrams in the diagram illustrate the identification of activity events in steps S704 to S711 performed as described above. First, Figure 14 This is a chart used to illustrate the identification of activity events in this embodiment. Figure 14 The number of people entering and exiting at each time point is displayed (personnel data). Furthermore, in... Figure 14 The diagram shows the number of people entering and leaving over time (solid line: number of people entering, dashed line: number of people leaving). Furthermore, the diagram above indicates the morning, noon, and afternoon time periods as groupings.

[0159] First, the processing of the morning grouping time period in steps S704 to S706 will be explained. First, in the morning, the first number of people entering is measured around 6:00 AM. In step S704, this time is identified as the arrival time of the first office worker. Next, after 8:00 AM, the maximum number of people entering in the morning is shown. Additionally, at this time, the maximum number of people present, calculated using (Mathematical Formula 1) using the difference between the number of people entering and leaving, is also represented.

[0160] Therefore, in steps S705 and S706, this time is identified as working hours. Furthermore, from 8:00 AM to 11:30 AM (the entire morning), the number of people entering exceeds the number of people present, and the number of people present is also increasing. Therefore, it can be concluded that after working hours, office staff are in the target room. Therefore, the activity event identification unit 162 can presume that no particular activity event occurred after working hours.

[0161] Next, the processing of the noon grouping time period in steps S707 and S708 will be explained. First, from the start of noon at 11:30 to 12:00, the number of people leaving exceeded the number of people entering. Then, at 12:00, the number of people leaving represents the maximum, and the number of people present is lower than the limit. As a result, 12:00 is identified as the start time of the lunch break.

[0162] Additionally, around 12:45 PM, the number of attendees reached its latest peak (extreme value), exceeding the capacity. As a result, 12:45 PM was identified as the end time of the lunch break. Furthermore, the latest peak (extreme value) and time factors can also be used to identify event events. This will be explained later. Also, here, the latest peak can also be identified as the maximum value at noon. Furthermore, "latest" refers to the latest majority of the day.

[0163] Finally, the processing of the afternoon grouping time period in steps S709 to S711 will be explained. First, around 17:30, the maximum number of people leaving in the afternoon is shown, which is the latest peak. Therefore, in step S709, this time is identified as the end of the workday. Additionally, from 16:00 to 20:00, the number of people leaving exceeds the number of people entering, and the number of people present is decreasing. As a result, in step S710, the time when the number of people present falls below a preset threshold during this period is identified. Furthermore, around 21:00, the last number of people leaving is measured. In step S711, this time is identified as the departure time of the last office worker.

[0164] In steps S704 to S711 above, the activity event identification unit 162 uses extreme values ​​(peak values) related to the number of people data, such as maximum and minimum values, to estimate the activity event. When there are multiple such extreme values ​​in the grouped time period, the activity event can be estimated as follows (1) or (2). (1) Select the maximum value in the grouped time period and use the maximum value. (2) Store the sequential data of the activity event in the storage unit 18, and identify the activity event by comparing it with the sequential data. Hereinafter, (2) will be explained.

[0165] First, the storage unit 18 stores sequential data representing the standard order in which activity events occur. Then, when multiple peaks are detected, the activity event identification unit 162 identifies the activity events according to the order represented by the sequential data. For example, as sequential data, consider the following scenario: start time (peak number of people entering) - start time - lunch break start time (peak number of people leaving) - lunch break end time (peak number of people entering) ... In this case, the activity event identification unit 162 identifies the time representing the first detected peak number of people entering as the start time.

[0166] In this embodiment, the event is categorized according to the grouped time period in step S703, and the results are used to identify the event. However, this step can be skipped, and sequential data can be used to identify the event. Furthermore, a learning function can be used to perform this embodiment. For example, the event identification unit 162 learns the relationship between features of people data such as the increasing or decreasing trend of the number of people present and the number of people entering, and identifies the event based on this trend. This concludes the description of the event. Figure 14 Explanation.

[0167] Next, Figure 15A , Figure 15B and Figure 15CThis is a graph illustrating the relationship between activity events and people data in this embodiment. Furthermore, taking the identification of an office-type room as an example, the number of people leaving is used as the vertical axis, and the number of people entering is used as the horizontal axis. Each point represents the people data extracted in step S701.

