Congestion prediction system

Abstract

To provide a technology for presenting information useful for predicting the number of visitors to a commercial facility side in the form of hiding the detailed information of an office in a composite building.SOLUTION: The storage device 105 stores the number of persons in the office 2 measured by the entry and exit management system 200 installed in the office 2. In CPU101, the degree of congestion of the office 2 on the day is calculated from the number of people in the office 2 stored in the storage device 105. The I / F device 104 transmits the congestion level of the office 2 on the day to the terminal 300 included in the tenant 3.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a congestion prediction system that predicts the degree of congestion in a complex building that houses offices and commercial facilities. [Background technology]

[0002] In a complex building that houses offices and commercial facilities, the number of customers from tenants such as restaurants in the commercial facilities is affected by the office attendance rate (number of people present). Although it is possible to measure the number of people present in an office in an office that is equipped with an access control system, the information from the access control system is usually not available to tenants.

[0003] For this reason, tenants have no way of knowing information such as office attendance rates, and can only predict the number of visitors to their offices based on trends in the number of visitors to their offices. Meanwhile, in recent years, the use of telecommuting has led to fluctuations in attendance rates even on weekdays. For this reason, fluctuations in attendance rates due to telecommuting also affect the forecast of the number of visitors to their offices.

[0004] For example, Japanese Patent Application Laid-Open No. 2023-73859 (Patent Document 1) discloses a congestion information sharing system that counts the number of people staying in a building using user information obtained from an entrance / exit management system. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2023-73859 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the congestion information sharing system disclosed in Patent Document 1 does not take into account complex buildings that house offices and commercial facilities, and does not consider the flow of people from offices to commercial facilities in complex buildings.Furthermore, no particular consideration is given to how office information such as the number of people occupying the office should be handled as information to be provided to tenants (which information should be kept confidential and which information should be disclosed).

[0007] The present disclosure has been made to solve such problems, and the purpose of the present disclosure is to provide technology that can present useful information to commercial facilities for predicting the number of visitors in a complex building while concealing detailed office information. [Means for solving the problem]

[0008] The congestion prediction system disclosed herein is a system for predicting the congestion level of a complex building that houses offices and commercial facilities. The congestion prediction system includes a storage device, a processing device, and a communication device. The storage device stores the number of people in the office measured by an access control system installed in the office. The processing device calculates the congestion level of the office for that day from the number of people in the office stored in the storage device. The communication device transmits the congestion level of the office for that day to a terminal installed in the commercial facility.

[0009] The congestion prediction system of the present disclosure is a system for predicting the congestion level of a complex building that houses an office and a commercial facility. The congestion prediction system includes a storage device, a processing device, and a communication device. The storage device stores the number of people in the office measured by an access control system installed in the office and the congestion level of the commercial facility measured at the commercial facility. The processing device calculates the congestion level of the commercial facility from the number of people in the office and the congestion level of the commercial facility stored in the storage device. The communication device transmits the congestion level of the commercial facility to a terminal installed in the commercial facility. [Effects of the Invention]

[0010] According to the present disclosure, in a complex building, it is possible to present information useful for predicting the number of visitors to the commercial facility side while concealing detailed office information. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a functional block diagram of a congestion prediction system according to a first embodiment. [Figure 2] FIG. 1 is a diagram illustrating a hardware configuration of a congestion prediction system. [Figure 3] 10 is a flowchart of a process executed by the congestion prediction system. [Figure 4] FIG. 10 is a functional block diagram of a congestion prediction system according to a second embodiment. [Figure 5] FIG. 10 is a diagram for explaining a model for transition of the number of people in a room. [Figure 6] FIG. 10 is a diagram for explaining a model for transition of the number of people in a room. [Figure 7] FIG. 10 is a diagram for explaining the degree of congestion in an office. [Figure 8] FIG. 10 is a diagram for explaining the degree of congestion in an office. [Figure 9] 10 is a flowchart of a process executed by the congestion prediction system. [Figure 10] FIG. 10 is a functional block diagram of a congestion prediction system according to a third embodiment. [Figure 11] FIG. 10 is a diagram for explaining the relationship between the congestion degree of an office and the congestion degree of a tenant. [Figure 12] FIG. 10 is a diagram for explaining prediction of the degree of congestion of tenants. [Figure 13] 10 is a flowchart of a process executed by the congestion prediction system. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. While several embodiments will be described below, it was originally intended that the configurations described in each embodiment be combined as appropriate. Note that identical or corresponding parts in the drawings will be designated by the same reference numerals, and their description will not be repeated.

