WO2020255337A1

WO2020255337A1 - Number-of-people prediction device, equipment management system, and number-of-people prediction method

Info

Publication number: WO2020255337A1
Application number: PCT/JP2019/024524
Authority: WO
Inventors: 智祐成井; 利宏妻鹿; 裕希川野; 修一村山; 浩田口; 淳二堀
Original assignee: 三菱電機ビルテクノサービス株式会社; 三菱電機株式会社
Priority date: 2019-06-20
Filing date: 2019-06-20
Publication date: 2020-12-24
Also published as: JPWO2020255337A1; JP6818937B1; CN114041151A

Abstract

According to the present invention, a schedule information acquisition unit 11 acquires schedule information on each user who uses a predetermined area in a building. A primary prediction unit 13 calculates a primary prediction value for the number of people present in an area at a prediction target time by predicting, from the schedule information, whether or not each user is present in the area at the prediction target time. A correction unit 19 calculates a secondary prediction value for the number of people present in the area by correcting the primary prediction value using a correction parameter set according to the area and the prediction target time.

Description

Location number prediction device, equipment management system, and location number prediction method

The present invention relates to a location number prediction device, a facility management system, and a location number prediction method.

When operating and managing the facilities of a building such as a building, it may be desirable to predict the number of people on each floor after a predetermined time. For example, in order to suppress the power consumption of the entire building one hour later, an air conditioner that reduces the output is selected from the air conditioners installed on each floor. At this time, if the predicted value of the number of people in the room on each floor after 1 hour is obtained, for example, the output of the air conditioning equipment on the floor where the number of people in the room after 1 hour is predicted to be small can be suppressed. It is possible to reduce power consumption while suppressing reduction of user comfort.

Conventionally, when predicting the number of employees, the estimated time of arrival at the office is calculated by detecting that an employee has passed the automatic ticket gate, and the estimated number of people in the office for each time is calculated from the estimated arrival time of each employee. (For example, Patent Document 1).

In addition, in predicting the number of people in the building, a technique has been proposed in which the predicted value of the number of people in a specific area in a specific time zone is increased or decreased according to the schedule of the user by referring to the schedule of the user of the building ( For example, Patent Document 2).

Further, a technique has been proposed in which the number of people on each floor is obtained from the elevator boarding / alighting history information and the number of people in each floor is corrected by the correction parameters obtained from the operation history data (for example, Patent Document 3).

Japanese Unexamined Patent Publication No. 2012-109680 Japanese Unexamined Patent Publication No. 2011-180974 Japanese Unexamined Patent Publication No. 2016-74525

By the way, when predicting the number of people in the future using schedule information, the schedule information of each user is created by each user registering the schedule. That is, a prediction error may occur in the number of people in the location due to omission of registration of schedule information or registration error.

Therefore, an object of the present invention is to make it possible to suppress a prediction error caused by schedule information in which schedule registration is left to the discretion of the user.

The location number prediction device according to the present invention includes a schedule information acquisition unit, a primary prediction unit, and a correction unit. The schedule information acquisition unit acquires schedule information of each user who uses a predetermined area in the building. The primary prediction unit calculates the primary prediction value of the number of people in the area at the prediction target time by predicting whether or not each user is located in the area at the prediction target time from the schedule information. The correction unit corrects the primary predicted value with the correction parameters set according to the area and the time to be predicted, and calculates the secondary predicted value of the number of people in the area.

According to the above invention, the secondary predicted value obtained by correcting the primary predicted value with the correction parameter is calculated. By correcting the prediction error of the primary prediction value by the correction parameter, the prediction error can be suppressed.

Further, in the above invention, the location number prediction device may include a location number acquisition unit and a correction parameter setting unit. The location number acquisition department acquires the actual value of the location number of people in the area. The correction parameter setting unit sets the correction parameter based on the difference between the primary predicted value and the actual value of the number of people in the area at the same time.

