WO2021001907A1 - Learning device and elevator device - Google Patents

Learning device and elevator device Download PDF

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Publication number
WO2021001907A1
WO2021001907A1 PCT/JP2019/026174 JP2019026174W WO2021001907A1 WO 2021001907 A1 WO2021001907 A1 WO 2021001907A1 JP 2019026174 W JP2019026174 W JP 2019026174W WO 2021001907 A1 WO2021001907 A1 WO 2021001907A1
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WO
WIPO (PCT)
Prior art keywords
car
learning
series data
time
lead
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Application number
PCT/JP2019/026174
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French (fr)
Japanese (ja)
Inventor
巧 尾田
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三菱電機ビルテクノサービス株式会社
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Publication date
Application filed by 三菱電機ビルテクノサービス株式会社 filed Critical 三菱電機ビルテクノサービス株式会社
Priority to JP2021529582A priority Critical patent/JP6958770B2/en
Priority to CN201980097917.9A priority patent/CN114026038B/en
Priority to PCT/JP2019/026174 priority patent/WO2021001907A1/en
Publication of WO2021001907A1 publication Critical patent/WO2021001907A1/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B13/00—Doors, gates, or other apparatus controlling access to, or exit from, cages or lift well landings
    • B66B13/02—Door or gate operation
    • B66B13/14—Control systems or devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B13/00—Doors, gates, or other apparatus controlling access to, or exit from, cages or lift well landings
    • B66B13/24—Safety devices in passenger lifts, not otherwise provided for, for preventing trapping of passengers
    • B66B13/26—Safety devices in passenger lifts, not otherwise provided for, for preventing trapping of passengers between closing doors

Definitions

  • the present invention relates to a learning device used in an elevator system and an elevator device.
  • Patent Document 1 describes an elevator system.
  • a camera is provided in the car. From the image acquired by the camera, it is detected that a string-like object is caught in the door.
  • Patent Document 1 The lead connected to the pet is thin, and it is difficult to detect the lead itself sandwiched between the doors.
  • the system described in Patent Document 1 requires an extremely high-performance camera in order to detect a thin lead. For this reason, conventionally, detection conditions have been desired so that the lead can be detected to be sandwiched between the doors without detecting the lead itself sandwiched between the doors.
  • An object of the present invention is to provide a learning device capable of generating detection conditions for detecting that a lead is pinched in a door by learning an operation pattern of a user or a pet.
  • Another object of the present invention is to provide an elevator device that can utilize the detection conditions so generated.
  • the learning device is a storage means for storing time-series data acquired by each of a plurality of sensors during the first period including immediately after the door of the elevator device is closed and immediately after the car starts traveling, and a storage means. Based on the first time-series data, the second time-series data, and the third time-series data of the time-series data stored in, the pet at the time of the lead pinching or the user with the pet It includes a learning means for learning at least one motion pattern, and a generation means for generating detection conditions for the detection means to detect lead pinching based on the motion pattern learned by the learning means.
  • the first time-series data includes time-series data acquired by each of the plurality of sensors when the lead pinching actually occurs and the detecting means detects the lead pinching.
  • the second time-series data includes time-series data acquired by each of the plurality of sensors when the detection means detects the lead pinch, although the lead pinch did not actually occur.
  • the third time-series data includes time-series data acquired by each of the plurality of sensors when the lead pinching actually occurred but the detecting means did not detect the lead pinching.
  • the elevator device includes a control device for controlling operation for carrying a user in a car, a plurality of specific sensors, a detection means for detecting lead pinching based on detection conditions, a control device, and a plurality of control devices.
  • a detection condition for detecting that a lead is pinched in a door can be generated by learning an operation pattern of a user or a pet.
  • the detection conditions thus generated can be used in the elevator device.
  • FIG. It is a figure which shows the example of an elevator system. It is a figure which shows the example of an elevator device. It is a figure which shows the example of the function which a monitoring device has. It is a figure which shows the example of the learning apparatus in Embodiment 1.
  • FIG. It is a flowchart which shows the operation example of the learning apparatus in Embodiment 1. It is a flowchart which shows the operation example of an elevator device. It is a figure which shows the example of the hardware resource of the learning apparatus. It is a figure which shows another example of the hardware resource of a learning device.
  • FIG. 1 is a diagram showing an example of an elevator system.
  • the elevator system shown in FIG. 1 includes a plurality of elevator devices 1 and a monitoring center 2.
  • Each of the elevator devices 1 is capable of bidirectional communication with the monitoring center 2.
  • the monitoring center 2 communicates with the elevator devices 1 provided in many buildings.
  • the elevator device 1 includes, for example, a basket 3 and a balance weight 4.
  • the car 3 moves up and down on the hoistway 5.
  • the balance weight 4 moves up and down on the hoistway 5.
  • the car 3 and the counterweight 4 are suspended from the hoistway 5 by the main rope 6.
  • the main rope 6 is wound around the drive sheave 8 of the hoisting machine 7.
  • the car 3 is driven by the hoist 7.
  • the control device 9 controls the operation for carrying the user in the car 3.
  • the control device 9 controls the hoisting machine 7.
  • the control device 9 controls the equipment provided in the car 3.
  • FIG. 1 shows an example in which the hoisting machine 7 and the control device 9 are installed in the machine room 10 above the hoistway 5.
  • the hoisting machine 7 and the control device 9 may be installed in the hoistway 5.
  • the hoisting machine 7 may be installed at the top of the hoistway 5 or in the pit of the hoistway 5.
  • the monitoring device 11 is connected to the control device 9.
  • the monitoring device 11 communicates with the monitoring center 2.
  • the method in which the monitoring device 11 communicates with the monitoring center 2 may be any method.
  • the monitoring device 11 communicates with the monitoring center 2 via, for example, a specific network.
  • the above network may be an IP network.
  • the IP network is a communication network that uses IP (Internet Protocol) as a communication protocol.
  • FIG. 2 is a diagram showing an example of the elevator device 1.
  • FIG. 2 is a diagram showing an example of the inside of the car 3 and the landing 12 where the car 3 is stopped.
  • the landing 12 is provided on each floor of the building equipped with the elevator device 1.
  • the landing 12 may be provided only on a part of the floor of the building.
  • the elevator device 1 is equipped with a plurality of sensors.
  • the learning device 40 provided in the monitoring center 2 learns the movement pattern of at least one of the elevator user carrying the pet or the pet.
  • the means capable of acquiring the data necessary for the above learning is referred to as a sensor.
  • the camera 13, the microphone 14, the button 15, the weighing device 16, and the photoelectric device 17 provided in the car 3 function as sensors.
  • the camera 20, the microphone 21, the button 22, and the photoelectric device 23 provided in the landing 12 function as sensors.
  • the camera 13, the microphone 14, the button 15, the weighing device 16, and the photoelectric device 17 are examples of sensors for acquiring data necessary for learning the movement pattern of the user or pet in the car 3.
  • the elevator device 1 may include any combination of the camera 13, the microphone 14, the button 15, the weighing device 16, and the photoelectric device 17 as a sensor.
  • the camera 13 is provided in the car 3.
  • the camera 13 photographs the inside of the car 3.
  • the time-series data indicating the image captured by the camera 13 is input to the monitoring device 11.
  • the microphone 14 is provided in the basket 3.
  • the microphone 14 may be a part of the intercom provided in the operation panel 18.
  • the time-series data indicating the sound acquired by the microphone 14 is input to the monitoring device 11.
  • the button 15 is provided on the operation panel 18 provided in the car 3.
  • the button 15 is, for example, a destination button.
  • the button 15 may be an open button or a close button.
  • the button 15 may be an intercom button for external reporting.
  • the time series data indicating whether or not the button 15 is pressed is input to the monitoring device 11 via the control device 9.
  • the weighing device 16 detects the load on the car 3.
  • the weighing device 16 is provided in the car 3.
  • a sensor having the same function as the weighing device 16 may be provided at the end of the main rope 6.