[0013] [First embodiment] 1 is a functional block diagram of a congestion prediction system 100 according to the first embodiment. In this embodiment, a complex building 1 is a complex building that houses offices 2 and commercial facilities (also referred to as "tenants") 3. The complex building 1 is made up of an area where the offices 2 are located and an area where the tenants 3 are located.

[0014] The office 2 may be made up of multiple companies or may be made up of a single company. The tenant 3 may be made up of multiple stores such as restaurants or may be made up of a single store. The congestion prediction system 100 is a system that predicts the degree of congestion in the complex building 1.

[0015] An entry / exit management system 200 is installed in the area where office 2 is located. The entry / exit management system 200 manages the entry and exit of all employees who come to work in office 2. All employees carry ID cards on which their employee IDs are recorded.

[0016] When an employee arrives at work, the ID card they are carrying is read by a card reader (not shown) installed at the entrance to office 2, and the number of people present detection unit 201 of the entry / exit management system 200 records that the employee has arrived at work (is present).

[0017] The entry / exit management system 200 may be configured to count the number of employees whose attendance is recorded on that day as the number of people present on that day. As a method for counting the "number of people present on that day," for example, the number of people present in office 2 at a specific time (for example, 11:00 AM) may be counted, the number of people present during a time period when attendance is at its peak may be counted, or the number of all employees whose attendance is recorded on that day may be counted as the number of people present. The number of people present for each time period may also be counted and recorded. The "number of people present on that day" recorded in this way is used to predict the degree of congestion, which will be described later.

[0018] The congestion prediction system 100 includes a building congestion degree calculation unit 22 and a notification unit 25. The congestion prediction system 100 also stores the number of people occupying an office 50 and a building congestion degree 80. The number of people occupying an office 50 includes the number of people occupying office 2 in the past (history) 51 and the number of people occupying office 2 on the day 52. ​​The building congestion degree 80 includes the number of people occupying office 2 in the past (history) 81 and the number of people occupying office 2 on the day 82.

[0019] The congestion prediction system 100 obtains the number of people present on that day from the entry / exit management system 200 and records this as the number of people present in office 2 on that day 52. ​​The congestion prediction system 100 obtains the number of people present on that day from the entry / exit management system 200 every day. The number of people present on the previous day and before is accumulated as the past number of people present in office 2 51.

[0020] The building congestion degree calculation unit 22 calculates and records the congestion degree 82 of office 2 on the day based on the number of people occupying office 2 on the day 52 and the number of people occupying office 2 in the past 51. The congestion degree calculated on the previous day or earlier is recorded as the past congestion degree 81 of office 2.

[0021] The congestion level 82 of office 2 on that day is calculated as follows. First, the average value of the number of people occupying the office 51 in the past is calculated. The average value is calculated as the average value for the previous year (365 days) (hereinafter referred to as the "average value for the previous year"). The average value for the previous year is calculated as the sum of the number of people occupying the office 51 for 365 days in the previous year / 365. The average value may be the average value for the year prior to the previous day, or the average value for a predetermined period (for example, two years), or the average value may be calculated by season or by time period.

[0022] Next, the congestion level 82 (unit [%]) for Office 2 on that day is calculated using the formula: congestion level 82 for Office 2 on that day = 100 × ((number of people in Office 2 on that day 52 - average value from the previous year) / average value from the previous year). For example, if the average number of people in the past 51 = 300 people and the number of people in Office 2 on that day 52 = 450 people, the congestion level 82 for Office 2 on that day = 100 × ((450 - 300) / 300) = 50 [%].

[0023] The notification unit 25 acquires the congestion level 82 of the office 2 for that day and sends it to the terminal 300. The terminal 300 is a terminal provided in the tenant 3, and is used by the manager or employees of the tenant 3 to check the congestion status within the building 2. The congestion level 82 of the office 2 for that day can be checked on the display screen of the terminal 300.

[0024] Terminal 300 is, for example, a mobile terminal such as a smartphone or tablet, or a personal computer. Terminal 300 includes a display unit that displays various information and an input unit that inputs command information. The display unit is, for example, a liquid crystal display unit or display of the mobile terminal. The input unit is, for example, a keyboard, a mouse, a touch panel, or the like.

[0025] 2 is a diagram showing the hardware configuration of the congestion prediction system 100. The congestion prediction system 100 includes a CPU (Central Processing Unit) 101 as a processing device, a RAM (Random Access Memory) 102, a ROM (Read Only Memory) 103, an I / F (Interface) device 104 as a communication device, and a storage device 105. The CPU 101, RAM 102, ROM 103, I / F device 104, and storage device 105 exchange various types of data via a communication bus 106.

