Elevator operation prediction system

Abstract

This elevator operation prediction system comprises: a prediction management device that predicts an elevator operation in an apartment house; and a management terminal that acquires event schedule information about an event scheduled in the apartment house. The prediction management device: stores operation history information indicating the history of the elevator operation, user number history information indicating the history of the number of users of an elevator for each hour, and event history information indicating the history of events carried out in the past in the apartment house; predicts an elevator congestion degree on the basis of the operation history information, the user number history information, the event history information, and the event schedule information acquired by the management terminal; and generates data for displaying the predicted congestion degree.

Description

[Technical Field]

[0001] The present disclosure relates to an elevator operation prediction system. [Background technology]

[0002] A conventional elevator congestion prediction and display system includes a collected data storage unit, an elevator operation simulator, and a display unit. The collected data storage unit stores past elevator operation information and past people flow information. The elevator operation simulator predicts the elevator congestion level when planned elevator construction is carried out based on the information stored in the collected data storage unit. The display unit displays the congestion level predicted by the elevator operation simulator (for example, see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-89454 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional congestion prediction and display systems do not take into account events specific to apartment complexes, such as garbage collection days, disaster prevention drills, seasonal events, etc. Therefore, there is a need to provide users with more appropriate information regarding elevator operation.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide an elevator operation prediction system that can provide users with more appropriate information regarding elevator operation. [Means for solving the problem]

[0006] The elevator operation prediction system of the present disclosure includes a prediction management device that makes predictions regarding elevator operations in an apartment building, and a management terminal that acquires event schedule information, which is information about events scheduled in the apartment building. The prediction management device stores operation history information, which is the history of elevator operations, user number history information, which is the history of the number of elevator users by hour, and event history information, which is the history of events that have previously taken place in the apartment building, and predicts the degree of congestion in the elevator based on the operation history information, user number history information, event history information, and the event schedule information acquired by the management terminal, and creates display data for the predicted degree of congestion. The system is equipped with a prediction management device that makes predictions regarding elevator operation, and a display device. The prediction management device calculates the estimated time until the elevator car arrives at the landing based on current operation information, which is information on the current operating status of the elevator, and current user number information, which is information on the current number of elevator users, and creates display data for the calculated estimated time. The display device displays the estimated time based on the display data created by the prediction management device. [Effects of the Invention]

[0007] According to the elevator operation prediction system of the present disclosure, more appropriate information regarding elevator operation can be provided to users. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing an example of the overall configuration of an elevator system according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating the projection display device shown in FIG. [Figure 3] FIG. 2 is a block diagram showing the configuration of the prediction management device shown in FIG. [Figure 4] 4 is a flowchart showing a learning process of the predictive management device shown in FIG. 3. [Figure 5] 4 is a flowchart showing a congestion level prediction process of the prediction management device shown in FIG. 3. [Figure 6]FIG. 10 is a block diagram showing the configuration of a prediction management device according to a second embodiment. [Figure 7] 7 is a flowchart showing an estimated arrival time calculation process of the predictive management device shown in FIG. 6. [Figure 8] FIG. 2 is a configuration diagram showing a first example of a processing circuit that realizes the prediction management device according to the first and second embodiments. [Figure 9] FIG. 10 is a configuration diagram showing a second example of a processing circuit that realizes the prediction management device according to the first and second embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments will be described with reference to the drawings.

[0010] Embodiment 1 1 is a diagram showing an example of the overall configuration of an elevator system according to Embodiment 1. The elevator system 1 includes an elevator operation prediction system 100 and an elevator 200.

[0011] The elevator 200 is installed in an apartment building. The elevator 200 includes an elevator car 210, an elevator control device 220, a hall detection device 230, and an outside-car display device 240.

[0012] The elevator car 210 is equipped with an in-car detection device 211 and an in-car display device 212 .

[0013] The car interior detection device 211 is a surveillance camera that captures images of the inside of the elevator car 210. The car interior detection device 211 outputs the captured images to the elevator control device 220.

[0014] The car interior display device 212 is a display device that is installed on an operation panel inside the elevator car 210. The car interior display device 212 provides information on the degree of congestion to users inside the elevator car 210. Here, the degree of congestion is an index value that indicates how crowded the elevator 200 is.