  • the time series data indicating the load detected by the weighing device 16 is input to the monitoring device 11 via the control device 9.
  • the photoelectric device 17 is provided on the door 19 of the car 3.
  • the photoelectric device 17 is a device for detecting foreign matter sandwiched between the doors 19.
  • Time-series data indicating whether or not the photoelectric device 17 has detected a foreign substance is input to the monitoring device 11 via the control device 9.
  • the camera 20, the microphone 21, the button 22, and the photoelectric device 23 are examples of sensors for acquiring data necessary for learning the movement pattern of the user or pet at the landing 12.
  • the elevator device 1 may include any combination of the camera 20, the microphone 21, the button 22, and the photoelectric device 23 as a sensor.
  • the camera 20 is provided at the landing 12.
  • the camera 20 photographs the landing 12.
  • the time series data indicating the image captured by the camera 20 is input to the monitoring device 11.
  • the microphone 21 is provided at the landing 12.
  • the microphone 21 may be a part of the intercom provided in the operation panel 24.
  • the time-series data indicating the sound acquired by the microphone 21 is input to the monitoring device 11.
  • the button 22 is provided on the operation panel 24 provided in the landing 12.
  • the button 22 is, for example, an up button or a down button.
  • the time series data indicating whether or not the button 22 is pressed is input to the monitoring device 11 via the control device 9.
  • the photoelectric device 23 is provided on the door 25 of the landing 12.
  • the photoelectric device 23 is a device for detecting foreign matter sandwiched between the doors 25. Time-series data indicating whether or not the photoelectric device 23 has detected a foreign substance is input to the monitoring device 11 via the control device 9.
  • FIG. 3 is a diagram showing an example of the function of the monitoring device 11.
  • the monitoring device 11 includes, for example, a storage unit 30, a communication unit 31, and a detection unit 32.
  • Various data related to the elevator are stored in the storage unit 30.
  • the storage unit 30 stores time-series data acquired by each of the plurality of sensors.
  • data representing the state of the elevator is stored in the storage unit 30.
  • data representing the state of the elevator is also referred to as state data.
  • the state data includes time-series data indicating whether or not the car 3 is running.
  • the state data includes time series data indicating whether the door 19 is completely closed.
  • the state data is acquired from, for example, the control device 9. A part of the time series data acquired by the sensor may be stored in the storage unit 30 as state data.
  • the communication unit 31 transmits the data stored in the storage unit 30 to the monitoring center 2. For example, the communication unit 31 periodically transmits data to the monitoring center 2. The communication unit 31 may transmit data to the monitoring center 2 when a specific event occurs. The communication unit 31 may transmit data to the monitoring center 2 in response to a request from the monitoring center 2.
  • the detection unit 32 detects that the lead connected to the pet is sandwiched between the door 19 or the door 25.
  • the fact that the lead connected to the pet is sandwiched between the door 19 or the door 25 is also referred to as “lead pinching”.
  • the communication unit 31 may transmit the data stored in the storage unit 30 to the monitoring center 2.
  • the monitoring center 2 is equipped with a learning device 40 in addition to the function of monitoring the elevator device 1.
  • the learning device 40 is capable of bidirectional communication with the elevator device 1.
  • the learning device 40 is a device for generating detection conditions for detecting lead pinching by learning a pet or an operation pattern of a user carrying the pet.
  • FIG. 4 is a diagram showing an example of the learning device 40 according to the first embodiment.
  • the learning device 40 includes, for example, a storage unit 41, a learning unit 42, a condition generation unit 43, and a transmission unit 44.
  • Data is transmitted to the learning device 40 from a large number of elevator devices 1 provided in various buildings.
  • the data transmitted from the monitoring device 11 of each elevator device 1 is stored in the storage unit 41.
  • the storage unit 41 stores time-series data acquired by each of the plurality of sensors included in the elevator device 1.
  • Elevator status data is stored in the storage unit 41.
  • the storage unit 41 stores time-series data indicating whether or not the car 3 is running.
  • the storage unit 41 stores time-series data indicating whether or not the door 19 is completely closed.
  • event data indicating that a specific event has occurred is stored in the storage unit 41.
  • event data indicating that the lead pinch has occurred is stored in the storage unit 41.
  • event data indicating that the failure has occurred is stored in the storage unit 41.
  • event data indicating that the report has been made is stored in the storage unit 41.
  • FIG. 5 is a flowchart showing an operation example of the learning device 40 according to the first embodiment.
  • the learning unit 42 learns the movement pattern of at least one of the pet and the user with the pet when the lead pinching occurs (S101).
  • a user with a pet is also referred to as an owner.
  • the learning unit 42 may learn both the movement pattern of the pet and the movement pattern of the owner.
  • the learning unit 42 may learn only one of the pet's motion pattern and the owner's motion pattern.
  • the learning unit 42 may learn both the movement pattern of one of the pets or the owner in the car 3 and the movement pattern of the other of the pet or the owner in the landing 12.
  • the learning unit 42 may learn only the movement patterns of the pet or the owner in the car 3.
  • the learning unit 42 may learn only the movement patterns of the pet or the owner at the landing 12.
  • the storage unit 41 stores time-series data acquired by each of the plurality of sensors at least in a specific first period.
  • the first period is a period including immediately after the door 19 is closed and the car 3 starts traveling in the elevator device 1.
  • the learning unit 42 learns S101 based on the first time series data, the second time series data, and the third time series data among the time series data stored in the storage unit 41.
  • the first time series data is time series data acquired by each of a plurality of sensors during the first period.
  • the first time-series data is data acquired when the lead pinching actually occurs and the detection unit 32 detects the lead pinching. That is, the first time series data is the data acquired when the detection of the lead pinch is appropriately performed.
  • the second time series data is time series data acquired by each of a plurality of sensors in the first period.
  • the second time-series data is data acquired when the detection unit 32 detects the lead pinching, although the lead pinching did not actually occur. That is, the second time-series data is the data acquired when the lead pinching is erroneously detected.
  • the third time series data is time series data acquired by each of a plurality of sensors in the first period.
  • the third time-series data is data acquired when the lead pinching actually occurred but the detection unit 32 did not detect the lead pinching. That is, the third time-series data is the data acquired when the detection omission is made for the lead pinch.
  • the learning unit 42 learns the operation pattern by using at least the first time series data, the second time series data, and the third time series data as input data. Since lead pinching is not a frequent event, the selection of the input data does not have to be automated.
  • the method in which the learning unit 42 learns the motion pattern may be any method.
  • Table 1 shows an example of the operation pattern when the lead pinching occurs, which was learned by the learning unit 42.
  • the motion pattern obtained by learning by the learning unit 42 includes, for example, a plurality of motions.
  • Table 1 shows, as the simplest example, an example in which an operation pattern includes two operations. That is, in the example shown in Table 1, the operation pattern includes the first operation and the second operation performed after the first operation.
  • the first operation is an operation performed immediately after the door 19 is closed and before the car 3 starts traveling.
  • the second operation is an operation immediately after the running of the car 3 is started.
  • the motion pattern obtained by learning by the learning unit 42 may include three or more motions.
  • the operation pattern may include a third operation performed after the second operation.
  • the second movement of the pet in the car 3 is "pulled toward the door"
  • "stopping on the floor or floating in the air” is included in the movement pattern as the third movement. Is also good.
  • the condition generation unit 43 generates a detection condition for the detection unit 32 to detect the lead pinch based on the operation pattern learned by the learning unit 42 (S102).
  • the detection condition includes a condition corresponding to each operation included in the operation pattern. As shown in Table 1, if the operation pattern includes the first operation and the second operation, the detection conditions include the first condition corresponding to the first operation and the second condition corresponding to the second operation. included. If the operation pattern further includes the third operation, the detection condition further includes the third condition corresponding to the third operation.