[0026] The CPU 101 loads a program stored in the ROM 103 or the storage device 105 into the RAM 102 and executes it. The program stored in the ROM 103 or the storage device 105 describes the processes to be executed by the congestion prediction system 100. The I / F device 104 is an input / output device for exchanging signals and data with each device. The storage device 105 is a storage for storing various types of information.

[0027] The storage device 105 stores the number of people in the office 50 and the building congestion level 80. The CPU 101 also executes the function realized by the building congestion level calculation unit 22. The I / F device 104 executes the function realized by the notification unit 25.

[0028] The following describes the processing executed by the congestion prediction system 100 using a flowchart. Figure 3 is a flowchart of the processing executed by the congestion prediction system 100. When this processing starts, in S101, the congestion prediction system 100 obtains the number of people present on that day from the entry / exit management system 200 and stores this in the storage device 105.

[0029] The congestion prediction system 100 calculates the congestion level 82 of office 2 for that day from the number of people in office 2 acquired from the entry / exit management system 200 and stored in the storage device 105. Specifically, in S102, the congestion prediction system 100 calculates the congestion level of office 2 for that day as the increase rate of the number of people 52 in office 2 for that day relative to the average number of people in office 2 in the past, which is tallied on a daily basis (congestion level 82 of office 2 for that day = 100 × ((number of people 52 in office 2 for that day - average number of people in the past) / average number of people in the past)). Here, the average value for the previous year is used as the average number of people in the past.

[0030] In S103, the congestion prediction system 100 stores the congestion degree of the office 2 for that day in the storage device 105. In S104, the congestion prediction system 100 transmits the congestion degree of the office 2 for that day to the terminal 300 provided in the commercial facility (tenant) 3.

[0031] In the above example, the average value of past number of people occupying a room 51 is 300 people, the number of people occupying office 2 on that day 52 is 450 people, and the congestion level of office 2 on that day 82 is 50%. Employees of tenant 3 can check the congestion level of office 2 on that day 82 on terminal 300. What can be checked on terminal 300 is the congestion level of office 2 on that day 82, and they cannot see the number of people occupying office 2 on that day 52, which is management information of the company's access control system 200. By knowing that the congestion level of office 2 on that day 82 = 50%, employees of tenant 3 can know that the number of people attending office 2 is 50% higher than on an average day, and can therefore predict that tenant 3 will be crowded.

[0032] The congestion prediction system 100 may be configured to include an entry / exit management system 200. The number of people in the office 50 and the building congestion level 80 may be stored in a memory unit of the entry / exit management system 200, and this data may be read from the memory unit of the entry / exit management system 200. The congestion level sent to the terminal 300 is not limited to a numerical value between 0 and 100% as described above, but may be information indicating the degree of congestion, such as "high congestion level," "medium congestion level," or "low congestion level." If there are multiple tenants, the congestion level may be calculated and sent for each tenant.

[0033] As described above, the congestion prediction system 100 is a system that predicts the degree of congestion in a complex building 1 that houses an office 2 and a tenant 3. The congestion prediction system 100 includes a storage device 105, a CPU 101 as a processing device, and an I / F device 104 as a communication device. The storage device 105 stores the number of people in office 2 measured by an entry / exit management system 200 installed in office 2. The CPU 101 calculates the degree of congestion in office 2 for that day from the number of people in office 2 stored in the storage device 105. The I / F device 104 transmits the degree of congestion in office 2 for that day to a terminal 300 provided in tenant 3.

[0034] In this way, the entrance / exit management system 200 installed in office 2 is used to calculate the degree of congestion in office 2 on that day from the number of people in office 2. Detailed information such as the number of people in office 2 is concealed, and only the degree of congestion in office 2 on that day is transmitted to commercial facility 3. In this way, in a complex building 1 where office 2 and tenant 3 are located side by side, it is possible to present information useful for predicting the number of visitors to commercial facility 3 while concealing detailed information about office 2.

[0035] The CPU 101 calculates the percentage increase in the number of people in office 2 on that day relative to the average value of the past number of people in office 2, which is tallied on a daily basis, as the congestion level of office 2 on that day. This makes it possible to conceal detailed information such as the number of people in office 2, and provide the percentage increase in the number of people as the congestion level to the commercial facility 3.