[0168] First of all, Figure 15A The text indicates the relationship between activity events and the number of participants during the morning group time slots. Figure 15A This data represents the morning's attendance, so there were many people going to work. Overall, the number of people entering exceeded the number leaving, and more points were drawn below the dashed line than above. Then, the group of people with the largest number of people entering was circled with dashed lines. The time information (element) of the data with the largest number of people entering was identified as the start of work hours.

[0169] Next, in Figure 15B The diagram shows the relationship between activity events and the number of participants during the midday grouping time period. Because... Figure 15B This data represents the number of attendees at noon. Since both those going out for lunch and those returning to their rooms participated, the overall number of people entering and leaving is roughly the same. Therefore, Figure 15B The points in the middle are drawn to the same degree below and above the dashed line. Then, in Figure 15B In the middle, the first group of people with more departures and the second group of people with more arrivals are circled with dashed lines.

[0170] Then, the time information (element) of the number of people leaving the first group is identified as the start time of the lunch break. In addition, the time information (element) of the number of people entering the second group is identified as the end time of the lunch break.

[0171] Finally, Figure 15C The diagram shows the relationship between activity events and the number of participants during the afternoon group time periods. Because... Figure 15C This data represents the afternoon's attendance, so there are more people leaving get off work. Overall, the number of people leaving exceeds the number entering, and more points are drawn above the dashed line than below. Then, the group of people leaving with the largest number of departures is circled with dashed lines. The time information (feature) of the data with the largest number of departures is identified as the departure time. Additionally, Figures 15A to 15C The processing result of this embodiment can be displayed on, for example, a building management terminal device 405. This concludes the description up to step S711. Return to... Figure 13 Proceed to step S712 and the following explanation.

[0172] In step S712, the activity event recognition unit 162 identifies activity events in non-office type rooms. At this time, the activity event recognition unit 162 uses the number of people extracted in step S701 to identify activity events different from those in office type rooms. For example, in the case of an unoccupied room, the activity event recognition unit 162 identifies the work start time and work end time based on the presence or absence of people present. That is, the time of the first person entering is identified as the work start time, and the time of the last person leaving is identified as the work end time.

[0173] Furthermore, in step S713, the activity event recognition unit 162 determines whether the processing of the number of people data classified in step S6 is complete. If complete (yes), the process proceeds to step S8. If incomplete (no), the process proceeds to step S701, where the next number of people data is extracted and subsequent processing is performed. Then, the activity event recognition unit 162 stores each activity event identified in step S7 in the storage unit 18.

[0174] Then, in step S8, the time scheduling data generation unit 163 estimates the activity status of the time period between each event based on the activity events identified in step S7, and generates time scheduling data 186 that at least represents the activity status. The details of step S8 will be explained below. Figure 16 This is a flowchart illustrating the detailed process of estimating the activity state (step S8) in the implementation method. First, in step S801, the schedule data generation unit 163 determines whether the utilization attribute of the target room is office type. 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.

[0175] Furthermore, in step S802, the time scheduling data generation unit 163 sets the "entry time of the first office worker" as the activity event identified in step S704. That is, for Figure 10 The time schedule data shown is 186, which records the "entry time of the first office worker" and its time. Figure 10 (The first record).

[0176] Furthermore, in step S803, the time scheduling data generation unit 163 sets the "time when the number of people present before work exceeds a threshold" as the activity event identified in step S705. Similar to step S802, this step can be implemented by recording it in the time scheduling data 186. The following steps are also the same. Furthermore, in step S804, the time scheduling data generation unit 163 sets the "work start time" as the activity event identified in step S706.

[0177] Then, in step S805, the time scheduling data generation unit 163 sets the time period (inter-event time period) between the "time when the number of people present before work exceeds the threshold" set in step S803 and the "work start time" set in step S804. Then, the time scheduling data generation unit 163 estimates the activity state of this inter-event time period as the "pre-work time period" and sets this activity state. Thus, in this embodiment, the time scheduling data generation unit 163 extracts adjacent activity events in the time sequence.

[0178] Furthermore, the time scheduling data generation unit 163 estimates the activity status within the inter-event time period, which is the time period between these activity events, based on these activity events. This activity status represents the content of activities within the room's tenants, estimated, for example, according to the activity status estimation rules stored in the storage unit 18. It shows the correspondence between two activity events and their activity statuses. Then, the time scheduling data generation unit 163 records the estimated activity status in... Figure 10 The time schedule data shown is in 186. The same process applies to the activity status setting steps below.