[0015] The landing detection device 230 is a surveillance camera, and is installed at the elevator landing on each floor of the apartment building. The landing detection device 230 outputs the captured image of the elevator landing to the elevator control device 220. The car exterior display device 240 is a display device, and is installed, for example, at the elevator landing. The car exterior display device 240 provides information on the degree of congestion to users waiting for the arrival of the elevator car 210. Here, the car interior display device 212 and the car exterior display device 240 may be equipped with speakers, and may notify users by voice.

[0016] The elevator control device 220 controls the operation of the elevator 200. The elevator control device 220 also controls the display of the car in-car display device 212 and the car outside display device 240. If the car in-car display device 212 and the car outside display device 240 are equipped with speakers, the elevator control device 220 also controls the audio output of the car in-car display device 212 and the car outside display device 240.

[0017] Furthermore, elevator control device 220 performs image analysis on the images received from car interior detection device 211 to detect the number of users in elevator car 210. Furthermore, elevator control device 220 performs image analysis on the images received from hall detection device 230 to detect the number of users in the elevator hall for each floor. Note that a device dedicated to image analysis may be used separately from elevator control device 220 to detect the number of users in elevator car 210 and the number of users at the elevator hall.

[0018] The elevator operation prediction system 100 includes a prediction management device 110, a management terminal 120, and a projection display device .

[0019] The management terminal 120 is a terminal device used by an administrator who manages the elevator operation prediction system 100 or an administrator who manages an apartment building. The administrator inputs information about events scheduled in the apartment building into the management terminal 120. The administrator also inputs information about events that have been held in the apartment building in the past into the management terminal 120.

[0020] Here, events include garbage collection days, disaster prevention drills, site cleanups, management association meetings, etc. Events also include seasonal events and social gatherings held in apartment complexes, such as rice cake pounding events, Christmas parties, and barbecues.

[0021] The administrator inputs the event name and the date and time of the event into the management terminal 120 as event information.

[0022] The prediction management device 110 makes predictions regarding the operation of the elevator 200. In the first embodiment, the prediction management device 110 predicts the degree of congestion of users using the elevator 200.

[0023] The prediction management device 110 is also connected to an elevator control device 220. The prediction management device 110 acquires from the elevator control device 220 the number of passengers in the elevator car 210 and the number of passengers at the elevator hall.

[0024] The prediction management device 110 also acquires operation information of the elevator 200 from the elevator control device 220. Here, the operation information includes status information on whether the elevator car 210 is moving or stopped. The operation information also includes information on the direction of travel of the elevator car 210. The operation information also includes call information. The call information includes a destination floor specified by a user in the elevator car 210 and a destination direction specified by a user at the elevator hall.

[0025] The prediction management device 110 also displays the predicted congestion degree on the car in-car display device 212 via the elevator control device 220. The prediction management device 110 also displays the predicted congestion degree on the projection display device 130. The prediction management device 110 also displays the predicted congestion degree on the car outside display device 240 via the elevator control device 220.

[0026] The prediction management device 110 also has a function of a web server. When a request to transmit the congestion degree is received from the mobile terminal 300, the prediction management device 110 transmits the predicted congestion degree of the elevator 200 to the mobile terminal 300.

[0027] Here, the mobile terminal 300 is a display device such as a smartphone or tablet carried by a resident of the apartment building. The mobile terminal 300 receives and displays the congestion degree of the elevator 200 from the prediction management device 110 via a web browser.

[0028] The communication between the prediction management device 110 and the mobile terminal 300 may be performed via the Internet network or via a local network.

[0029] The prediction management device 110 can also handle requests from multiple mobile terminals 300 .

[0030] The projection display device 130 is a display device provided in a common space such as an entrance, a lobby, a common passageway, etc. of an apartment building. The projection display device 130 includes a projection unit 131.

[0031] FIG. 2 is a diagram showing the projection display device 130 shown in FIG. 1. The projection unit 131 projects an image onto the floor of the entrance 400 on the first floor, for example. As a result, the projection display device 130 can provide users near the projection surface 132 with the congestion level predicted by the prediction management device 110 in an easily visually understandable manner. The image displayed on the projection surface 132 may be a still image or a video. Furthermore, the image displayed on the projection surface 132 may be monochrome or color.

[0032] Furthermore, the elevator operation prediction system 100 is provided with a plurality of projection display devices 130, so that the congestion degree predicted by the prediction management device 110 can be projected at a plurality of locations. In addition, the elevator operation prediction system 100 is provided with an outside-car display device 240 at the elevator hall on each floor, so that the congestion degree predicted by the prediction management device 110 can be displayed at a plurality of locations.

[0033] FIG. 3 is a block diagram showing the configuration of the prediction management device 110 shown in FIG.