[0036] [Second embodiment] 4 is a functional block diagram of a congestion prediction system 100a according to the second embodiment. The congestion prediction system 100 according to the first embodiment is configured to transmit the congestion level 82 of the office 2 on the current day to the terminal 300. In addition, the congestion prediction system 100a according to the second embodiment also calculates a future congestion level (from the next day onwards) 83 and transmits it to the terminal 300. The differences from the congestion prediction system 100 are explained below.

[0037] Compared to the congestion prediction system 100, the congestion prediction system 100a further includes an occupancy number transition model generation unit 91 and stores an occupancy number transition model 90. The building congestion level 80 further includes a future congestion level (from the next day onwards) 83 of the office 2. The processing details will be explained below with reference to FIG. 5 and subsequent figures.

[0038] 5 and 6 are diagrams for explaining the occupancy number transition model 90. As in the first embodiment, the occupancy number transition model generation unit 91 calculates the average value of the past occupancy numbers 51 in office 2 for the previous year (average value for the previous year) (300 people).

[0039] Next, the occupancy number transition model generation unit 91 calculates the average value for each day of the week (hereinafter referred to as the "weekday average value") for the past year's worth of occupancy numbers 51 in office 2. The calculated previous year average value and weekday average value are stored as the occupancy number transition model 90.

[0040] Graph 11 in Figure 5 plots the average value for the previous year (300 people) and the average value for each day of the week. The average values ​​for each day of the week are shown in Table 12 in Figure 6. For the past number of people occupying Office 2 51, the average value for Sunday was 30 people, the average value for Monday was 360 people, the average value for Tuesday was 300 people, the average value for Wednesday was 210 people, the average value for Thursday was 330 people, the average value for Friday was 270 people, and the average value for Saturday was 60 people.

[0041] 7 and 8 are diagrams for explaining the congestion degree of office 2. The building congestion degree calculation unit 22 calculates the congestion degree 82 of office 2 on the current day and estimates the congestion degree 82 of office 2 in the future (the next day and beyond). The method for calculating the congestion degree 82 of office 2 on the current day is the same as in the first embodiment. For example, if the average value of the past number of people 51 is 300 people and the number of people 52 in office 2 on the current day is 450 people, then the congestion degree 82 of office 2 on the current day (Tuesday, December 5th) is 100×((450−300) / 300)=50[%] (see FIGS. 7 and 8).

[0042] Meanwhile, the future congestion level 82 (from the next day onwards) of office 2 is calculated as follows: The building congestion level calculation unit 22 calculates the congestion level for one week (until Monday, December 11th) including the current day (Tuesday, December 5th) as shown in table 14 in Fig. 8. Each day of the week in this table (Fig. 8) corresponds to each day of the week in the occupancy transition model 90 (Fig. 6).

[0043] For 12 / 5 (Tuesday), which is the current day of "Ratio to the current day" in Table 14, the number of people in the room on Tuesday / number of people in the room on that day (Tuesday) = 300 / 300 = 1 is set in the number of people in the room transition model 90 (see Figure 6). For 12 / 6 (Wednesday), the number of people in the room on Wednesday / number of people in the room on that day (Tuesday) = 210 / 300 = 0.7 is set in the number of people in the room transition model 90. For 12 / 7 (Thursday), the number of people in the room on Thursday / number of people in the room on that day (Tuesday) = 330 / 300 = 1.1 is set. Calculations are made in the same manner.

[0044] In Table 14, for "Number of people present" on 12 / 5 (Tue), the actual number of people present on that day is set to "450 people." From 12 / 6 onwards, the predicted number of people present is set to the number of people present on that day multiplied by the ratio to the current day. For example, the number of people present on 12 / 6 (Wed) = 450 x 0.7 = 315 people, and the number of people present on 12 / 7 (Thu) = 450 x 1.1 = 495 people. Calculations are similar for the following days.

[0045] In Table 14, the "Congestion Level" for 12 / 5 (Tue) is set to "50%," which is the congestion level already calculated for that day. From 12 / 6 onwards, the "Number of people present" calculated above is used to set the predicted congestion level as 100 x (Number of people present - Average value from the previous year) / Average value from the previous year. For example, the congestion level for 12 / 6 (Wed) = 100 x (315 - 300) / 300 = 5%, and the congestion level for 12 / 7 (Thu) = 100 x (495 - 300) / 300 = 65%. Calculations are made in the same way for the following days.

[0046] The processing executed by the congestion prediction system 100a will be explained below using a flowchart. Figure 9 is a flowchart of the processing executed by the congestion prediction system 100a. When this processing starts, in S201, the congestion prediction system 100a obtains the number of people present on that day from the entry / exit management system 200 and stores this in the storage device 105.