  • the first condition is a condition for determining whether or not the first operation is being performed based on the data acquired by at least one of the plurality of sensors. For example, consider a case where the motion pattern learned by the learning unit 42 includes "barking" of the pet in the car 3 as the first motion. In such a case, the condition generation unit 43 generates a detection condition so that the first condition is satisfied in the following cases. -The loudness of the sound detected by the microphone 14 exceeds the threshold value immediately after the door 19 is closed and before the car 3 starts running.
  • the condition generation unit 43 will satisfy the first condition in the following cases. Generate detection conditions in. The load fluctuation detected by the weighing device 16 exceeds the threshold value immediately after the door 19 is closed and before the car 3 starts traveling.
  • the second condition is a condition for determining whether or not the second operation is being performed based on the data acquired by at least one of the plurality of sensors. For example, consider a case where the motion pattern learned by the learning unit 42 includes "attracting the pet in the car 3 to the door side" as the second motion. In such a case, the condition generation unit 43 generates a detection condition so that the second condition is satisfied in the following cases. Immediately after the car 3 starts running, a change exceeding the threshold value occurs in the image showing a specific area in the image captured by the camera 13.
  • the condition generation unit 43 satisfies the second condition in the following cases. Generate detection conditions so that -Immediately after the car 3 starts running, the button 15 is repeatedly hit.
  • the first condition may be a condition for determining whether or not the first operation is being performed based on the data acquired by a plurality of sensors.
  • the second condition may be a condition for determining whether or not the second operation is being performed based on the data acquired by the plurality of sensors.
  • the transmission unit 44 transmits the detection condition generated by the condition generation unit 43 to the elevator device 1 (S103).
  • the communication unit 31 of the monitoring device 11 receives the detection condition from the learning device 40.
  • the detection unit 32 detects the lead pinch based on the detection condition generated by the condition generation unit 43.
  • FIG. 6 is a flowchart showing an operation example of the elevator device 1.
  • the detection unit 32 detects the lead pinch (S201)
  • the control device 9 keeps the door 19 and the door 25 in the open state for a certain period of time (S204).
  • the control device 9 makes an emergency stop of the car 3 (S205).
  • the control device 9 determines whether or not the car 3 is stopped at a position where the door 19 of the car 3 can be opened (S206). For example, if the door 19 of the car 3 is opened and the car 3 is stopped at a position where the door 25 of the landing 12 is interlocked with the door 19, the result is determined to be Yes in S206.
  • the control device 9 opens the door 19 (S203). Further, when the door 19 is opened in S203, the control device 9 keeps the door 19 and the door 25 in the open state for a certain period of time (S204).
  • the guidance to the effect that the door 19 is opened may be given in the car 3 before the door 19 is opened.
  • the detection unit 32 may detect the lead pinch based on the detection condition in which the learning by the learning unit 42 is not reflected. After that, when the learning by the learning unit 42 is performed for a certain period of time, the detection unit 32 detects the lead pinch based on the detection condition in which the learning by the learning unit 42 is reflected. Further, the detection conditions stored in the monitoring device 11 are periodically updated by receiving the latest detection conditions from the transmission unit 44.
  • the method for creating the detection condition by the condition generation unit 43 may be any method.
  • the condition generation unit 43 generates a new detection condition based on the operation pattern learned by the learning unit 42.
  • the condition generation unit 43 may modify the previously generated detection condition based on the operation pattern learned by the learning unit 42.
  • condition generation unit 43 may newly add the fourth condition to the existing detection conditions having the first condition to the third condition.
  • the condition generation unit 43 may delete the third condition included in the existing detection condition.
  • the condition generation unit 43 may change the sensor used when determining the first condition.
  • the condition generation unit 43 may change the threshold value used when determining the first condition.
  • the condition generation unit 43 may delete some of the detection conditions.
  • condition generation unit 43 executes the following processing in S102. -Determine the type of sensor used for condition judgment-Determine the order of condition judgment (timing to perform condition judgment) -Set the data range where the condition is satisfied
  • the detection condition for detecting the lead pinch can be generated by learning the movement pattern of the pet or the owner.
  • the function of the learning unit 42 and the function of the condition generation unit 43 may be realized by using artificial intelligence (AI).
  • the learning data for generating the detection conditions acquired from the control device 9 and the plurality of sensors is stored in the storage unit 30.
  • the communication unit 31 transmits the learning data stored in the storage unit 30 to the learning device 40, and receives the detection conditions from the learning device 40.
  • the detection conditions generated by the learning device 40 can be used in the elevator device 1. Further, the latest detection conditions generated by the learning device 40 can be reflected in the elevator device 1.
  • the plurality of movements included in the movement pattern may be performed by both the pet and the owner.
  • the motion pattern includes the first motion and the second motion, even if the first motion is an motion performed by the pet in the car 3 and the second motion is performed by the owner at the landing 12. good.
  • the first action may be an action performed by the pet at the landing 12
  • the second action may be an action performed by the owner in the car 3.
  • the first operation may be an operation performed by the owner
  • the second operation may be an operation performed by the pet.
  • reference numerals 41 to 44 indicate the functions of the learning device 40.
  • FIG. 7 is a diagram showing an example of hardware resources of the learning device 40.
  • the learning device 40 includes a processing circuit 50 including, for example, a processor 51 and a memory 52 as hardware resources.
  • the function of the storage unit 41 is realized by the memory 52.
  • the learning device 40 realizes the functions of the respective parts indicated by reference numerals 42 to 44 by executing the program stored in the memory 52 by the processor 51.
  • FIG. 8 is a diagram showing another example of the hardware resource of the learning device 40.
  • the learning device 40 includes, for example, a processing circuit 50 including a processor 51, a memory 52, and dedicated hardware 53.
  • FIG. 8 shows an example in which a part of the functions of the learning device 40 is realized by the dedicated hardware 53. All the functions of the learning device 40 may be realized by the dedicated hardware 53.
  • the dedicated hardware 53 a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof can be adopted.
  • reference numerals 30 to 32 indicate the functions of the monitoring device 11.
  • the hardware resources of the monitoring device 11 are the same as those shown in FIG. 7.
  • the monitoring device 11 includes a processing circuit including, for example, a processor and a memory as hardware resources.
  • the monitoring device 11 realizes the functions of the respective parts shown by reference numerals 31 to 32 by executing the program stored in the memory by the processor.
  • the hardware resources of the monitoring device 11 may be the same as the example shown in FIG.
  • the monitoring device 11 includes a processing circuit including a processor, a memory, and dedicated hardware. Some of the functions of the monitoring device 11 may be realized by dedicated hardware. All the functions of the monitoring device 11 may be realized by dedicated hardware.
  • the hardware resources of the control device 9 are the same as those shown in FIG. 7.
  • the control device 9 includes a processing circuit including, for example, a processor and a memory as hardware resources.
  • the control device 9 realizes the functions of the respective parts shown by reference numerals 31 to 32 by executing the program stored in the memory by the processor.
  • control device 9 includes a processing circuit including a processor, a memory, and dedicated hardware. Some of the functions of the control device 9 may be realized by dedicated hardware. All the functions of the control device 9 may be realized by dedicated hardware.
  • the learning device according to the present invention can be used at a management center or the like capable of communicating with an elevator device.

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Indicating And Signalling Devices For Elevators (AREA)
  • Elevator Control (AREA)
  • Elevator Door Apparatuses (AREA)

Abstract

This learning device (40) is provided with, for example, a storage unit (41), a learning unit (42), and a condition generation unit (43). The storage unit (41) stores time series data acquired by each of a plurality of sensors in a specific first period. The learning unit (42) learns an operation pattern of at least one among a pet and an owner when lead sandwiching occurs, on the basis of first time series data, second time series data, and third time series data among the time series data stored in the storage unit (41). The condition generation unit (43) generates, on the basis of the operation pattern learnt by the learning unit (42), a detection condition for the detection unit (32) to detect the lead sandwiching.

Description

学習装置及びエレベーター装置Learning device and elevator device
　この発明は、エレベーターシステムで利用される学習装置と、エレベーター装置とに関する。 The present invention relates to a learning device used in an elevator system and an elevator device.