[0034] The prediction management device 110 includes a receiving unit 111, a storage unit 112, a congestion level learning unit 113, a congestion level prediction unit 114, a display data creation unit 115, and a transmission unit .

[0035] The receiving unit 111 receives the number of passengers in the elevator car 210, the number of passengers at the elevator hall on each floor, and operation information from the elevator control device 220. This information is transmitted from the elevator control device 220 sequentially.

[0036] The receiving unit 111 also receives information about upcoming events and information about events that have taken place in the past from the management terminal 120.

[0037] The storage unit 112 associates the operation information received by the receiving unit 111 with date and time information at the time of reception, and stores the information as operation history information 121. Here, the date and time information is information of year, month, day, hour, minute, and second.

[0038] The storage unit 112 associates the number of passengers in the car received by the receiving unit 111 with the date and time information at the time of reception. The storage unit 112 also associates the number of passengers at the landing on each floor received by the receiving unit 111 with the date and time information at the time of reception. The storage unit 112 stores these together as passenger number history information 122.

[0039] The storage unit 112 stores a history of events that have been held in the apartment complex in the past as event history information 123. The storage unit 112 also stores information on events that are scheduled to be held in the apartment complex as event schedule information 125.

[0040] The congestion level learning unit 113 performs machine learning based on the operation history information 121, the number of users history information 122, and the event history information 123. The congestion level learning unit 113 stores the results of the machine learning in the memory unit 112 as learning results 124.

[0041] The congestion level learning unit 113 classifies the data in the operation history information 121 into days on which an event occurred and days on which no event occurred, based on the event history information 123. Similarly, the congestion level learning unit 113 classifies the data in the number of users history information 122 into days on which an event occurred and days on which no event occurred, based on the event history information 123. As a result, one group is formed by combining the operation history information 121 for days on which an event occurred and the number of users history information 122 for days on which an event occurred. In addition, another group is formed by combining the operation history information 121 for days on which no event occurred and the number of users history information 122 for days on which no event occurred.

[0042] The congestion level learning unit 113 creates a learning result based on a group of days on which no events occurred. The learning result created based on days on which no events occurred is called a normal day learning result.

[0043] The congestion level learning unit 113 further subdivides the group of days on which an event occurred into subgroups by event name and creates learning results for each subgroup. This makes it possible to obtain learning results for each event, such as learning results for garbage collection days, disaster prevention drill days, and site cleaning days. The learning results created in this way are referred to as event day learning results.

[0044] In this way, the congestion level learning unit 113 creates the learning result 124 that includes the normal day learning result and the event day learning result. Note that the data structure of the learning result 124 is merely an example and is not limited to this.

[0045] The congestion level prediction unit 114 predicts the congestion level of the elevator 200 for every 10 to 15 minutes on a specified day based on the learning result 124 and the event schedule information 125.

[0046] The congestion degree prediction unit 114 classifies the specified day as either a scheduled event day or a normal day without an event, based on the event schedule information 125. If the specified day is a normal day, the congestion degree prediction unit 114 predicts the congestion degree of the elevator 200 using the normal day learning result.

[0047] When the specified date is a scheduled event date, the congestion degree prediction unit 114 acquires an event implementation date learning result corresponding to the name of the scheduled event from the storage unit 112. Then, the congestion degree prediction unit 114 predicts the congestion degree of the elevator 200 using the acquired event implementation date learning result.

[0048] The congestion degree prediction unit 114 predicts the passenger density in the elevator car 210 and the landing density at each floor as the congestion degree of the elevator 200. The passenger density is calculated by dividing the number of passengers in the elevator car 210 by the maximum capacity. The landing density is calculated by dividing the number of passengers at the elevator landing by a predetermined set number of people.

[0049] In this way, the use of boarding density and hall density as the degree of congestion is merely an example, and is not limited to this.

[0050] The display data creation unit 115 creates display data for the congestion degree predicted by the congestion degree prediction unit 114. The display data creation unit 115 creates, as the display data, HTML (Hypertext Markup Language) data in which the congestion degree value is written. The display data creation unit 115 also creates, as the display data, image data incorporating the congestion degree value.

[0051] The transmitting unit 116 transmits the HTML data created as display data to the mobile terminal 300. The transmitting unit 116 also transmits the image data created as display data to the projection display device 130. The transmitting unit 116 also transmits the image data created as display data to the car inside display device 212 via the elevator control device 220. The transmitting unit 116 also transmits the image data created as display data to the car outside display device 240 via the elevator control device 220.