[0047] The congestion prediction system 100a generates an occupancy number transition model 90 that indicates the transition of the number of people in office 2 from the number of people in office 2 stored in the storage device 105. Specifically, in S202, the congestion prediction system 100a sets the average number of people in office 2 in the past, calculated on a daily basis (average value for the previous year), and the average number of people in office 2 in the past, by day of the week (average value for each day of the week), as the occupancy number transition model 90. In the above example, the occupancy number transition model 90 is set as shown in FIGS. 5 and 6.

[0048] In S203, the congestion prediction system 100 calculates the congestion level of office 2 on that day as the percentage increase in the number of people in office 2 on that day relative to the average number of people in office 2 in the past, calculated on a daily basis (congestion level of office 2 on that day 82 = 100 x ((number of people in office 2 on that day 52 - average number of people in the past) / average number of people in the past)). Here, the average value for the previous year is used as the average number of people in the past.

[0049] The congestion prediction system 100 estimates the future congestion level of office 2 using the number of people present in office 2 on that day and the number of people transition model 90. Specifically, in S204, the congestion prediction system 100 first calculates the predicted number of people present for each day of the week by dividing the average number of people present for each day of the week (weekday average value) by the average number of people present (previous year average value) and multiplying the result by the number of people present in office 2 on that day. In the above example, this corresponds to the "number of people present" in table 14 in FIG. 8.

[0050] Next, in S205, the congestion prediction system 100 calculates the future congestion level of office 2 as the rate of increase in the predicted number of people present for each day of the week relative to the average number of people present (average value for the previous year) (future congestion level of office 2 = 100 x (predicted number of people present for each day of the week - average value for the previous year) / average value for the previous year). In the above example, this corresponds to the "congestion level" in table 14 in Figure 8 and graph 13 in Figure 7.

[0051] In S206, the congestion prediction system 100a stores the congestion level 82 for the day and the future congestion level 81 for office 2 in the storage device 105. In S207, the congestion prediction system 100a transmits the congestion level 82 for the day and the future congestion level 81 for office 2 to the terminal 300 provided in tenant 3. This allows employees of tenant 3 to know the congestion level 82 for the day and the future congestion level 81 for office 2, and enables predictions of the congestion situation for tenant 3 for the day and in the future.

[0052] As described above, the CPU 101 generates transition data (occupancy number transition model 90) indicating a transition in the number of people in office 2 from the number of people in office 2 stored in the storage device 105. The CPU 101 estimates the future congestion level of office 2 using the number of people in office 2 on the day and the transition data (occupancy number transition model 90). The I / F device 104 further transmits the future congestion level of office 2 to the terminal 300. In this way, not only the congestion level of office 2 on the day but also the future congestion level of office 2 is transmitted to the tenant 3, and useful information for predicting the number of visitors in the future is presented to the tenant 3 with detailed information about office 2 concealed, making it easier to formulate business plans for the tenant (commercial facility) 3.

[0053] The CPU 101 sets the average number of people in office 2 in the past, which is tallied on a daily basis, and the average number of people in office 2 in the past, by day of the week, as transition data (number of people transition model 90). The CPU 101 calculates the predicted number of people in office 2 for each day of the week by dividing the average number of people in office 2 for each day of the week by the average number of people in office 2, and multiplying the result by the number of people in office 2 on that day. The CPU 101 calculates the rate of increase in the predicted number of people in office 2 for each day of the week relative to the average number of people in office 2 as the future congestion level of office 2. This makes it possible to provide the tenant 3 with a predicted value of future congestion level that accurately reflects fluctuations in the number of visitors for each day of the week, while concealing detailed information such as the number of people in office 2.

[0054] [Third embodiment] FIG. 10 is a functional block diagram of a congestion prediction system 100b according to the third embodiment. The congestion prediction system 100a according to the second embodiment is configured to transmit the congestion level 82 of the current day and the future congestion level 83 of office 2 to the terminal 300. On the other hand, the congestion prediction system 100b according to the third embodiment is configured to estimate the congestion level of tenant 3, rather than the congestion level of office 2, and transmit this to the terminal 300 of tenant 3. Furthermore, this information can be confirmed not only by the terminal 300, but also by a terminal 400 owned by a user 5 of tenant 3 (such as an employee of office 2). Below, the differences from the congestion prediction system 100b will be explained.

[0055] The congestion prediction system 100b further includes a congestion degree prediction calculation unit 23 and stores a tenant congestion degree 70. The tenant congestion degree 70 includes a past congestion degree (history) 71 of the tenant 3 and a predicted congestion degree (prediction) 72 of the tenant 3. The processing contents will be explained below with reference to FIG. 11 and subsequent figures.