　特許文献１に、エレベーターシステムが記載されている。特許文献１に記載されたシステムでは、かごにカメラが設けられる。カメラによって取得された画像から、ドアに紐状の物体が挟まれていることを検出する。 Patent Document 1 describes an elevator system. In the system described in Patent Document 1, a camera is provided in the car. From the image acquired by the camera, it is detected that a string-like object is caught in the door.
日本特開２０１１－９３７０２号公報Japanese Patent Application Laid-Open No. 2011-93702
　ペットに繋がれたリードは細く、ドアに挟まれているリード自体を検出することは難しい。例えば、特許文献１に記載されたシステムでは、細いリードを検出するために、極めて高性能なカメラが必要になる。このため、従来から、ドアに挟まれているリード自体を検出しなくても、リードがドアに挟まれていることを検出できるような検出条件が望まれていた。 The lead connected to the pet is thin, and it is difficult to detect the lead itself sandwiched between the doors. For example, the system described in Patent Document 1 requires an extremely high-performance camera in order to detect a thin lead. For this reason, conventionally, detection conditions have been desired so that the lead can be detected to be sandwiched between the doors without detecting the lead itself sandwiched between the doors.
　この発明は、上述のような課題を解決するためになされた。この発明の目的は、ドアにリードが挟まれていることを検出するための検出条件を、利用者又はペットの動作パターンを学習することによって生成できる学習装置を提供することである。この発明の他の目的は、そのように生成された検出条件を利用できるエレベーター装置を提供することである。 This invention was made to solve the above-mentioned problems. An object of the present invention is to provide a learning device capable of generating detection conditions for detecting that a lead is pinched in a door by learning an operation pattern of a user or a pet. Another object of the present invention is to provide an elevator device that can utilize the detection conditions so generated.
　この発明に係る学習装置は、エレベーター装置のドアが閉じてからかごが走行を開始した直後を含む第１期間に複数のセンサのそれぞれによって取得された時系列データを記憶する記憶手段と、記憶手段に記憶された時系列データのうちの第１時系列データ、第２時系列データ、及び第３時系列データに基づいて、リード挟みが発生した時のペット又はそのペットを連れている利用者の少なくとも一方の動作パターンを学習する学習手段と、学習手段によって学習された動作パターンに基づいて、検出手段がリード挟みを検出するための検出条件を生成する生成手段と、を備える。第１時系列データは、リード挟みが実際に発生し、且つ検出手段がリード挟みを検出した時に複数のセンサのそれぞれが取得した時系列データを含む。第２時系列データは、リード挟みが実際には発生していなかったが、検出手段がリード挟みを検出した時に複数のセンサのそれぞれが取得した時系列データを含む。第３時系列データは、リード挟みが実際に発生したが、検出手段がリード挟みを検出しなかった時に複数のセンサのそれぞれが取得した時系列データを含む。 The learning device according to the present invention is a storage means for storing time-series data acquired by each of a plurality of sensors during the first period including immediately after the door of the elevator device is closed and immediately after the car starts traveling, and a storage means. Based on the first time-series data, the second time-series data, and the third time-series data of the time-series data stored in, the pet at the time of the lead pinching or the user with the pet It includes a learning means for learning at least one motion pattern, and a generation means for generating detection conditions for the detection means to detect lead pinching based on the motion pattern learned by the learning means. The first time-series data includes time-series data acquired by each of the plurality of sensors when the lead pinching actually occurs and the detecting means detects the lead pinching. The second time-series data includes time-series data acquired by each of the plurality of sensors when the detection means detects the lead pinch, although the lead pinch did not actually occur. The third time-series data includes time-series data acquired by each of the plurality of sensors when the lead pinching actually occurred but the detecting means did not detect the lead pinching.
　この発明に係るエレベーター装置は、利用者をかごで運ぶための運転を制御する制御装置と、特定の複数のセンサと、検出条件に基づいて、リード挟みを検出する検出手段と、制御装置及び複数のセンサから取得した、検出条件を生成するための学習用データを記憶する記憶手段と、検出条件を生成する学習装置に学習用データを送信し、学習装置から検出条件を受信する通信手段と、を備える。 The elevator device according to the present invention includes a control device for controlling operation for carrying a user in a car, a plurality of specific sensors, a detection means for detecting lead pinching based on detection conditions, a control device, and a plurality of control devices. A storage means for storing learning data for generating detection conditions acquired from the sensor of the above, a communication means for transmitting learning data to a learning device for generating detection conditions, and a communication means for receiving detection conditions from the learning device. To be equipped.
　この発明によれば、ドアにリードが挟まれていることを検出するための検出条件を、利用者又はペットの動作パターンを学習することによって生成できる。また、そのように生成された検出条件をエレベーター装置において利用できる。 According to the present invention, a detection condition for detecting that a lead is pinched in a door can be generated by learning an operation pattern of a user or a pet. In addition, the detection conditions thus generated can be used in the elevator device.
エレベーターシステムの例を示す図である。It is a figure which shows the example of an elevator system. エレベーター装置の例を示す図である。It is a figure which shows the example of an elevator device. 監視装置が有する機能の例を示す図である。It is a figure which shows the example of the function which a monitoring device has. 実施の形態１における学習装置の例を示す図である。It is a figure which shows the example of the learning apparatus in Embodiment 1. FIG. 実施の形態１における学習装置の動作例を示すフローチャートである。It is a flowchart which shows the operation example of the learning apparatus in Embodiment 1. エレベーター装置の動作例を示すフローチャートである。It is a flowchart which shows the operation example of an elevator device. 学習装置のハードウェア資源の例を示す図である。It is a figure which shows the example of the hardware resource of the learning apparatus. 学習装置のハードウェア資源の他の例を示す図である。It is a figure which shows another example of the hardware resource of a learning device.
　添付の図面を参照し、本発明を説明する。重複する説明は、適宜簡略化或いは省略する。各図において、同一の符号は同一の部分又は相当する部分を示す。 The present invention will be described with reference to the accompanying drawings. Overlapping description will be simplified or omitted as appropriate. In each figure, the same reference numerals indicate the same parts or corresponding parts.
実施の形態１．
　図１は、エレベーターシステムの例を示す図である。図１に示すエレベーターシステムは、複数のエレベーター装置１と監視センター２とを備える。エレベーター装置１のそれぞれは、監視センター２と双方向の通信が可能である。監視センター２は、多数の建築物に設けられたエレベーター装置１と通信する。 Embodiment 1.
FIG. 1 is a diagram showing an example of an elevator system. The elevator system shown in FIG. 1 includes a plurality of elevator devices 1 and a monitoring center 2. Each of the elevator devices 1 is capable of bidirectional communication with the monitoring center 2. The monitoring center 2 communicates with the elevator devices 1 provided in many buildings.
　エレベーター装置１は、例えばかご３及びつり合いおもり４を備える。かご３は、昇降路５を上下に移動する。つり合いおもり４は、昇降路５を上下に移動する。かご３及びつり合いおもり４は、主ロープ６によって昇降路５に吊り下げられる。 The elevator device 1 includes, for example, a basket 3 and a balance weight 4. The car 3 moves up and down on the hoistway 5. The balance weight 4 moves up and down on the hoistway 5. The car 3 and the counterweight 4 are suspended from the hoistway 5 by the main rope 6.
　主ロープ６は、巻上機７の駆動綱車８に巻き掛けられる。かご３は、巻上機７によって駆動される。制御装置９は、利用者をかご３で運ぶための運転を制御する。例えば、制御装置９は、巻上機７を制御する。制御装置９は、かご３に備えられた機器を制御する。図１は、巻上機７及び制御装置９が昇降路５の上方の機械室１０に設置される例を示す。巻上機７及び制御装置９は、昇降路５に設置されても良い。巻上機７が昇降路５に設置される場合、巻上機７は、昇降路５の頂部に設置されても良いし、昇降路５のピットに設置されても良い。 The main rope 6 is wound around the drive sheave 8 of the hoisting machine 7. The car 3 is driven by the hoist 7. The control device 9 controls the operation for carrying the user in the car 3. For example, the control device 9 controls the hoisting machine 7. The control device 9 controls the equipment provided in the car 3. FIG. 1 shows an example in which the hoisting machine 7 and the control device 9 are installed in the machine room 10 above the hoistway 5. The hoisting machine 7 and the control device 9 may be installed in the hoistway 5. When the hoisting machine 7 is installed in the hoistway 5, the hoisting machine 7 may be installed at the top of the hoistway 5 or in the pit of the hoistway 5.