[0052] Fig. 4 is a flowchart showing the learning process of the prediction management device 110 shown in Fig. 3. The flowchart shown in Fig. 4 is performed periodically, such as every hour or every day.

[0053] In step S101, the congestion level learning unit 113 acquires the operation history information 121, the number of users history information 122, and the event history information 123 stored in the storage unit 112.

[0054] In step S102, the congestion level learning unit 113 performs machine learning based on the operation history information 121, the number of users history information 122, and the event history information 123, and creates a learning result 124.

[0055] In step S103, the storage unit 112 stores the learning result 124.

[0056] Fig. 5 is a flowchart showing the congestion degree prediction process of the prediction management device 110 shown in Fig. 3. Fig. 5 shows the process of displaying the congestion degree of the elevator 200 on a specified date on the mobile terminal 300. This specified date is information on a date specified by the operator of the mobile terminal 300. This specified date is referred to as a prediction target specified date.

[0057] The flowchart of FIG. 5 is executed each time a congestion degree request is received from the mobile terminal 300.

[0058] In step S201, the congestion level prediction unit 114 acquires the prediction target designated date designated by the operator of the mobile terminal 300.

[0059] In step S202, the congestion level prediction unit 114 classifies the specified prediction target date based on the event schedule information 125 as either a normal day or a scheduled event date.

[0060] In step S203, the congestion prediction unit 114 predicts the congestion level of the elevator 200 on the specified prediction date. If the specified prediction date is classified as a normal day, the congestion prediction unit 114 predicts the congestion level using the normal day learning result. On the other hand, if the specified prediction date is classified as a scheduled event date, the congestion prediction unit 114 predicts the congestion level using the event implementation day learning result.

[0061] In step S204, display data creation unit 115 creates HTML data including the congestion degree as display data. Transmission unit 116 transmits the created HTML data to mobile terminal 300 in step S205.

[0062] 5, the congestion degree prediction unit 114 predicts the congestion degree of the elevator 200 by specifying the day unit. In contrast, the congestion degree prediction unit 114 can predict the congestion degree by specifying the week unit, the month unit, or the year unit up to one year.

[0063] For example, when predicting the congestion level on a weekly basis, the congestion level prediction unit 114 classifies each day constituting the week into whether it is a normal day or a day on which an event is scheduled to be held, and then predicts the congestion level. In this case, the congestion level may be predicted every 10 to 15 minutes as described above, or may be aggregated for every hour, every day, etc.

[0064] Furthermore, the congestion level prediction unit 114 can predict the congestion level by specifying a time or a time period. In this case, the congestion level prediction unit 114 predicts the congestion level at the specified time or time period on the day when the request is made.

[0065] Furthermore, when predicting the congestion degree to be displayed by the projection display device 130 and the car exterior display device 240, the congestion degree prediction unit 114 executes step S201, setting the day on which the flowchart of Fig. 5 is executed as the designated prediction target day. Similarly, for the car interior display device 212, the day on which the flowchart of Fig. 5 is executed as the designated prediction target day, and executes step S201.

[0066] In addition, when predicting the congestion degree to be displayed by the projection display device 130, the car interior display device 212, and the car exterior display device 240, the congestion degree prediction unit 114 executes the flowchart of Figure 5 periodically, such as every hour.

[0067] The prediction management device 110 in the first embodiment predicts the congestion degree of the elevator 200 based on operation history information 121, number of users history information 122, event history information 123, and event schedule information 125 acquired by the management terminal 120. The prediction management device 110 also creates display data for the predicted congestion degree.

[0068] Therefore, elevator operation prediction system 100 in the first embodiment can predict the congestion degree taking into account events specific to apartment buildings. Therefore, elevator operation prediction system 100 in the first embodiment can provide users with more appropriate information regarding the operation of elevator 200.

[0069] An apartment building is a private space for its residents. For this reason, residents desire to avoid meeting each other face-to-face. In addition, residents desire to use elevator 200 when it is not crowded. With elevator operation prediction system 100 of the first embodiment, residents of the apartment building can use elevator 200 at a time when it is not crowded.

[0070] The elevator operation prediction system 100 also includes a projection display device 130 having a projection unit 131. The projection unit 131 projects an image created as display data by the prediction management device 110 onto the entrance 400 of the apartment building. This makes it possible to notify the residents of the apartment building of the congestion level of the elevator 200. The elevator operation prediction system 100 also causes the outside-car display device 240 to display the image created as display data. This makes it possible to further notify the residents of the apartment building of the congestion level of the elevator 200.