[0056] 11 is a diagram illustrating the relationship between the congestion level of office 2 and the congestion level of tenant 3. Table 15 shows the average past congestion level of office 2 for each day of the week and the average past congestion level of commercial facility (tenant) 3 for each day of the week.

[0057] The average past congestion level for each day of the week for Office 2 is calculated using the formula: average past congestion level for each day of the week for Office 2 = 100 x ((average past number of people in Office 2 for each day of the week (average day of the week) - average value for the previous year) / average value for the previous year). The average day of the week values ​​are as shown in Figure 6.

[0058] For example, the average past number of people occupying 51 = 300 people, and the average past Sunday congestion level for Office 2 = 30 people, so the average past Sunday congestion level for Office 2 = 100 x ((30-300) / 300) = -90[%]. The average past Monday congestion level for Office 2 = 360 people, so the average past Monday congestion level for Office 2 = 100 x ((360-300) / 300) = 20[%]. The same calculations can be made below.

[0059] The average past congestion level for Tenant 3 by day of the week indicates the average number of visitors to Tenant 3 by day of the week during a certain time period (for example, lunchtime, 6:00 PM to 7:00 PM, etc.). In this example, the number of visitors to Tenant 3 during lunchtime on Sundays in the previous year was 14, and similarly, Table 15 shows that there were 58 visitors on Mondays, 50 visitors on Tuesdays, 38 visitors on Wednesdays, 54 visitors on Thursdays, 46 visitors on Fridays, and 18 visitors on Saturdays. Note that Tenant 3's congestion level may indicate the number of visitors during a certain time period in this way, but, like Office 2's congestion level, it may also be a numerical value indicating the percentage increase in the number of visitors during a certain time period from the average for the previous year.

[0060] The past congestion level of tenant 3 is measured by a congestion level measurement device (not shown) installed in tenant 3. A congestion level recording unit 301 of the congestion level measurement device accumulates the past congestion levels of tenant 3. The past congestion level of tenant 3 (here, the number of visitors) can be measured using publicly known technology (for example, counting the number of people by analyzing images from an installed camera). The congestion level prediction system 10b acquires the past congestion level of tenant 3 from the congestion level recording unit 301 and saves it as the past congestion level (history) 71 of tenant 3 in the tenant congestion level 70.

[0061] The congestion level prediction calculation unit 23 uses the table 15 to generate a relational expression that indicates the correspondence between the past congestion level 81 of the office 2 and the past congestion level 71 of the tenant 3. This relational expression may be derived using, for example, the least squares method. Alternatively, instead of a relational expression, a map that indicates the correspondence between the past congestion level 81 of the office 2 and the past congestion level 71 of the tenant 3 may be generated.

[0062] In this example, based on Table 15, a linear equation is derived: Tenant 3's congestion level = 40 × (1 + Office 2's congestion level / 100) + 10. This equation indicates that when Office 2's congestion level = 0% (average value for the previous year), 40 people visit Tenant 3 from Office 2, and for every 10% increase in Office 2's congestion level, an additional 4 people visit Tenant 3 from Office 2. On the other hand, it indicates that 10 people other than those working in Office 2 (from outside Office 2) visit Tenant 3 regardless of the day of the week. Note that this is merely an example, and the number of people visiting Tenant 3 from outside Office 2 may differ depending on the day of the week.

[0063] 12 is a diagram for explaining the prediction of the congestion degree of tenant 3. The congestion degree prediction calculation unit 23 estimates the congestion degrees of tenant 3 on the day and in the future using the above relational expression and the congestion degrees of office 2 on the day and in the future.

[0064] 12, "Office Crowding Level" in Table 16 indicates the current and future congestion levels of Office 2. "Tenant Crowding Level" in Table 16 indicates the current and future congestion levels of Tenant 3 calculated using the above relational expression.

[0065] The "office congestion level" in table 16 is the same as the congestion level in table 14 (FIG. 8). Using the same method as in the second embodiment, the congestion level 82 of office 2 on the current day (December 5th (Tuesday)) and the congestion level 81 of office 2 in the future (December 6th (Wednesday) to December 11th (Monday)) are calculated.

[0066] Using the relational expression, the congestion degree prediction calculation unit 23 calculates the congestion degree for tenant 3 in table 16 on the current day (Tuesday, December 5th) as 40×(1+50 / 100)+10=70 people. The congestion degree for tenant 3 on the following day (Wednesday, December 6th) is calculated as 40×(1+5 / 100)+10=52 people. Subsequent calculations can be made in the same manner. The congestion degree prediction calculation unit 23 saves the calculated congestion degree as a predicted congestion degree 72 for tenant 3. The notification unit 25 transmits the predicted congestion degree 72 to the terminals 300, 400.