　制御装置９に、監視装置１１が接続される。監視装置１１は、監視センター２と通信する。監視装置１１が監視センター２と通信する方式は、如何なる方式でも良い。監視装置１１は、例えば特定のネットワークを介して監視センター２と通信する。上記ネットワークは、ＩＰネットワークでも良い。ＩＰネットワークは、通信プロトコルとしてＩＰ（Ｉｎｔｅｒｎｅｔ　Ｐｒｏｔｏｃｏｌ）を用いた通信ネットワークである。 The monitoring device 11 is connected to the control device 9. The monitoring device 11 communicates with the monitoring center 2. The method in which the monitoring device 11 communicates with the monitoring center 2 may be any method. The monitoring device 11 communicates with the monitoring center 2 via, for example, a specific network. The above network may be an IP network. The IP network is a communication network that uses IP (Internet Protocol) as a communication protocol.
　図２は、エレベーター装置１の例を示す図である。図２は、かご３の内部とかご３が停止した乗場１２の例を示す図である。乗場１２は、エレベーター装置１が備えられた建築物の各階に設けられる。乗場１２は、建築物の一部の階のみに設けられても良い。 FIG. 2 is a diagram showing an example of the elevator device 1. FIG. 2 is a diagram showing an example of the inside of the car 3 and the landing 12 where the car 3 is stopped. The landing 12 is provided on each floor of the building equipped with the elevator device 1. The landing 12 may be provided only on a part of the floor of the building.
　エレベーター装置１に複数のセンサが備えられる。本実施の形態に示す例では、監視センター２に備えられた学習装置４０が、ペットを連れているエレベーター利用者又はそのペットの少なくとも一方の動作パターンを学習する。本実施の形態では、上記学習に必要なデータを取得し得る手段のことをセンサという。例えば、図２に示す例では、かご３に設けられたカメラ１３、マイクロホン１４、ボタン１５、秤装置１６、及び光電装置１７は、センサとして機能する。同様に、乗場１２に設けられたカメラ２０、マイクロホン２１、ボタン２２、及び光電装置２３は、センサとして機能する。 The elevator device 1 is equipped with a plurality of sensors. In the example shown in the present embodiment, the learning device 40 provided in the monitoring center 2 learns the movement pattern of at least one of the elevator user carrying the pet or the pet. In the present embodiment, the means capable of acquiring the data necessary for the above learning is referred to as a sensor. For example, in the example shown in FIG. 2, the camera 13, the microphone 14, the button 15, the weighing device 16, and the photoelectric device 17 provided in the car 3 function as sensors. Similarly, the camera 20, the microphone 21, the button 22, and the photoelectric device 23 provided in the landing 12 function as sensors.
　カメラ１３、マイクロホン１４、ボタン１５、秤装置１６、及び光電装置１７は、かご３にいる利用者又はペットの動作パターンを学習するために必要なデータを取得するためのセンサの例である。エレベーター装置１は、カメラ１３、マイクロホン１４、ボタン１５、秤装置１６、及び光電装置１７の何れかの組み合わせをセンサとして備えても良い。 The camera 13, the microphone 14, the button 15, the weighing device 16, and the photoelectric device 17 are examples of sensors for acquiring data necessary for learning the movement pattern of the user or pet in the car 3. The elevator device 1 may include any combination of the camera 13, the microphone 14, the button 15, the weighing device 16, and the photoelectric device 17 as a sensor.
　カメラ１３は、かご３に設けられる。カメラ１３は、かご３の内部を撮影する。カメラ１３によって撮影された映像を示す時系列データは、監視装置１１に入力される。 The camera 13 is provided in the car 3. The camera 13 photographs the inside of the car 3. The time-series data indicating the image captured by the camera 13 is input to the monitoring device 11.
　マイクロホン１４は、かご３に設けられる。マイクロホン１４は、操作盤１８に備えられたインターホンの一部でも良い。マイクロホン１４が取得した音を示す時系列データは、監視装置１１に入力される。ボタン１５は、かご３に設けられた操作盤１８に備えられる。ボタン１５は、例えば行先ボタンである。ボタン１５は、開ボタン或いは閉ボタンでも良い。ボタン１５は、外部通報用のインターホンボタンでも良い。ボタン１５が押されたか否かを示す時系列データは、制御装置９を介して監視装置１１に入力される。 The microphone 14 is provided in the basket 3. The microphone 14 may be a part of the intercom provided in the operation panel 18. The time-series data indicating the sound acquired by the microphone 14 is input to the monitoring device 11. The button 15 is provided on the operation panel 18 provided in the car 3. The button 15 is, for example, a destination button. The button 15 may be an open button or a close button. The button 15 may be an intercom button for external reporting. The time series data indicating whether or not the button 15 is pressed is input to the monitoring device 11 via the control device 9.
　秤装置１６は、かご３の負荷を検出する。秤装置１６は、かご３に設けられる。他の例として、主ロープ６の端部に秤装置１６と同様の機能を有するセンサが設けられても良い。秤装置１６によって検出された負荷を示す時系列データは、制御装置９を介して監視装置１１に入力される。 The weighing device 16 detects the load on the car 3. The weighing device 16 is provided in the car 3. As another example, a sensor having the same function as the weighing device 16 may be provided at the end of the main rope 6. The time series data indicating the load detected by the weighing device 16 is input to the monitoring device 11 via the control device 9.
　光電装置１７は、かご３のドア１９に設けられる。光電装置１７は、ドア１９に挟まれる異物を検出するための装置である。光電装置１７が異物を検出したか否かを示す時系列データは、制御装置９を介して監視装置１１に入力される。 The photoelectric device 17 is provided on the door 19 of the car 3. The photoelectric device 17 is a device for detecting foreign matter sandwiched between the doors 19. Time-series data indicating whether or not the photoelectric device 17 has detected a foreign substance is input to the monitoring device 11 via the control device 9.
　カメラ２０、マイクロホン２１、ボタン２２、及び光電装置２３は、乗場１２にいる利用者又はペットの動作パターンを学習するために必要なデータを取得するためのセンサの例である。エレベーター装置１は、カメラ２０、マイクロホン２１、ボタン２２、及び光電装置２３の何れかの組み合わせをセンサとして備えても良い。 The camera 20, the microphone 21, the button 22, and the photoelectric device 23 are examples of sensors for acquiring data necessary for learning the movement pattern of the user or pet at the landing 12. The elevator device 1 may include any combination of the camera 20, the microphone 21, the button 22, and the photoelectric device 23 as a sensor.
　カメラ２０は、乗場１２に設けられる。カメラ２０は、乗場１２を撮影する。カメラ２０によって撮影された映像を示す時系列データは、監視装置１１に入力される。 The camera 20 is provided at the landing 12. The camera 20 photographs the landing 12. The time series data indicating the image captured by the camera 20 is input to the monitoring device 11.
　マイクロホン２１は、乗場１２に設けられる。マイクロホン２１は、操作盤２４に備えられたインターホンの一部でも良い。マイクロホン２１が取得した音を示す時系列データは、監視装置１１に入力される。ボタン２２は、乗場１２に設けられた操作盤２４に備えられる。ボタン２２は、例えば上ボタン或いは下ボタンである。ボタン２２が押されたか否かを示す時系列データは、制御装置９を介して監視装置１１に入力される。 The microphone 21 is provided at the landing 12. The microphone 21 may be a part of the intercom provided in the operation panel 24. The time-series data indicating the sound acquired by the microphone 21 is input to the monitoring device 11. The button 22 is provided on the operation panel 24 provided in the landing 12. The button 22 is, for example, an up button or a down button. The time series data indicating whether or not the button 22 is pressed is input to the monitoring device 11 via the control device 9.