According to the above invention, the correction parameter is set based on the difference between the primary predicted value and the actual value of the number of residents. By setting a correction parameter that reduces the difference, it is possible to calculate a predicted value in which the prediction error is suppressed.

Further, in the above invention, the location number prediction device may include an attendance / leaving information acquisition unit that acquires attendance / leaving information for each user in the area. In this case, the primary prediction unit predicts the number of employees in the area at the time to be predicted by using the history of attendance / leaving information of each user, and calculates the primary prediction value based on the number of employees and the schedule information.

According to the above invention, since the unemployed and the leaving employees are excluded from the capacity of the area, it is possible to calculate the predicted value with more suppressed error.

Further, in the above invention, the primary prediction unit may calculate the primary prediction value at the prediction target time by subtracting the number of absentees at the prediction target time predicted from the schedule information from the number of workers at the prediction target time.

According to the above invention, the number of employees who have not attended or left the office is excluded from the capacity of the area as a population, and the number of absentees predicted from the schedule information is subtracted from this number to obtain the primary predicted value. , It becomes possible to calculate a predicted value with more suppressed error.

The present invention also relates to a facility management system. The system includes a location number prediction device according to the above invention and a facility management device that manages equipment installed in a building based on the number of locations in an area predicted by the location number prediction device.

The present invention also relates to a method for predicting the number of people. In this method, the step of acquiring the schedule information of each user who uses a predetermined area in the building and the prediction target time are predicted from the schedule information whether or not each user is located in the area at the prediction target time. The step of calculating the primary predicted value of the number of people in the area and the step of correcting the primary predicted value with the correction parameters set according to the area and the time to be predicted to calculate the secondary predicted value of the number of people in the area. And, including.

According to the present invention, it is possible to suppress a prediction error caused by schedule information in which schedule registration is left to the discretion of the user, as compared with the conventional case.

It is an overall block diagram which showed one Embodiment of the equipment management system which concerns on this invention. FIG. 5 is a hardware configuration diagram of a computer forming the location number prediction device according to the first embodiment. It is a block block diagram which exemplifies the location number predicting apparatus in Embodiment 1. FIG. It is a figure which shows the data structure example of the schedule information stored in the schedule information storage part in Embodiment 1. FIG. It is a figure which illustrates the correction parameter in Embodiment 1. FIG. It is a figure explaining the correction process (1/2) of the primary prediction value. It is a figure explaining the correction process (2/2) of the primary prediction value. It is a flowchart exemplifying the location number prediction processing in Embodiment 1. It is a block block diagram which exemplifies the location number predicting apparatus in Embodiment 2. FIG. It is a flowchart which showed the location number prediction process in Embodiment 2.

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.

<Embodiment 1. ＞
FIG. 1 is an overall configuration diagram showing an embodiment of the equipment management system according to the present invention. The equipment management system in the present embodiment is constructed in the building 1. In the present embodiment, a multi-story building 1 is assumed as the building.

Further, in the present embodiment, each floor of the building 1 corresponds to the area in the present invention. When each floor is divided into a plurality of areas (rooms) by a partition wall or the like, each area (room) may be used as the area of the present invention.

Furthermore, in the following, it is assumed that building 1 is used exclusively by one company for convenience of explanation. Employees and visitors engaged in the company will come and go to the building 1, but in this embodiment, the number of people on each floor is referred to as the "number of people located" on the floor.

The building 1 is provided with a configuration in which the number of people locating device 10, the equipment management device 2, the schedule management server 3, and the attendance management device 5 according to the present embodiment are connected to the network 4. The equipment management device 2 manages the equipment installed in the building based on the number of people in the area predicted by the number of people in the building 10.

The schedule management server 3 collectively manages the schedule information of each employee engaged in the building 1. For the schedule information, a general-purpose schedule management application may be used. However, the application needs to have a function of specifying the start and end times of events such as meetings subject to schedule management and the floor (area) to be held.