[0067] The processing executed by the congestion prediction system 100b will be explained below using a flowchart. Fig. 13 is a flowchart of the processing executed by the congestion prediction system 100b. The processing of S301 to S306 executed by the congestion prediction system 100b is the same as the processing of S201 to S206 executed by the congestion prediction system 100a, so a description of the processing of S301 to S306 will be omitted.

[0068] In S301 to S306, the congestion prediction system 100b generates an occupancy trend model 90 that shows the trend in the number of people occupying office 2, and performs processing such as estimating the future congestion level of office 2 using the number of people occupying office 2 on that day and the occupancy trend model 90.

[0069] In S307, the congestion prediction system 100b acquires from tenant 3 the congestion level of tenant 3 measured at tenant 3 and stores it in the storage device 105. The congestion prediction system 100b calculates the congestion level of tenant 3 from the number of people in office 2 and the congestion level of tenant 3 stored in the storage device 105. Specifically, the following process is performed.

[0070] In S308, the congestion prediction system 100b generates a relational expression that indicates the correspondence relationship between the past congestion level of office 2 and the past congestion level of tenant 3. As explained in the above example, first, the past congestion level of office 2 (FIG. 11) is calculated from the past number of people occupying office 2 (FIG. 6). Then, a relational expression is generated from the past congestion levels of office 2 and tenant 3. In the above example, the linear expression of tenant 3 congestion level = 40 × (1 + office 2 congestion level / 100) + 10 is derived.

[0071] In S309, congestion prediction system 100b estimates the congestion level of tenant 3 for that day using the congestion level of office 2 for that day and the relational expression. In the above example, the congestion level of tenant 3 for that day (Tuesday, December 5th) is calculated to be 70 [people] (FIG. 12). In S308, congestion prediction system 100b estimates the future congestion level of tenant 3 using the future congestion level of office 2 and the relational expression. In the above example, the future congestion level of tenant 3 (from Wednesday, December 6th onwards) is calculated as shown in FIG. 12.

[0072] In S311, the congestion prediction system 100b transmits the congestion level for the current day and the congestion level for future days to the terminals 300 and 400 as the congestion level of tenant 3. This allows employees of tenant 3 or users of tenant 3 to know the predicted congestion level for tenant 3 for the current day or the next day and beyond. If there are multiple tenants, the congestion level can be calculated and transmitted for each tenant.

[0073] As described above, congestion prediction system 100b is a system that predicts the congestion level of complex building 1, which includes office 2 and tenant 3. Congestion prediction system 100b includes storage device 105, CPU 101, and I / F device 104. Storage device 105 stores the number of people in office 2 measured by the entry / exit management system 200 installed in office 2 and the congestion level of tenant 3 measured at tenant 3. CPU 101 calculates the congestion level of tenant 3 from the number of people in office 2 and the congestion level of tenant 3 stored in storage device 105. I / F device 104 transmits the congestion level of tenant 3 to terminal 300 provided in tenant 3.

[0074] In this way, the entry / exit management system 200 installed in office 2 is used to obtain the number of people in office 2, and from this number of people and the congestion level of tenant 3, the congestion level of tenant 3, which directly contributes to predicting visitors to tenant 3, is calculated. Detailed information such as the number of people in office 2 is concealed, and only the congestion level of tenant 3 is sent to the commercial facility 3. As a result, in a complex building 1 where office 2 and tenant 3 are located side by side, it is possible to present information that can be directly used to predict the number of visitors to the commercial facility 3, with detailed information about office 2 concealed. This makes it even easier to create business plans for tenant 3.

[0075] CPU 101 generates a relational expression that indicates the correspondence relationship between the past congestion levels of office 2 and tenant 3. CPU 101 estimates the congestion level of tenant 3 for that day using the congestion level of office 2 for that day and the relational expression. I / F device 104 transmits the congestion level of tenant 3 for that day to terminal 300. In this way, using the correspondence relationship between the past congestion levels of office 2 and tenant 3, the degree of influence of the congestion level of office 2 on the congestion level of tenant 3 is derived as a relational expression. By calculating the congestion level of tenant 3 using this relational expression, it is possible to present to commercial facility 3 information that appropriately reflects the attendance rate of office 2 and is useful for predicting the number of visitors.