　光電装置２３は、乗場１２のドア２５に設けられる。光電装置２３は、ドア２５に挟まれる異物を検出するための装置である。光電装置２３が異物を検出したか否かを示す時系列データは、制御装置９を介して監視装置１１に入力される。 The photoelectric device 23 is provided on the door 25 of the landing 12. The photoelectric device 23 is a device for detecting foreign matter sandwiched between the doors 25. Time-series data indicating whether or not the photoelectric device 23 has detected a foreign substance is input to the monitoring device 11 via the control device 9.
　図３は、監視装置１１が有する機能の例を示す図である。監視装置１１は、例えば記憶部３０、通信部３１、及び検出部３２を備える。記憶部３０に、エレベーターに関する各種データが記憶される。例えば、記憶部３０に、上記複数のセンサのそれぞれによって取得された時系列データが記憶される。 FIG. 3 is a diagram showing an example of the function of the monitoring device 11. The monitoring device 11 includes, for example, a storage unit 30, a communication unit 31, and a detection unit 32. Various data related to the elevator are stored in the storage unit 30. For example, the storage unit 30 stores time-series data acquired by each of the plurality of sensors.
　また、記憶部３０に、エレベーターの状態を表すデータが記憶される。以下においては、エレベーターの状態を表すデータを状態データとも表記する。例えば、状態データに、かご３が走行しているか否かを示す時系列データが含まれる。他の例として、状態データに、ドア１９が完全に閉まっているか否かを示す時系列データが含まれる。状態データは、例えば制御装置９から取得される。センサによって取得された時系列データの一部が状態データとして記憶部３０に記憶されても良い。 In addition, data representing the state of the elevator is stored in the storage unit 30. In the following, data representing the state of the elevator is also referred to as state data. For example, the state data includes time-series data indicating whether or not the car 3 is running. As another example, the state data includes time series data indicating whether the door 19 is completely closed. The state data is acquired from, for example, the control device 9. A part of the time series data acquired by the sensor may be stored in the storage unit 30 as state data.
　通信部３１は、記憶部３０に記憶されたデータを監視センター２に送信する。例えば、通信部３１は、監視センター２に定期的にデータを送信する。通信部３１は、特定のイベントが発生した時に監視センター２にデータを送信しても良い。通信部３１は、監視センター２からの要求に応じて監視センター２にデータを送信しても良い。 The communication unit 31 transmits the data stored in the storage unit 30 to the monitoring center 2. For example, the communication unit 31 periodically transmits data to the monitoring center 2. The communication unit 31 may transmit data to the monitoring center 2 when a specific event occurs. The communication unit 31 may transmit data to the monitoring center 2 in response to a request from the monitoring center 2.
　例えば、検出部３２は、ペットに繋がれたリードがドア１９或いはドア２５に挟まれたことを検出する。以下においては、ペットに繋がれたリードがドア１９或いはドア２５に挟まれることを「リード挟み」ともいう。通信部３１は、検出部３２がリード挟みを検出すると、記憶部３０に記憶されたデータを監視センター２に送信しても良い。 For example, the detection unit 32 detects that the lead connected to the pet is sandwiched between the door 19 or the door 25. In the following, the fact that the lead connected to the pet is sandwiched between the door 19 or the door 25 is also referred to as “lead pinching”. When the detection unit 32 detects the lead pinch, the communication unit 31 may transmit the data stored in the storage unit 30 to the monitoring center 2.
　監視センター２は、エレベーター装置１を監視する機能に加え、学習装置４０を備える。学習装置４０は、エレベーター装置１と双方向の通信が可能である。学習装置４０は、リード挟みを検出するための検出条件を、ペット或いはそのペットを連れている利用者の動作パターンを学習することによって生成するための装置である。 The monitoring center 2 is equipped with a learning device 40 in addition to the function of monitoring the elevator device 1. The learning device 40 is capable of bidirectional communication with the elevator device 1. The learning device 40 is a device for generating detection conditions for detecting lead pinching by learning a pet or an operation pattern of a user carrying the pet.
　図４は、実施の形態１における学習装置４０の例を示す図である。学習装置４０は、例えば記憶部４１、学習部４２、条件生成部４３、及び送信部４４を備える。 FIG. 4 is a diagram showing an example of the learning device 40 according to the first embodiment. The learning device 40 includes, for example, a storage unit 41, a learning unit 42, a condition generation unit 43, and a transmission unit 44.
　学習装置４０には、様々な建築物に設けられた多数のエレベーター装置１からデータが送信されてくる。記憶部４１に、各エレベーター装置１の監視装置１１から送信されてきたデータが記憶される。例えば、記憶部４１に、エレベーター装置１が有する複数のセンサのそれぞれによって取得された時系列データが記憶される。記憶部４１に、エレベーターの状態データが記憶される。例えば、記憶部４１に、かご３が走行しているか否かを示す時系列データが記憶される。記憶部４１に、ドア１９が完全に閉まっているか否かを示す時系列データが記憶される。 Data is transmitted to the learning device 40 from a large number of elevator devices 1 provided in various buildings. The data transmitted from the monitoring device 11 of each elevator device 1 is stored in the storage unit 41. For example, the storage unit 41 stores time-series data acquired by each of the plurality of sensors included in the elevator device 1. Elevator status data is stored in the storage unit 41. For example, the storage unit 41 stores time-series data indicating whether or not the car 3 is running. The storage unit 41 stores time-series data indicating whether or not the door 19 is completely closed.
　他の例として、記憶部４１に、特定のイベントが発生したことを示すイベントデータが記憶される。例えば、検出部３２がリード挟みを検出すると、リード挟みが発生したことを示すイベントデータが記憶部４１に記憶される。特定の故障が検出されると、その故障が発生したことを示すイベントデータが記憶部４１に記憶される。エレベーター装置１から監視センター２に通報が行われると、通報が行われたことを示すイベントデータが記憶部４１に記憶される。 As another example, event data indicating that a specific event has occurred is stored in the storage unit 41. For example, when the detection unit 32 detects the lead pinch, event data indicating that the lead pinch has occurred is stored in the storage unit 41. When a specific failure is detected, event data indicating that the failure has occurred is stored in the storage unit 41. When a report is sent from the elevator device 1 to the monitoring center 2, event data indicating that the report has been made is stored in the storage unit 41.
　以下に、図５も参照し、学習装置４０が有する機能について詳しく説明する。図５は、実施の形態１における学習装置４０の動作例を示すフローチャートである。 The functions of the learning device 40 will be described in detail below with reference to FIG. FIG. 5 is a flowchart showing an operation example of the learning device 40 according to the first embodiment.
　学習部４２は、リード挟みが発生した時のペット又はそのペットを連れた利用者の少なくとも一方の動作パターンを学習する（Ｓ１０１）。以下においては、ペットを連れた利用者のことを飼い主ともいう。学習部４２は、ペットの動作パターンと飼い主の動作パターンとの双方を学習しても良い。学習部４２は、ペットの動作パターン又は飼い主の動作パターンの一方のみを学習しても良い。学習部４２は、かご３内にいるペット又は飼い主の一方の動作パターンと乗場１２にいるペット又は飼い主の他方の動作パターンとの双方を学習しても良い。学習部４２は、かご３内にいるペット又は飼い主の動作パターンのみを学習しても良い。学習部４２は、乗場１２にいるペット又は飼い主の動作パターンのみを学習しても良い。 The learning unit 42 learns the movement pattern of at least one of the pet and the user with the pet when the lead pinching occurs (S101). In the following, a user with a pet is also referred to as an owner. The learning unit 42 may learn both the movement pattern of the pet and the movement pattern of the owner. The learning unit 42 may learn only one of the pet's motion pattern and the owner's motion pattern. The learning unit 42 may learn both the movement pattern of one of the pets or the owner in the car 3 and the movement pattern of the other of the pet or the owner in the landing 12. The learning unit 42 may learn only the movement patterns of the pet or the owner in the car 3. The learning unit 42 may learn only the movement patterns of the pet or the owner at the landing 12.