The attendance / leaving management device 5 manages attendance / leaving information of employees who work in the building 1. For example, the attendance management device 5 includes an attendance system installed on each floor in the building 1 and accessible from a terminal assigned to each employee. When an employee accesses the attendance system from a terminal and stamps the attendance time and the attendance time, the attendance / attendance management device 5 records the attendance / leaving information of each employee in the building 1. For example, information such as an employee's ID (name, identification number, etc.), office floor (office area), affiliation, etc. is stored in the storage unit of the attendance management device 5 in association with the attendance time and the leaving time.

FIG. 2 is a hardware configuration diagram of a computer forming the location number prediction device 10 according to the present embodiment. The computer forming the location number prediction device 10 in the present embodiment can be realized by a general-purpose hardware configuration such as a personal computer (PC) that has existed before.

That is, the location number prediction device 10 includes a CPU 21, a ROM 22, a RAM 23, and a hard disk drive (HDD) 24 as shown in FIG. 2, and these are connected to the internal bus 30. Further, the location number prediction device 10 includes a mouse 25 and a keyboard 26 provided as input means, and a display 27 provided as a display means, and these are connected to the input / output controller 28. In addition, the location number prediction device 10 includes the input / output controller 28 and a network controller 29 provided as a communication means, which are also connected to the internal bus 30. Since the equipment management device 2 is also realized by a computer, its hardware configuration can be illustrated in the same manner as in FIG.

FIG. 3 is a block configuration diagram of the number of people locating device 10 according to the present embodiment. The components not used in the description of the present embodiment are omitted from FIG. The location number prediction device 10 in the present embodiment includes a schedule information acquisition unit 11, a schedule information storage unit 12, a primary prediction unit 13, a primary prediction value information storage unit 14, a location number acquisition unit 15, and a location number information storage unit 16. It includes a correction parameter setting unit 17, a correction parameter information storage unit 18, and a correction unit 19.

Each component of the location number prediction device 10, that is, the schedule information acquisition unit 11 to the correction unit 19, comprises a computer forming the location number prediction device 10 and a program operated by a CPU 21 (see FIG. 2) mounted on the computer. It is realized by cooperative operation. Further, each of the

storage units

12, 14, 16 and 18 is realized by the HDD 24 mounted on the location number prediction device 10. Alternatively, the RAM 23 or an external storage means may be used via the network.

Further, the program used in the present embodiment can be provided not only by communication means but also by storing it in a computer-readable recording medium such as a CD-ROM or a USB memory. The programs provided by the communication means and the recording medium are installed in the computer, and the CPU of the computer sequentially executes the programs to configure a resident device that realizes various processes.

The schedule information acquisition unit 11 acquires schedule information of users (for example, employees) on each floor (that is, each area) of the building 1 from the schedule management server 3 (see FIG. 1) and stores it in the schedule information storage unit 12. For example, the schedule information acquisition unit 11 acquires schedule information for the forecast day at 0:00 every day. Furthermore, since the schedule information for the current day may be changed even after 0:00, the schedule information may be updated periodically.

FIG. 4 illustrates the data structure of the schedule information stored in the schedule information storage unit 12 in the present embodiment in a table format. Schedule information is generated for each event (schedule) for each user (employee), and each record represents one schedule (schedule) for one user.

In FIG. 4, the user ID is user, that is, employee identification information (employee number). The start date and start time are information indicating the start of the schedule (planned). The end date and end time are information indicating the end of the schedule (planned). The schedule type is information indicating the type of the schedule, and is set by selecting from the items specified in advance when the user registers the schedule. The schedule content is information indicating specific content in the schedule type. The location is information indicating the implementation location of the schedule.

In this embodiment, the number of people located is predicted for each floor (area), so it is necessary to include information that can identify the floor where the location is located. According to the information setting example shown in FIG. 4, both users u001, u002, and u003 are scheduled to participate in the "partial meeting" held at the same date and time (February 1, 2017) and place (4th floor, 1st meeting room). You can see that. In addition, the schedule information is set and registered as a different schedule for each user even in the same conference.