[0071] Furthermore, the projection unit 131 projects the image created as display data onto the floor surface of the entrance 400. Therefore, the elevator operation prediction system 100 can display the congestion degree of the elevator 200 in a position where there is no wall, such as the center of the entrance 400 where residents often pass through. Furthermore, the elevator operation prediction system 100 displays the congestion degree of the elevator 200 on the frequently used outside-car display device 240, thereby making the congestion degree more widely known.

[0072] Furthermore, the prediction management device 110 performs machine learning based on operation history information 121, user count history information 122, and event history information 123. Furthermore, the prediction management device 110 predicts the congestion level of the elevator 200 based on learning results 124 of the machine learning and event schedule information 125. Therefore, the elevator operation prediction system 100 can obtain more accurate prediction results of the congestion level.

[0073] In addition to the above, the prediction management device 110 may predict the congestion level of the elevator 200 based on a weather forecast. In this case, the prediction management device 110 stores weather history information, which is a history of past weather. The prediction management device 110 then acquires weather forecast information from the management terminal 120 or an external system. The prediction management device 110 then predicts the congestion level of the elevator 200 based on the weather history information and weather forecast information in addition to the operation history information 121, the number of passengers history information 122, the event history information 123, and the event schedule information 125. This allows the elevator operation prediction system 100 to obtain more accurate congestion prediction results.

[0074] Furthermore, although the passenger count history information 122 is assumed to be information including both the number of passengers in the elevator car 210 per hour and the number of passengers at the landings on each floor per hour, this is not limited to this. The passenger count history information 122 is determined depending on the desired congestion level. For example, if the desired congestion level is only the boarding density of the elevator car 210, the passenger count history information 122 will be only the number of passengers in the elevator car 210 per hour. If the desired congestion level is only the landing density, the passenger count history information 122 will be only the number of passengers at the landings on each floor per hour. If the desired congestion level is only the landing density on a specific floor, the passenger count history information 122 will be only the number of passengers at the landings on a specific floor per hour.

[0075] Furthermore, the car interior detection device 211 may be a load cell. In this case, the car interior detection device 211 measures the load weight, thereby determining the number of passengers in the elevator car 210.

[0076] Furthermore, after an event is carried out, the event schedule information 125 may be added directly to the existing event history information 123. This allows the administrator to omit the work of inputting past events into the management terminal 120.

[0077] Also, if there is luggage in the elevator car 210, the luggage may be added to the number of passengers depending on the size, weight, etc. of the luggage.

[0078] In addition, the car interior display device 212, the projection display device 130, and the car exterior display device 240 may periodically switch screens to sequentially display the congestion level for that day, the congestion level for the week, and the congestion level for the month.

[0079] Embodiment 2 6 is a block diagram showing the configuration of a prediction control device 110 according to the second embodiment. The overall configuration of the elevator system 1 is the same as that of the first embodiment.

[0080] The prediction management device 110 in the second embodiment includes an estimated arrival time calculation unit 117 .

[0081] The estimated arrival time calculation unit 117 calculates the estimated time until the elevator car 210 arrives at each floor. The estimated arrival time calculation unit 117 calculates the estimated arrival time at each floor based on current car occupancy information 126, current hall information 127, and current operation information 128.

[0082] Here, current car occupancy information 126 is information on the number of passengers currently in the elevator car 210. Current hall information 127 is information on the number of passengers currently at the hall on each floor. Current operation information 128 is information on the current operation status of the elevator 200. These pieces of information are sequentially transmitted from the elevator control device 220 and stored in the memory unit 112. Furthermore, current number of passengers information 129 is information on the current number of passengers in the elevator 200, and is information that includes current car occupancy information 126 and current hall information 127.

[0083] The calculation method of estimated arrival time calculation unit 117 will be specifically described. Here, we consider a case where an estimated arrival time to the first floor is calculated using an example of a 10-story apartment building with no basement. Also, we assume that elevator car 210 is currently located between the fifth and fourth floors and moving downward.

[0084] If there is no user in the elevator car 210, the estimated arrival time to the first floor varies depending on whether there is a user who has specified a downward direction at the landing on the fourth, third, or second floor. On the other hand, the status of the landings on the first floor and the fifth to tenth floors does not affect the estimated arrival time to the first floor. Therefore, the estimated arrival time calculation unit 117 excludes the status of the landings on the first floor and the fifth to tenth floors from the targets of consideration.