[0179] Furthermore, in step S806, the time scheduling data generation unit 163 sets the "lunch break start time" as the activity event identified in step S707. Then, in step S807, the time scheduling data generation unit 163 estimates the activity status of the time period between the "work hours" set in step S804 and the "lunch break start time" set in step S806 as the "morning work hours". This can be estimated in the same way as in step S805. This is also true in the activity status estimation steps below.

[0180] Furthermore, in step S808, the time scheduling data generation unit 163 sets the "lunch break end time" as the activity event identified in step S708. Then, in step S809, the time scheduling data generation unit 163 estimates the activity state of the time period between the event interval set in step S806 ("lunch break start time") and the event interval set in step S808 ("lunch break end time") as the "lunch break time period". Based on this, the time scheduling data generation unit 163 sets this activity state.

[0181] Furthermore, in step S810, the time scheduling data generation unit 163 sets "off-get off work time" as the activity event identified in step S709. Then, in step S811, the time scheduling data generation unit 163 estimates the activity status of the time period between the event time set in step S808 ("lunch break end time") and the event time set in step S810 as "afternoon office hours". Based on this, the time scheduling data generation unit 163 sets this activity status.

[0182] Furthermore, in step S812, the time scheduling data generation unit 163 sets "time when the number of people present is below a threshold" as the activity event identified in step S710. Then, in step S813, the time scheduling data generation unit 163 estimates the activity state of the time period between the "off-get off work time" set in step S810 and the "time when the number of people present is below a threshold" set in step S12 as an "overtime period". Based on this, the time scheduling data generation unit 163 sets this activity state.

[0183] Furthermore, in step S814, the time scheduling data generation unit 163 sets the "last office worker's departure time" as the activity event identified in step S711. Here, the "last office worker's departure time" is the last activity event of the day. Therefore, in step S815, the time scheduling data generation unit 163 generates time scheduling data 186 including the activity events and activity statuses set in steps S802 to S814. Figure 10 Additionally, for example, the above sequential data can be used to determine that "the last office worker's departure time" is the last activity event of the day.

[0184] Furthermore, in step S816, the time scheduling data generation unit 163 generates time scheduling data 186 for non-office type rooms based on the utilization attributes. At this time, the time scheduling data 186 can be generated by estimating the activity status of the activity events and the time periods between events in the same way as for office type rooms (same as in steps S802 to S815).

[0185] will use Figure 17A and Figure 17B A diagram is used to illustrate the relationship between the activity events and activity states set in steps S802 to S815 as described above. First, Figure 17A This is a graph showing the relationship between the time elapsed between the activity events and the number of people present in this embodiment. Figure 14 The diagram illustrates the relationship between event events and the number of attendees and departers; here, the number of attendees is used. Figure 17A The diagram illustrates the identification of various activity events in chronological order based on changes in the number of people present. Specifically, it identifies "the arrival time of the first office worker," "the time when the number of people present exceeds a threshold before work," "work start time," "lunch break start time," "lunch break end time," "get off work end time," "the time when the number of people present is below a threshold after get off work," and "the departure time of the last office worker." Thus, the activity event identification unit 162 can identify activity events based on changes in the number of people present.

[0186] also, Figure 17B This is a graph showing the relationship between the activity status and the number of people present over time in this embodiment. Figure 17B With Figure 17A The same scale indicates the time and the number of people present. Figure 17B It shows Figure 17A The activity status of the time period between adjacent activities is shown. Specifically, it shows the "pre-work time period", "morning office time period", "lunch break time period", "afternoon office time period" and "overtime time period".

[0187] Here, "pre-work hours" refers to the period from "the time the first office worker enters" or "the time when the number of people present before work exceeds a threshold" to "work hours". Additionally, "morning work hours" is from "work hours" to "lunch break start time". Furthermore, "lunch break hours" is from "lunch break start time" to "lunch break end time". Additionally, "afternoon work hours" is from "lunch break end time" to "get off work hours". Finally, "overtime hours" is from "get off work hours" to "the time when the number of people present after get off work falls below a threshold" or "the time when the last office worker leaves".

[0188] Furthermore, the above-mentioned activity events and activity states are merely examples and are not limited to; at least a portion of them can be used, or other activity events and activity states can be added. Specifically, for activity events, due to the presumed activity states, more than two activity events can be identified. In this case, it is more preferable to identify "starting time," "lunch break start time," "lunch break end time," and "off time." In this case, the activity states of "morning office hours," "lunch break hours," and "afternoon office hours" are presumed. Additionally, Figures 17A to 17B The processing results of this embodiment can be displayed on, for example, a building management terminal device 405.