Since the schedule information acquisition unit 11 has already acquired the schedule information before (past) the prediction day and saved it in the schedule information storage unit 12, the schedule information acquired here may be only the schedule information related to the prediction day. .. The schedule information related to the forecast day means the schedule information including the schedule information of the forecast day. For example, the schedule information is not limited to the schedule information set only on the forecast day, but also includes long-term business trips including the forecast day and dates before and after the forecast day.

Strictly speaking, the schedule after the time of prediction may be changed by the user on the day of prediction (such as adding a schedule of 16:00 at 10 o'clock on the day of prediction). Therefore, every time the location number prediction process is executed, the schedule information on the day of prediction may be acquired. However, events that cause a large error in the prediction of the number of residents (exceptional events in which the number of residents is expected to fluctuate significantly) are usually scheduled in advance. Therefore, the schedule information may be acquired only for the first time on the prediction day in consideration of the processing load when a huge number of employees are engaged.

In the present embodiment, the schedule information is acquired from the schedule management server 3 (see FIG. 1), but an alternative means may be used. For example, when the schedule management server 3 does not exist, for example, it may be acquired directly from the groupware or scheduler commonly used by users on the building 1 or each floor. Further, it may be acquired from a scheduler installed in a mobile terminal used by each user.

Returning to FIG. 3, the primary prediction unit 13 predicts whether or not each user is located on each floor (each area) at the prediction target time from the schedule information, so that each floor (each area) at the prediction target time Calculate the primary prediction value of the number of people.

That is, the primary prediction unit 13 is the schedule information stored in the schedule information storage unit 12, and is based on the schedule information input by each user who uses the predetermined area, for example, an employee on the predetermined floor (predetermined area). Then, the predicted value (primary predicted value) of the future number of people in the predetermined area is calculated. The specific calculation process will be described later.

The primary predicted value is calculated for each time. That is, the primary prediction unit 13 calculates the primary prediction value Y (i) at time i from the prediction start time i = 1 to the prediction end time i = k.

The primary predicted value Y (i) predicted by the primary predicted value unit 13 is stored in the primary predicted value information storage unit 14. For example, the primary predicted value information storage unit 14 stores the primary predicted value Y (i) for each floor (by area) and by time.

The location number acquisition unit 15 periodically (for example, every minute) acquires the current number of locations, that is, the actual value of the number of locations, for each floor (each area) in the building 1, and the location number information storage unit 16 save. For example, the number-of-location acquisition unit 15 may include a number-of-person count sensor installed on each floor (each area). Alternatively, the number-of-location acquisition unit 15 may include a measuring instrument for measuring the number of people getting on and off the elevator.

For example, the location number information stored in the location number information storage unit 16 is associated with at least the location number of each floor acquired from the location number acquisition unit 15, the floor (area) where the location number was acquired, and the acquisition date and time. It is formed on each floor.

The correction parameter setting unit 17 has a history of the number of people in location information stored in the number of people in location information storage unit 16, in other words, an actual value Y * (i) and a primary prediction value Y stored in the primary prediction value information storage unit 14 ( The correction parameter A (i) is calculated based on the difference from i). This detailed calculation process will be described later.

The correction parameter A (i) is set according to each floor (each area) and the time to be predicted, and is a parameter for compensating for a defect in the scheduler operation. When the input (registration) of the schedule information to the scheduler is left to the discretion of the user (employee) on each floor, schedule information that is not registered in the scheduler such as a short absence may occur. In addition, inaccurate schedule information such as incorrect date and time or location can be registered in the scheduler.

Due to these unregistered schedule information and inaccurate schedule information, the number of absentees on a predetermined floor (predetermined area) may be estimated to be less than the actual number. As a result, the primary predicted value Y (i) calculated based on the schedule information may have a positive error.