[0085] The estimated arrival time calculation unit 117 determines whether or not a downward call is being made between the fourth floor and the second floor. If a downward call is being made on the third floor, the estimated arrival time calculation unit 117 calculates the dwell time on the third floor based on the number of passengers at the landing on the third floor. The more passengers there are at the landing, the longer it takes for the passengers to get into the elevator car 210. Therefore, the estimated arrival time calculation unit 117 calculates the dwell time so that the more passengers there are at the landing, the longer the time becomes.

[0086] If there are also calls for passengers going up at the third floor hall, the estimated arrival time calculation unit 117 calculates the stay time assuming that half of the passengers are heading down.

[0087] Estimated arrival time calculation unit 117 calculates the estimated arrival time to the first floor based on the rated speed of elevator car 210 and the calculated dwell time at the third floor.

[0088] In addition to the above example, consider a case where multiple passengers are in the elevator car 210 and have destinations of the fourth, second, and first floors. In this case, the estimated arrival time calculation unit 117 calculates the estimated arrival time to the first floor after calculating the dwell times on the fourth and second floors.

[0089] The estimated arrival time calculation unit 117 calculates the dwell times on the fourth floor and the second floor by apportioning the number of passengers in the car. Specifically, the estimated arrival time calculation unit 117 calculates the dwell times on the fourth floor and the second floor, assuming that one-third of the passengers will get off on the fourth floor, one-third of the passengers will get off on the second floor, and one-third of the passengers will get off on the first floor.

[0090] The estimated arrival time calculation unit 117 uses this method to calculate the estimated arrival time at each floor. However, the method for calculating the estimated arrival time is not limited to this.

[0091] Fig. 7 is a flowchart showing the estimated arrival time calculation process of the prediction management device 110 shown in Fig. 6. The flowchart in Fig. 7 shows the process when a request for estimated arrival time is received from the mobile terminal 300. The flowchart in Fig. 7 is performed each time a request for estimated arrival time is received from the mobile terminal 300. Note that the car exterior display device 240 may also be configured to be able to request the estimated arrival time. In this case, the car exterior display device 240 has a touch panel, and the request for estimated arrival time is made via the touch panel of the car exterior display device 240.

[0092] In step S301, the receiving unit 111 receives the current car occupancy information 126, the current hall information 127, and the current operation information 128 from the elevator control device 220. The storage unit 112 stores the current car occupancy information 126, the current hall information 127, and the current operation information 128.

[0093] In step S302, the estimated arrival time calculation unit 117 calculates the estimated arrival time at each floor based on the current car occupancy information 126, the current hall information 127, and the current operation information 128.

[0094] In step S303, the display data creation unit 115 creates HTML data as display data. This HTML data describes the estimated arrival time at each floor. In step S304, the transmission unit 116 transmits the created HTML data to the mobile terminal 300. Note that if the request for the estimated arrival time comes from the car outside display device 240, the transmission unit 116 transmits the display data to the car outside display device 240.

[0095] Furthermore, when the car interior display device 212 displays the estimated arrival time, it may display the estimated arrival time for all floors. Alternatively, the car interior display device 212 may display only the estimated arrival time for the destination floor specified in the elevator car 210.

[0096] Furthermore, when the projection display device 130 and the car exterior display device 240 display the estimated arrival time, the estimated arrival time for all floors may be displayed. Alternatively, when the projection display device 130 is installed at the first floor entrance 400, the projection display device 130 may only display the estimated arrival time for the first floor.

[0097] Furthermore, if the projection display device 130 and the car exterior display device 240 are installed on each floor, the projection display device 130 and the car exterior display device 240 may only display the estimated arrival time at the floor on which the vehicle is installed, and may not display the estimated arrival time at other floors.

[0098] 7 is repeatedly and continuously executed when the car interior display device 212, the projection display device 130, and the car exterior display device 240 display the estimated arrival time, thereby enabling the car interior display device 212, the projection display device 130, and the car exterior display device 240 to display the estimated arrival time in real time.

[0099] The elevator operation prediction system 100 in the second embodiment includes a prediction management device 110 and a display device. The prediction management device 110 calculates an estimated time until the elevator car 210 arrives at the landing based on current operation information 128 and current number of users information 129. The prediction management device 110 also creates display data for the calculated estimated time. The display device displays the estimated time based on the display data created by the prediction management device 110.