[0189] The above explains the estimation of activity status and the generation of schedule data 186. As described above, schedule data 186 is generated at a predetermined period. It is then stored in storage unit 18. Using this data, daily building operations can be implemented. The following section discusses the processing used for building operations, referring back to... Figure 11 The subsequent processing (after step S9) is explained. Alternatively, steps S8 and S9 can be executed consecutively, or step S8 can be executed temporarily, followed by step S9 and subsequent steps during routine operations. Here, "consecutive" means, for example, at a predetermined time (e.g., 4:00 AM daily), using past movement states up to the previous day, step S8 is executed, and step S9 and subsequent steps are executed on the same day.

[0190] In step S9, the instruction generation unit 17 generates instructions for managing the building, which is the object of management, based on the schedule data 186 generated in step S8. As instructions in this embodiment, control instructions and management operation instructions for control equipment are used. Therefore, the instruction generation unit 17 includes a building equipment control unit 171 and a building management operation unit 172. First, the building equipment control unit 171 generates control instructions based on the schedule data 186 related to the rooms being managed, and outputs the control instructions to each control device of the control equipment.

[0191] For example, during morning and afternoon office hours when there are more people present, control commands are generated and output for operation in a comfort-oriented mode. Conversely, during periods with fewer people present, such as lunch breaks, commands are generated and output for operation in an energy-efficient mode.

[0192] Furthermore, the building management operations unit 172 generates management operation instructions based on the generated schedule data 186 and outputs these instructions to management devices such as the building security operation device 50. Additionally, the management operation instructions can be output to the building management terminal device 405. As a result, these devices display the management operation instructions, and their users can confirm the displayed content. The management operation instructions may include the schedule data 186 itself. Consequently, building managers and others can formulate policies regarding building operations.

[0193] According to the above implementation method, time arrangement data for rooms such as dedicated departments can be generated with high precision based on movement status. As a result, building owners and managers can improve building operations. Furthermore, appropriate advice and guidance can be provided to these owners and managers. For example, consulting firms acting as third parties other than owners and managers can analyze building operations and provide recommendations. The above is a description of this implementation method, but the invention is not limited thereto.

[0194] For example, the facility also includes areas outside the building, and can estimate the activity status of various areas outside the rooms. Furthermore, predetermined schedule data (plans) can be prepared in advance. In this case, the activity status estimation unit 16 can compare it with the estimated schedule data 186 in this embodiment, analyze the deviation, and manage whether activities are carried out as planned (especially office work).

[0195] Label Explanation

[0196] 1. Building Management System; 10. Building Management Device; 11. Room Setting Unit; 12. Movement Status Acquisition Unit; 13. People Data Calculation Unit; 14. Normalization Processing Unit; 15. Attribute Recognition Unit; 16. Activity Status Estimation Unit; 17. Instruction Generation Unit; 18. Storage Unit; 181. Attribute Data; 182. Activity Event Recognition Rules; 183. Movement Status Data; 184. People Data; 185. Attribute Recognition Data; 186. Time Scheduling Data; 20. Elevator Equipment; 21. Elevator Group Control Device; 30. Building-Wide Patrol Robot Group; 31. Robot Group Control Device; 40. Equipment Groups on Each Floor; 41. Air Conditioning Equipment Control Device; 42. Lighting Equipment Control Device; 43. Access Control Device; 44. Human Body Sensor Management Device; 401. Air Conditioning Equipment; 402. Lighting Equipment; 403. Access Control Device; 404. Human Body Sensor; 405. Building Management Terminal Device; 50. Building Security Operation Device; 60. Building Cleaning Operation Device; 70. Building Cafeteria Operation System; 80. Public Lines.

Claims

1. A facility management device, characterized in that, include: The movement status acquisition unit acquires movement status data, which represents the movement status of a user in a first unit area within the facility detected by the detection device. The personnel data calculation unit calculates personnel data based on the movement status data. The personnel data includes at least one of the number of users staying in the second unit area within the facility and the number of users entering and exiting the second unit area, and also includes time information. The attribute recognition unit identifies utilization attributes related to the use of the second unit area based on the population data. as well as An activity status estimation unit estimates the activity status in the second unit area based on the number of people and the utilization attributes.