Therefore, in the location number prediction process according to the present embodiment, the correction parameter A (i) is used as a ratio for removing the above error from the primary prediction value Y (i).

Considering that the above-mentioned deficiency in scheduler operation is due to the customs of users on each floor, in other words, the habits of employees in each area, the deficiency rate is considered to be constant over multiple dates and times. Moreover, the deficiency rate is considered to be unique on each floor (each area). Therefore, as will be described later, the correction parameter setting unit 17 calculates the correction parameter A (i) at predetermined time intervals (for example, 30-minute intervals) for each floor (each area).

FIG. 5 illustrates the correction parameter A (i) for each area and each time. For example, the correction parameter setting unit 17 sets the correction parameter A (i) at a predetermined time interval (for example, every 30 minutes) based on the difference between the actual value Y * (i) of the number of people and the primary predicted value Y (i). ) Is calculated. This calculation process will be described later. The correction parameter A (i) can take, for example, a value of 0 or more and 1 or less, in other words, a value of 0% or more and 100% or less. Further, the correction parameter A (i) may be set to have a negative value.

The correction parameter A (i) calculated by the correction parameter setting unit 17 is stored in the correction parameter information storage unit 18. The correction parameter information storage unit 18 stores the correction parameter A (i) for each floor (by area) and by time.

The correction unit 19 corrects the primary prediction value Y (i) calculated by the primary prediction unit 13 using the correction parameter A (i) calculated by the correction parameter setting unit 17, and corrects the primary prediction value Y (i) on each floor (each area). The secondary predicted value X (i) of the number of people in the area is calculated. This secondary predicted value X (i) becomes the final predicted value of the number of people at time i. The detailed calculation process of the secondary predicted value X (i) will be described later.

FIG. 6 illustrates a characteristic line of the primary predicted value Y (i) when the time 10:00 is the prediction start time (i = 1) and the time 22:00 is the prediction end time (i = k). .. In the time zone before 10:00, the actual value of the number of people in the area is exemplified. In the graph of FIG. 6, the horizontal axis shows the time and the vertical axis shows the number of people.

For example, assume that the primary predicted value Y (i) contains a positive error. This positive error is corrected by the correction parameter A (i) and plotted as the quadratic predicted value X (i) as illustrated in FIG.

<Number of people predicted processing>
Next, with reference to FIG. 8, the location number prediction process according to the present embodiment will be described. As described above, in the location prediction process according to the present embodiment, the primary prediction value Y (i) and the correction parameter A (i) are obtained, respectively, and the primary prediction value Y (i) and the correction parameter A (i) are obtained. ) Is used to obtain the secondary predicted value X (i).

As described above, the schedule information stored in the schedule information storage unit 12 is updated periodically (for example, every 30 minutes). At the time of this update, the location number prediction processing flow of FIG. 8 is started. The primary prediction unit 13 sets the count i of the prediction target time time_i to the initial value 1 (S101).

The prediction target time time_1 when the initial value i = 1 may be, for example, the start time of the location number prediction processing flow (10:00 in FIG. 6). Further, the prediction target time time_k when the final value i = k may be, for example, the final time of the location number prediction target time zone, for example, 22:00.

First, the primary prediction unit 13 is the number of users (employees) on the prediction target floor (prediction target area), that is, the number of people estimated to be absent on the floor at the prediction target time based on the schedule information (the number of people). The primary predicted value Y (i) is calculated by subtracting the number of absentees).