　記憶部４１に、少なくとも特定の第１期間に複数のセンサのそれぞれによって取得された時系列データが記憶される。第１期間は、エレベーター装置１においてドア１９が閉じてからかご３が走行を開始した直後を含む期間である。学習部４２は、記憶部４１に記憶された時系列データのうち、第１時系列データ、第２時系列データ、及び第３時系列データに基づいてＳ１０１の学習を行う。 The storage unit 41 stores time-series data acquired by each of the plurality of sensors at least in a specific first period. The first period is a period including immediately after the door 19 is closed and the car 3 starts traveling in the elevator device 1. The learning unit 42 learns S101 based on the first time series data, the second time series data, and the third time series data among the time series data stored in the storage unit 41.
　第１時系列データは、第１期間に複数のセンサのそれぞれによって取得された時系列データである。第１時系列データは、リード挟みが実際に発生し、且つ検出部３２がリード挟みを検出した時に取得されたデータである。即ち、第１時系列データは、リード挟みの検出が適切に行われた際に取得されたデータである。 The first time series data is time series data acquired by each of a plurality of sensors during the first period. The first time-series data is data acquired when the lead pinching actually occurs and the detection unit 32 detects the lead pinching. That is, the first time series data is the data acquired when the detection of the lead pinch is appropriately performed.
　第２時系列データは、第１期間に複数のセンサのそれぞれによって取得された時系列データである。第２時系列データは、リード挟みが実際には発生していなかったが、検出部３２がリード挟みを検出した時に取得されたデータである。即ち、第２時系列データは、リード挟みについて誤検出がなされた際に取得されたデータである。 The second time series data is time series data acquired by each of a plurality of sensors in the first period. The second time-series data is data acquired when the detection unit 32 detects the lead pinching, although the lead pinching did not actually occur. That is, the second time-series data is the data acquired when the lead pinching is erroneously detected.
　第３時系列データは、第１期間に複数のセンサのそれぞれによって取得された時系列データである。第３時系列データは、リード挟みが実際に発生したが、検出部３２がリード挟みを検出しなかった時に取得されたデータである。即ち、第３時系列データは、リード挟みについて検出漏れがなされた際に取得されたデータである。 The third time series data is time series data acquired by each of a plurality of sensors in the first period. The third time-series data is data acquired when the lead pinching actually occurred but the detection unit 32 did not detect the lead pinching. That is, the third time-series data is the data acquired when the detection omission is made for the lead pinch.
　このように、学習部４２は、少なくとも第１時系列データ、第２時系列データ、及び第３時系列データを入力データとして、動作パターンの学習を行う。なお、リード挟みは頻繁に発生する事象ではないため、上記入力データの選別は自動化されていなくても良い。 In this way, the learning unit 42 learns the operation pattern by using at least the first time series data, the second time series data, and the third time series data as input data. Since lead pinching is not a frequent event, the selection of the input data does not have to be automated.
　学習部４２が動作パターンの学習を行う方法は、如何なる方法でも構わない。表１は、学習部４２が学習した、リード挟みが発生した時の動作パターンの例を示す。 The method in which the learning unit 42 learns the motion pattern may be any method. Table 1 shows an example of the operation pattern when the lead pinching occurs, which was learned by the learning unit 42.
Figure JPOXMLDOC01-appb-T000001
Figure JPOXMLDOC01-appb-T000001
　学習部４２による学習によって得られた動作パターンには、例えば複数の動作が含まれる。表１は、最も簡単な例として、動作パターンに２つの動作が含まれる例を示す。即ち、表１に示す例では、動作パターンに、第１動作と第１動作の後に行われる第２動作とが含まれる。例えば、第１動作は、ドア１９が閉まった直後で且つかご３の走行が開始される前に行われる動作である。第２動作は、かご３の走行が開始された直後の動作である。 The motion pattern obtained by learning by the learning unit 42 includes, for example, a plurality of motions. Table 1 shows, as the simplest example, an example in which an operation pattern includes two operations. That is, in the example shown in Table 1, the operation pattern includes the first operation and the second operation performed after the first operation. For example, the first operation is an operation performed immediately after the door 19 is closed and before the car 3 starts traveling. The second operation is an operation immediately after the running of the car 3 is started.
　学習部４２による学習によって得られた動作パターンに、３つ以上の動作が含まれても良い。例えば、動作パターンに、第２動作の後に行われる第３動作が含まれても良い。一例として、かご３内にいるペットの第２動作が「ドア側に引き寄せられる」ことである場合に、「床に張り付き停止、又は宙に浮く」ことが第３動作として動作パターンに含まれても良い。 The motion pattern obtained by learning by the learning unit 42 may include three or more motions. For example, the operation pattern may include a third operation performed after the second operation. As an example, when the second movement of the pet in the car 3 is "pulled toward the door", "stopping on the floor or floating in the air" is included in the movement pattern as the third movement. Is also good.
　条件生成部４３は、学習部４２が学習した動作パターンに基づいて、検出部３２がリード挟みを検出するための検出条件を生成する（Ｓ１０２）。例えば、検出条件に、動作パターンに含まれる各動作に対応する条件が含まれる。表１に示すように動作パターンに第１動作と第２動作とが含まれていれば、検出条件には、第１動作に対応する第１条件と第２動作に対応する第２条件とが含まれる。動作パターンに第３動作が更に含まれていれば、検出条件に、第３動作に対応する第３条件が更に含まれる。 The condition generation unit 43 generates a detection condition for the detection unit 32 to detect the lead pinch based on the operation pattern learned by the learning unit 42 (S102). For example, the detection condition includes a condition corresponding to each operation included in the operation pattern. As shown in Table 1, if the operation pattern includes the first operation and the second operation, the detection conditions include the first condition corresponding to the first operation and the second condition corresponding to the second operation. included. If the operation pattern further includes the third operation, the detection condition further includes the third condition corresponding to the third operation.
　第１条件は、第１動作が行われているか否かを、上記複数のセンサの少なくとも何れか一つが取得したデータに基づいて判定するための条件である。例えば、学習部４２が学習した動作パターンに、かご３内のペットが「吠える」ことが第１動作として含まれている場合を考える。かかる場合、条件生成部４３は、以下の場合に第１条件が成立するように検出条件を生成する。
・ドア１９が閉まった直後で且つかご３の走行が開始される前に、マイクロホン１４が検出した音の大きさが閾値を超える。 The first condition is a condition for determining whether or not the first operation is being performed based on the data acquired by at least one of the plurality of sensors. For example, consider a case where the motion pattern learned by the learning unit 42 includes "barking" of the pet in the car 3 as the first motion. In such a case, the condition generation unit 43 generates a detection condition so that the first condition is satisfied in the following cases.
-The loudness of the sound detected by the microphone 14 exceeds the threshold value immediately after the door 19 is closed and before the car 3 starts running.
　他の例として、動作パターンに、かご３内のペットが「暴れる（跳ねる）」ことが第１動作として含まれていれば、条件生成部４３は、以下の場合に第１条件が成立するように検出条件を生成する。
・ドア１９が閉まった直後で且つかご３の走行が開始される前に、秤装置１６によって検出された負荷の変動が閾値を超える。 As another example, if the motion pattern includes "rambling (bouncing)" of the pet in the car 3 as the first motion, the condition generation unit 43 will satisfy the first condition in the following cases. Generate detection conditions in.
The load fluctuation detected by the weighing device 16 exceeds the threshold value immediately after the door 19 is closed and before the car 3 starts traveling.