2. The facility management device as described in claim 1, characterized in that, include: The data generation department is responsible for generating a time schedule that represents the status of the activity. as well as An instruction generation unit generates instructions for managing the facility based on scheduling information.

3. The facility management device as described in claim 2, characterized in that, The instruction generation unit includes an equipment control unit that outputs control instructions to a control device for controlling the equipment in the facility.

4. The facility management device as described in claim 2, characterized in that, The facility management device includes an output unit that outputs the schedule.

5. The facility management device as described in claim 1, characterized in that, The facility includes dedicated sections and shared sections. The first unit region is the unit region associated with the proprietary part. The second unit area is the proprietary part.

6. The facility management device as described in claim 1, characterized in that, The detection device is installed in the elevator within the facility.

7. The facility management device as described in claim 1, characterized in that, The activity status estimation unit includes an activity event identification unit, which identifies activity events that correspond to the number of people and that occur within a specified period.

8. The facility management device as described in claim 7, characterized in that, The activity event identification unit identifies multiple activity events. The facility management device includes a time schedule data generation unit, which estimates the activity status of each time period between events, where the time period between events is the time period between adjacent activity events.

9. The facility management device as described in claim 8, characterized in that, The activity status estimation unit also includes a time period classification unit, which classifies the calculated number of people into grouped time periods that constitute a specified period. The activity event identification unit identifies the activity event by applying the utilization attributes, the grouping time period, and the calculated number of people data to the activity event identification rules representing the characteristics of the number of people data.

10. The facility management device as described in claim 9, characterized in that, The activity event identification rules include identification rules that show the following characteristics: features shown by comparing the size of the number of people in each time period condition of the grouped time period. The event recognition unit identifies the event based on the comparison results of the calculated number of people data.

11. The facility management device as described in claim 9, characterized in that, The specified period is one day. The time period classification department categorizes the number of people in a day into morning, noon, and afternoon.

12. The facility management device as described in claim 11, characterized in that, The facility includes multiple rooms. When the utilization attribute identification unit classifies the utilization attribute of the second unit area as office type, The activity event recognition unit identifies at least two of the following as the multiple activity events: the time when the first office worker enters the premises, the time when the number of people present before work exceeds a threshold, 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 present after get off work is below a threshold, and the time when the last office worker leaves the premises.

13. The facility management device as described in claim 12, characterized in that, The activity event identification unit identifies at least one of the following times: The time period in which the grouping time is in the morning, the number of people entering is greater than the number of people leaving, and the number of people entering reaches its maximum value is identified as the working time. The time period for grouping is noon. The time when the number of people leaving after the start of work is greater than the number of people entering, and the time when the number of people leaving reaches its maximum value, is identified as the start time of the lunch break. The time period is defined as noon. The time when the number of people entering is greater than the number of people leaving after the start of the lunch break, and the last time when the number of people entering shows a peak during this time, is identified as the end of the lunch break. The time period for grouping is afternoon. The time when the number of people leaving after the lunch break is greater than the number of people entering, and the time when the number of people leaving reaches its peak, is identified as the end of the workday.

14. The facility management device as described in claim 12, characterized in that, It also includes an equipment control unit that controls the equipment of the facility. The activity event recognition unit identifies the following as multiple activity events: the arrival time of the first office worker, the time when the number of people present before work exceeds a threshold, 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 present after get off work is below a threshold, and the departure time of the last office worker. The equipment control unit generates control commands to start the equipment or increase output based on at least one of the following: the arrival time of the first office worker, the time when the number of people present before work exceeds a threshold, the start time of work, and the end time of lunch break. The equipment control unit generates control commands to stop or reduce the output of the equipment for at least one of the following: the lunch break start time, the end time, the time when the number of people present after get off work is below a threshold, and the departure time of the last office worker.

15. The facility management device as described in claim 7, characterized in that, The event identification unit identifies the event by comparing sequence data, which represents the standard order in which the event occurred, with the number of people.

16. A facility management method, executed by a facility management device, characterized in that, The movement status acquisition unit acquires movement status data, which represents the movement status of a user in a first unit area within the facility detected by the detection device. The people data calculation unit calculates people data based on the movement status data. This people data includes at least one of the number of users staying in the second unit area within the facility and the number of users entering and exiting the second unit area, and also includes time information. The attribute recognition unit uses the population data to identify usage attributes related to the purpose of the second unit area. The activity status estimation unit estimates the activity status in the second unit area based on the number of people and the utilization attributes.

Images