For example, the primary prediction unit 13 sets the number of users (employees) who are enrolled in a predetermined floor (predetermined area) as the capacity, and increases or decreases this capacity based on the number of absentees predicted from the schedule information. Then, let this value be the primary predicted value Y (i). For example, the value obtained by subtracting the number of schedules n_skd_i at the prediction target time time_i from the capacity is defined as the primary prediction value Y (i) (S102). If the number of schedules n_skd_i does not directly become the number of absentees, the number of schedules n_skd_i may be corrected and subtracted from the capacity, and the subtracted value may be used as the primary predicted value Y (i). For example, if the schedule implementation area is the work area (does not leave the predetermined area), the area different from the office area (predetermined floor, predetermined area) among the number of schedules n_skd_i is set as the implementation area. The number of schedules may be subtracted from the capacity and used as the primary predicted value Y (i).

For example, suppose that the number of employees using the 4th floor, that is, the capacity is 100, and the schedule on the predicted day is only 7 cases illustrated in FIG. When predicting the number of people at 15:00, user u001 will have a meeting on the 6th floor, user u002 will go out, and user u003 will have a meeting on the 5th floor, so these three people will be absent on the 4th floor as of 15:00. It can be estimated that (moves to another area or outside building 1). Therefore, 100-3 = 97 people are calculated as the primary predicted value Y (i = 15:00) of the number of people in the area at 15:00. The calculated primary predicted value Y (i) is stored in the primary predicted value information storage unit 14.

Next, the correction parameter setting unit 17 obtains the correction parameter A (i) based on the past primary predicted value Y (i) and the actual value Y * (i) of the number of people located at the same time on the same day (S103). The correction parameter setting unit 17 obtains the history of the primary predicted value Y (i) stored in the primary predicted value information storage unit 14 for the past predetermined period (excluding holidays). Further, the correction parameter setting unit 17 obtains the history of the corresponding number of people (actual value) Y * (i) stored in the number of people information storage unit 16.

Further, the correction parameter setting unit 17 calculates the correction parameter A (i) for each floor and each time. For example, the correction parameter setting unit 17 calculates the average value of "(primary predicted value-actual value) / primary predicted value" at each time on each floor (each area) as the correction parameter A (i).

That is, the past primary predicted value at a predetermined date d (d = 1 to n) and time i is Y (d, i), and the actual value of the number of people at the same time on the same day is Y * (d, i). The correction parameter A (i) at the time i on the predetermined floor (area) can be described by the following mathematical formula (1).

Note that the correction parameter setting process in step S103 does not have to be performed at the time of all executions of the location number prediction process of FIG. For example, the correction parameter setting process may be executed when the location number prediction process is executed for the first time, and then executed at an arbitrary timing (for example, every month) to update the correction parameter A (i).

Further, the location number prediction device 10 according to the present embodiment is not provided with the correction parameter setting unit 17, and the correction parameter A (i) is obtained by another arithmetic unit and given to the location number prediction device 10. May be good. In any case, the calculated correction parameter A (i) is stored in the correction parameter information storage unit 18.

When the correction parameter A (i) is calculated, the correction unit 19 calculates the secondary predicted value X (i) at the time time (i) (S104). Specifically, the secondary predicted value X (i) is calculated based on the following mathematical formula (2).

Further, the calculated secondary predicted value X (i) is output to the display 27 (see FIG. 2) (S105). Further, in addition to the display on the display 27, the secondary predicted value X (i) may be stored in the HDD 24. Further, the information to be output may not be the predicted value of the number of people in the area, but may be output with a range such as several percent before and after the predicted value.

After outputting the secondary prediction value X (i), the primary prediction unit 13 determines whether or not the count i is the final value k (S106). When the count i has reached the final value k, the location number prediction processing flow shown in FIG. 8 ends. On the other hand, if the count i has not yet reached the final value k, the primary prediction unit 13 increments the count i (S107) and returns to step S102.

As described above, according to the present embodiment, the primary predicted value of the number of residents predicted from the schedule information is corrected according to the predicted target floor and the predicted target time, and the predicted value (secondary predicted value) is applied. ) Is calculated. As a result, it is possible to suppress actions that are not registered as schedule information, omission of registration of schedule information, and prediction error caused by registration error.