[0100] Therefore, the elevator operation prediction system 100 can provide users with more appropriate information regarding the operation of the elevator 200. Furthermore, the elevator operation prediction system 100 in the second embodiment can give users an opportunity to consider using other means of transportation, such as stairs or an escalator. Therefore, congestion in the elevator 200 can be suppressed.

[0101] The elevator operation prediction system 100 also includes a projection display device 130 installed at an entrance 400 inside the building as a display device. The projection display device 130 has a projection unit 131 that projects an image created as display data by the prediction management device 110 onto the entrance 400. The elevator operation prediction system 100 causes the car exterior display device 240 to display the image created as display data by the prediction management device 110.

[0102] Therefore, the estimated arrival time of the elevator 200 can be notified to the user.

[0103] Furthermore, the projection unit 131 projects the image created as display data onto the floor surface of the entrance 400. Therefore, the elevator operation prediction system 100 can display the estimated arrival time of the elevator 200 in a location where there is no wall, such as the center of the entrance 400 where residents often pass through. Furthermore, the elevator operation prediction system 100 displays the estimated arrival time of the elevator 200 on the outside-car display device 240, which is frequently used by users. Therefore, users can be further informed.

[0104] The prediction management device 110 in the second embodiment may include the receiving unit 111, the storage unit 112, the estimated arrival time calculation unit 117, the display data creation unit 115, and the transmission unit 116 shown in Fig. 6. The storage unit 112 shown in Fig. 6 may store information 126 about the number of passengers currently in the car, information 127 about the current platform, and information 128 about the current operation.

[0105] Furthermore, the elevator 200 in the second embodiment may be installed not only in an apartment building but also in an office building, a commercial facility, a public facility, or the like.

[0106] Furthermore, elevator operation prediction system 100 in the second embodiment may be linked to a call registration system that registers calls in advance using mobile terminal 300. In this case, elevator operation prediction system 100 can acquire the number of passengers at the landing on each floor, call information for each floor, the number of passengers in the car, the destination floors in the car, and the like from the call registration system.

[0107] In addition, in the first and second embodiments, the elevator system 1 is provided with both the projection display device 130 and the outside-car display device 240 to transmit information to the outside of the elevator car 210, but it may be provided with only one of them. In the first and second embodiments, at least one of the outside-car display device 240, the inside-car display device 212, and the mobile terminal 300 may be included in the elevator operation prediction system 100 as a display device.

[0108] Moreover, each function of the predictive management device 110 according to the first and second embodiments is realized by a processing circuit. Fig. 8 is a configuration diagram showing a first example of a processing circuit that realizes each function of the predictive management device 110 according to the first and second embodiments. The processing circuit 600 of the first example is dedicated hardware.

[0109] The processing circuit 600 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each function of the predictive management device 110 may be implemented by a separate processing circuit 600. Alternatively, the functions of the predictive management device 110 may be implemented collectively by the processing circuit 600.

[0110] 9 is a diagram showing a second example of a processing circuit that realizes each function of the predictive management device 110 according to the first and second embodiments. The processing circuit 700 of the second example includes a processor 710 and a memory 720.

[0111] In the processing circuit 700, each function of the predictive management device 110 is realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs. The software and firmware are stored in the memory 720. The processor 710 realizes the function of each unit by reading and executing the programs stored in the memory 720.

[0112] It can be said that the programs stored in memory 720 cause the computer to execute the procedures or methods of the above-mentioned sections. Here, memory 720 corresponds to non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), and EEPROM (Electrically Erasable and Programmable Read Only Memory). Also included in memory 720 are magnetic disks, flexible disks, optical disks, compact disks, minidisks, DVDs, and the like.

[0113] The functions of the above-described units may be partially realized by dedicated hardware and partially realized by software or firmware.

[0114] In this way, the processing circuit can realize the functions of each of the above-mentioned units by hardware, software, firmware, or a combination of these.

[0115] The processing circuits 600 and 700 are applicable to the management terminal 120 and the mobile terminal 300 in addition to the prediction management device 110 . [Explanation of symbols]

[0116] 1 elevator system, 100 elevator operation prediction system, 110 prediction management device, 120 management terminal, 130 projection display device (display device), 131 projection unit, 200 elevator, 210 elevator car, 212 in-car display device (display device), 240 outside-car display device (display device), 300 mobile terminal (display device), 400 entrance (common space).