[0076] The CPU 101 generates transition data (occupancy number transition model 90) showing the transition of the number of people in office 2 from the number of people in office 2 stored in the storage device 105. The CPU 101 estimates the future congestion level of office 2 using the number of people in office 2 on that day and the transition data (occupancy number transition model 90). The CPU 101 estimates the future congestion level of tenant 3 using the future congestion level of office 2 and the relational expression. The I / F device 104 further transmits the future congestion level of tenant 3 to the terminal 300. In this way, by transmitting not only the congestion level of tenant 3 on that day but also the predicted value of the future congestion level of tenant 3 to the commercial facility 3, it becomes easier to formulate business plans for tenant 3.

[0077] The I / F device 104 also transmits the current and future congestion levels of tenant 3 to the terminal 400 owned by the user 5 of tenant 3. Receiving the predicted congestion level of tenant 3 is useful for the user 5 when considering which store in tenant 3 to choose. This improves convenience for the user 5.

[0078] The embodiments disclosed herein are merely examples and are not limited to the above. The scope of the present invention is defined by the claims, and it is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0079] 1 Building, 2 Office, 3 Commercial facility, 5 User, 11 Graph, 12 Table, 13 Graph, 14 Table, 22 Building congestion calculation unit, 23 Congestion prediction calculation unit, 24 Building congestion calculation unit, 25 Notification unit, 50 Number of people in office, 51 Past number of people (past), 52 Number of people in today (today), 70 Tenant congestion, 71 Past congestion (past), 72 Predicted congestion (prediction), 80 Building congestion, 81 Past congestion (past), 82 Today's congestion (today), 83 Future congestion (next day and beyond), 90 Number of people transition model, 91 Number of people transition model generation unit, 100, 100a, 100b Congestion prediction system, 101 CPU, 102 RAM, 103 ROM, 104 I / F device, 105 Storage device, 106 Communication bus, 200 Access control system, 201, occupancy detection unit, 300 terminal, 400 terminal.

Claims

1. A congestion prediction system that predicts the degree of congestion in a complex building that houses offices and commercial facilities, a storage device that stores the number of people in the office measured by an access control system installed in the office; a processing device that calculates the congestion level of the office on that day based on the number of people in the office stored in the storage device; A congestion prediction system comprising a communication device that transmits the degree of congestion of the office on that day to a terminal provided in the commercial facility.

2. The congestion prediction system of claim 1, wherein the processing device calculates the degree of congestion of the office on that day as the percentage increase in the number of people occupying the office on that day relative to the average number of people occupying the office in the past, calculated on a daily basis.

3. The processing device includes: generating transition data indicating a transition of the number of people in the office from the number of people in the office stored in the storage device; Using the number of people in the office on that day and the transition data, a future congestion level of the office is estimated; The congestion prediction system according to claim 1 or 2, wherein the communication device further transmits a future congestion degree of the office to the terminal.

4. The processing device includes: The average number of people in the office in the past, which is collected on a daily basis, and the average number of people in the office in the past, which is collected on a daily basis, are set as the transition data; Calculating a predicted number of people in the office for each day of the week by dividing the average number of people in the office for each day of the week by the average number of people in the office for each day of the week; The congestion prediction system according to claim 3 , wherein the future congestion level of the office is calculated as an increase rate of the predicted number of people for each day of the week relative to the average number of people.

5. A congestion prediction system that predicts the degree of congestion in a complex building that houses offices and commercial facilities, a storage device that stores the number of people in the office measured by an entry / exit management system installed in the office and the degree of congestion of the commercial facility measured at the commercial facility; a processing device that calculates a congestion level of the commercial facility based on the number of people in the office and the congestion level of the commercial facility stored in the storage device; A congestion prediction system comprising: a communication device that transmits the degree of congestion of the commercial facility to a terminal provided in the commercial facility.

6. The processing device includes: generating a relational expression indicating a correspondence relationship between the past congestion degree of the office and the past congestion degree of the commercial facility; estimating the congestion level of the commercial facility on that day using the congestion level of the office on that day and the relational expression; The congestion prediction system according to claim 5 , wherein the communication device transmits a congestion level of the commercial facility on that day to the terminal.

7. The processing device includes: generating transition data indicating a transition of the number of people in the office from the number of people in the office stored in the storage device; Using the number of people in the office on that day and the transition data, a future congestion level of the office is estimated; estimating a future congestion degree of the commercial facility using the future congestion degree of the office and the relational expression; The congestion prediction system according to claim 6 , wherein the communication device further transmits a future congestion degree of the commercial facility to the terminal.

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