In addition, since the correction parameters for each floor and time are calculated and set from the difference between the primary predicted value and the actual value of the number of people, it is possible to set the correction parameters that reflect the characteristics of the users for each floor. it can.

<Embodiment 2. ＞
In the first embodiment described above, in calculating the primary predicted value Y (i), the number of absentees predicted from the schedule information is subtracted from the number of people on each floor (area), but instead of the number of people working May be used.

For example, due to work outside the specified working hours (overtime) or the application of the flextime system, it is often the case that the user goes to work at the specified start time and leaves the work at the specified end time. In addition, it is generally considered that schedule registration is not performed for work time and work time. That is, outside working hours, the users (employees) who make up the capacity of each floor (area) are
(1) Users who are registered in the schedule (2) Users who are not scheduled to be registered in the schedule but are located (overtime) (3) There are no plans to be registered in the schedule and they are absent (before going to work) Or it can be divided into three users (after leaving work). In the present embodiment, in calculating the primary predicted value Y (i), the number of working people obtained by reducing the number of people in (3) from the capacity is used.

FIG. 9 exemplifies a block configuration diagram of the location number prediction device 10 according to the present embodiment. The difference from FIG. 3 is that the attendance / leaving information acquisition unit 40 and the attendance / leaving information storage unit 41 have been added to the location number prediction device 10. In the following description, the description of the configuration with the same reference numerals as those in FIG. 3 will be omitted as appropriate.

The attendance / leaving information storage unit 41 is connected to the upper level of the primary prediction unit 13. Further, the attendance / leaving information acquisition unit 40 is connected to the upper level of the attendance / leaving information storage unit 41. The attendance / leaving information acquisition unit 40 acquires attendance / leaving information of a user (employee) who works in the building 1 from the attendance / leaving management device 5 provided in the building 1 (see FIG. 1).

As described above, for example, the attendance management device 5 includes an attendance system installed on each floor in the building 1 and accessible from a terminal assigned to each employee. Alternatively, the attendance / leaving management device 5 includes an entry / exit management system for the building 1.

In the case of the attendance system, when an employee accesses the attendance system from a terminal and stamps the attendance time and the attendance time, the attendance / attendance management device 5 records the attendance / leaving information of each employee in the building 1. For example, information such as an employee's ID (name, employee number, etc.), work floor (work area), affiliation, etc. is stored in the storage unit of the attendance management device 5 in association with the attendance time and the leaving time.

In the case of the entrance / exit management system, each time each user (employee) goes to work or leaves the office based on the entrance / exit information to the building 1 or the entrance / exit information to each floor, the user's identification information and the arrival time Alternatively, the leaving time is set and stored in the storage unit of the attendance management device 5.

The attendance / leaving information acquisition unit 40 accesses the storage unit of the attendance / attendance management device 5 and acquires attendance / leaving information for each user (employee) on each floor (each area). The acquired attendance / leaving information is stored in the attendance / leaving information storage unit 41. For example, information such as an employee ID (name, employee number, etc.), office floor (office area), affiliation, etc. is stored in the attendance / leaving information storage unit 41 in association with the attendance time and the leaving time.

In the location number prediction process, the primary prediction unit 13 refers to the history of attendance / leaving information in addition to the schedule information to predict whether or not each user is located on the prediction target floor at the prediction target time, and predicts whether or not each user is located on the prediction target floor. Calculate the primary forecast value of the number of people on the forecast target floor.

FIG. 10 exemplifies the location number prediction processing according to the present embodiment. The difference from FIG. 8 is that step S201 is inserted between step S101 and step S102. In the following description, the description of the steps with the same reference numerals as those in FIG. 8 will be omitted as appropriate.

In step S201, the primary prediction unit 13 acquires the number of employees on each floor at the prediction target time time_i from the attendance / leaving information storage unit 41. For example, a user (employee) who has an attendance record at a time before the predicted target time time_i and no attendance record after that is added to the number of employees S (i) at the predicted target time time_i.