Claims

1. A prediction management device that predicts elevator operation in an apartment building; a management terminal that acquires event schedule information that is information about events scheduled in the apartment complex and includes at least the name of the event; Equipped with The prediction management device storing operation history information, which is a history of the operation of the elevator; user number history information, which is a history of the number of users of the elevator by time; and event history information, which is a history of events that have been held in the apartment building in the past; performing machine learning based on the operation history information, the number of users history information, and the event history information, and predicting the congestion level of the elevator based on a learning result of the machine learning and the event schedule information acquired by the management terminal; Create data to display the predicted congestion level, When performing the machine learning, Based on the event history information, the data in the operation history information and the data in the number of users history information are grouped into days on which an event occurred and days on which no event occurred; For days when an event occurred, the data is further divided into subgroups subdivided by event name, and the machine learning is performed for each of the subdivided subgroups. As a learning result of the machine learning, an event implementation date learning result is created for each subgroup. The machine learning is performed on a group of days on which no events occurred, and a normal day learning result is created as the learning result of the machine learning. When predicting the congestion degree of the elevator, Based on the event schedule information, classifying the prediction target date as either a scheduled event date or a normal day on which no event is scheduled to be held; If the prediction target date is the scheduled event date, a congestion degree is predicted using the event implementation date learning result corresponding to the name of the event included in the event schedule information; When the prediction target day is the normal day, the elevator operation prediction system predicts the congestion level using the normal day learning result.

2. a projection display device having a projection unit, The elevator operation prediction system according to claim 1 , wherein the projection unit projects the image created as the display data by the prediction management device onto a common space of the apartment building.

3. The elevator operation prediction system according to claim 2 , wherein the projection unit projects the image created as the display data onto a floor surface of the common space.

4. The prediction management device It stores weather history information, which is a history of past weather conditions, Obtaining weather forecast information from the management terminal or an external system; 4. The elevator operation prediction system according to claim 1, wherein the congestion level of the elevator is predicted based on the operation history information, the number of users history information, the event history information, the event schedule information, the weather history information, and the weather forecast information.

5. A display device is provided, the prediction management device calculates an estimated time until the elevator car arrives at the landing based on current operation information, which is information on the current operation status of the elevator, and current user number information, which is information on the current number of users of the elevator, and creates estimated time display data as data for displaying the calculated estimated time; 2. The elevator operation prediction system according to claim 1, wherein the display device displays the estimated time based on the estimated time display data created by the prediction management device.

6. the display device is a projection display device installed in a common space within a building, the projection display device has a projection unit that projects an image created by the prediction management device as the estimated time display data onto the common space, The elevator operation prediction system according to claim 5 , wherein the projection display device displays the estimated time by projecting the image using the projection unit.

7. The elevator operation prediction system according to claim 6 , wherein the projection unit projects an image created as the expected time display data onto a floor surface of the common space.

8. a prediction management device that makes predictions regarding elevator operations in an apartment building; a management terminal that acquires event schedule information, which is information about events scheduled in the apartment complex and includes at least the name of the event; and a mobile terminal owned by a resident of the apartment complex; Equipped with The prediction management device storing operation history information, which is a history of the operation of the elevator; user number history information, which is a history of the number of users of the elevator by time; and event history information, which is a history of events that have been held in the apartment building in the past; performing machine learning based on the operation history information, the number of users history information, and the event history information, and predicting the congestion level of the elevator based on a learning result of the machine learning and the event schedule information acquired by the management terminal; A configuration is provided in which display data of the predicted congestion level is generated, The mobile terminal designates a time to be predicted on a day to be predicted, the prediction management device receives the prediction target date and the specified time from the mobile terminal, predicts the congestion level at the specified time, creates reply data that is the display data regarding the predicted congestion level, and returns the reply data to the mobile terminal; The mobile terminal displays the reply data, The prediction management device When performing the machine learning, Based on the event history information, the data in the operation history information and the data in the number of users history information are grouped into days on which an event occurred and days on which no event occurred; For days when an event occurred, the data is further divided into subgroups subdivided by event name, and the machine learning is performed for each of the subdivided subgroups. As a learning result of the machine learning, an event implementation date learning result is created for each subgroup. The machine learning is performed on a group of days on which no events occurred, and a normal day learning result is created as the learning result of the machine learning. When predicting the congestion degree of the elevator, classifying the prediction target date as a scheduled event date or a normal day on which no event is scheduled to be held based on the event schedule information; If the prediction target date is the scheduled event date, a congestion degree is predicted using the event implementation date learning result corresponding to the name of the event included in the event schedule information; If the prediction target day is the normal day, the normal day learning result is used to predict the congestion degree. Elevator operation prediction system.

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