In addition, for users whose forecast start time is early in the morning and there is no attendance record (naturally, there is also no attendance record), the predicted values of the attendance time and the leaving time are obtained from the history of the attendance time and the leaving time, and this is the work. It may be used to calculate the number of people S (i). For example, the attendance time and the leaving time are statistically obtained from the history of each user. For example, after obtaining the average value and the mode value, the predicted values of the attending time and the leaving time are obtained, and the predetermined user at the predicted target time. May be estimated whether or not is working. This estimation is not limited to early morning, but when the forecast target time is night and there is only attendance record at the start of forecast and there is no attendance record, but there is a possibility that you are leaving work at the forecast target time. It is also applicable to.

The primary prediction unit 13 calculates the primary prediction value Y (i) at the prediction target time time_i based on the estimation result of whether or not the user is working, that is, the number of employees S (i) and the schedule information. For example, the value obtained by subtracting the number of schedules n_skd_i at the prediction target time time_i from the number of employees S (i) is defined as the primary prediction value Y (i). Alternatively, as described above, of the number of schedules n_skd_i, the value obtained by subtracting the number of schedules whose implementation area is different from the work area (predetermined floor, predetermined area) from the number of employees S (i) is the primary predicted value Y ( i).

According to this embodiment, whether or not each user is working at the time to be predicted is estimated from the history of attendance / leaving information, and the number of employees is predicted based on the result and the schedule information. It is possible to suppress the prediction error including the time zone after the time.

1 Building, 2 Equipment management device, 3 Schedule management server, 4 Network, 5 Attendance management device, 10 Location prediction device, 11 Schedule information acquisition unit, 12 Schedule information storage unit, 13 Primary prediction unit, 14 Primary prediction value information storage Department, 15 location number acquisition unit, 16 location number information storage unit, 17 correction parameter setting unit, 18 correction parameter information storage unit, 19 correction unit, 40 attendance / leaving information acquisition unit, 41 attendance / departure information storage unit.

Claims

A schedule information acquisition unit that acquires schedule information for each user who uses a predetermined area in the building,
A primary prediction unit that calculates the primary prediction value of the number of people in the area at the prediction target time by predicting whether or not each user is located in the area at the prediction target time from the schedule information.
A correction unit that corrects the primary prediction value with correction parameters set according to the area and the prediction target time to calculate the secondary prediction value of the number of people in the area.
A number-of-location predictor.
The number-of-location prediction device according to claim 1.
The number of people in the area acquisition department that acquires the actual value of the number of people in the area
A correction parameter setting unit that sets the correction parameter based on the difference between the primary predicted value and the actual value of the number of people in the area at the same time.
A number prediction device that further has.
The number-of-location predictor according to claim 1 or 2.
It also has an attendance / leaving information acquisition department that acquires attendance / leaving information for each user in the area.
The primary prediction unit predicts the number of employees in the area at the time to be predicted by using the history of attendance / leaving information of each user, and based on the number of employees and the schedule information, the primary prediction value is calculated. A number-of-location prediction device that calculates.
The number-of-location prediction device according to claim 3.
The primary prediction unit calculates the primary prediction value at the prediction target time by subtracting the number of absentees at the prediction target time predicted from the schedule information from the number of employees at the prediction target time. Predictor.
The location prediction device according to any one of claims 1 to 4,
An equipment management device that manages the equipment installed in the building based on the number of people in the area predicted by the number-of-location prediction device.
Has an equipment management system.
Steps to acquire schedule information of each user who uses a predetermined area in the building,
A step of predicting whether or not each user is located in the area at the predicted target time from the schedule information, and calculating a primary predicted value of the number of people located in the area at the predicted target time.
A step of correcting the primary predicted value with correction parameters set according to the area and the predicted target time to calculate a secondary predicted value of the number of people in the area.
How to predict the number of people, including.