　第２条件は、第２動作が行われているか否かを、上記複数のセンサの少なくとも何れか一つが取得したデータに基づいて判定するための条件である。例えば、学習部４２が学習した動作パターンに、かご３内のペットが「ドア側に引き寄せられる」ことが第２動作として含まれている場合を考える。かかる場合、条件生成部４３は、以下の場合に第２条件が成立するように検出条件を生成する。
・かご３の走行が開始された直後に、カメラ１３によって撮影された映像のうち特定の領域を示す映像に閾値を超える変化が発生する。 The second condition is a condition for determining whether or not the second operation is being performed based on the data acquired by at least one of the plurality of sensors. For example, consider a case where the motion pattern learned by the learning unit 42 includes "attracting the pet in the car 3 to the door side" as the second motion. In such a case, the condition generation unit 43 generates a detection condition so that the second condition is satisfied in the following cases.
Immediately after the car 3 starts running, a change exceeding the threshold value occurs in the image showing a specific area in the image captured by the camera 13.
　他の例として、動作パターンに、かご３内の飼い主が「操作盤に近づき操作」することが第２動作として含まれていれば、条件生成部４３は、以下の場合に第２条件が成立するように検出条件を生成する。
・かご３の走行が開始された直後に、ボタン１５が連打される。 As another example, if the operation pattern includes that the owner in the car 3 "approaches the operation panel and operates" as the second operation, the condition generation unit 43 satisfies the second condition in the following cases. Generate detection conditions so that
-Immediately after the car 3 starts running, the button 15 is repeatedly hit.
　第１条件は、第１動作が行われているか否かを、複数のセンサが取得したデータに基づいて判定するための条件であっても良い。同様に、第２条件は、第２動作が行われているか否かを、複数のセンサが取得したデータに基づいて判定するための条件であっても良い。 The first condition may be a condition for determining whether or not the first operation is being performed based on the data acquired by a plurality of sensors. Similarly, the second condition may be a condition for determining whether or not the second operation is being performed based on the data acquired by the plurality of sensors.
　送信部４４は、条件生成部４３によって生成された検出条件をエレベーター装置１に送信する（Ｓ１０３）。エレベーター装置１では、監視装置１１の通信部３１が学習装置４０から検出条件を受信する。監視装置１１が学習装置４０から検出条件を受信すると、検出部３２は、条件生成部４３が生成した検出条件に基づいて、リード挟みを検出する。 The transmission unit 44 transmits the detection condition generated by the condition generation unit 43 to the elevator device 1 (S103). In the elevator device 1, the communication unit 31 of the monitoring device 11 receives the detection condition from the learning device 40. When the monitoring device 11 receives the detection condition from the learning device 40, the detection unit 32 detects the lead pinch based on the detection condition generated by the condition generation unit 43.
　図６は、エレベーター装置１の動作例を示すフローチャートである。エレベーター装置１では、検出部３２がリード挟みを検出すると（Ｓ２０１）、かご３が走行しているか否かが判定される（Ｓ２０２）。リード挟みが検出された際にかご３が走行していなければ、制御装置９は、ドア１９を開放する（Ｓ２０３）。これにより、かご３が停止している乗場１２のドア２５も開放する。制御装置９は、Ｓ２０３でドア１９を開放すると、ドア１９及びドア２５を一定時間開放した状態に保持しておく（Ｓ２０４）。 FIG. 6 is a flowchart showing an operation example of the elevator device 1. In the elevator device 1, when the detection unit 32 detects the lead pinch (S201), it is determined whether or not the car 3 is traveling (S202). If the car 3 is not running when the lead pinch is detected, the control device 9 opens the door 19 (S203). As a result, the door 25 of the landing 12 where the car 3 is stopped is also opened. When the door 19 is opened in S203, the control device 9 keeps the door 19 and the door 25 in the open state for a certain period of time (S204).
　リード挟みが検出された際にかご３が走行していれば（Ｓ２０２のＹｅｓ）、制御装置９は、かご３を緊急停止させる（Ｓ２０５）。制御装置９は、かご３を緊急停止させると、かご３のドア１９を開放することが可能な位置にかご３が停止しているか否かを判定する（Ｓ２０６）。例えば、かご３のドア１９を開放すると乗場１２のドア２５がドア１９に連動する位置にかご３が停止していれば、Ｓ２０６でＹｅｓと判定される。Ｓ２０６でＹｅｓと判定されると、制御装置９は、ドア１９を開放する（Ｓ２０３）。また、制御装置９は、Ｓ２０３でドア１９を開放すると、ドア１９及びドア２５を一定時間開放した状態に保持しておく（Ｓ２０４）。Ｓ２０３でドア１９が開放される場合は、ドア１９が開放される前に、ドア１９が開く旨の案内がかご３内で行われても良い。 If the car 3 is running when the lead pinch is detected (Yes in S202), the control device 9 makes an emergency stop of the car 3 (S205). When the car 3 is urgently stopped, the control device 9 determines whether or not the car 3 is stopped at a position where the door 19 of the car 3 can be opened (S206). For example, if the door 19 of the car 3 is opened and the car 3 is stopped at a position where the door 25 of the landing 12 is interlocked with the door 19, the result is determined to be Yes in S206. When it is determined Yes in S206, the control device 9 opens the door 19 (S203). Further, when the door 19 is opened in S203, the control device 9 keeps the door 19 and the door 25 in the open state for a certain period of time (S204). When the door 19 is opened in S203, the guidance to the effect that the door 19 is opened may be given in the car 3 before the door 19 is opened.
　なお、学習部４２による学習が初期段階である場合、検出部３２は、学習部４２による学習が反映されていない検出条件に基づいてリード挟みを検出しても良い。その後、学習部４２による学習が一定期間行われると、検出部３２は、学習部４２による学習が反映された検出条件に基づいてリード挟みを検出する。また、監視装置１１に記憶された検出条件は、送信部４４から最新の検出条件を受信することによって定期的に更新される。 When the learning by the learning unit 42 is in the initial stage, the detection unit 32 may detect the lead pinch based on the detection condition in which the learning by the learning unit 42 is not reflected. After that, when the learning by the learning unit 42 is performed for a certain period of time, the detection unit 32 detects the lead pinch based on the detection condition in which the learning by the learning unit 42 is reflected. Further, the detection conditions stored in the monitoring device 11 are periodically updated by receiving the latest detection conditions from the transmission unit 44.
　条件生成部４３が検出条件を作成する方法は、どのような方法であっても良い。例えば、条件生成部４３は、学習部４２が学習した動作パターンに基づいて、新規の検出条件を生成する。条件生成部４３は、学習部４２が学習した動作パターンに基づいて、以前に生成した検出条件を修正しても良い。 The method for creating the detection condition by the condition generation unit 43 may be any method. For example, the condition generation unit 43 generates a new detection condition based on the operation pattern learned by the learning unit 42. The condition generation unit 43 may modify the previously generated detection condition based on the operation pattern learned by the learning unit 42.
　例えば、条件生成部４３は、第１条件から第３条件を有する既存の検出条件に、第４条件を新たに追加しても良い。条件生成部４３は、既存の検出条件に含まれる第３条件を削除しても良い。条件生成部４３は、第１条件を判定する際に使用するセンサを変更しても良い。条件生成部４３は、第１条件を判定する際に使用する閾値を変更しても良い。条件生成部４３は、既存の検出条件が複数存在する場合に、一部の検出条件を削除しても良い。 For example, the condition generation unit 43 may newly add the fourth condition to the existing detection conditions having the first condition to the third condition. The condition generation unit 43 may delete the third condition included in the existing detection condition. The condition generation unit 43 may change the sensor used when determining the first condition. The condition generation unit 43 may change the threshold value used when determining the first condition. When a plurality of existing detection conditions exist, the condition generation unit 43 may delete some of the detection conditions.
　即ち、条件生成部４３は、Ｓ１０２において以下の処理を実行する。
・条件判定に使用するセンサの種類の決定
・条件判定の順番（条件判定を行うタイミング）の決定
・条件が成立するデータ範囲の設定 That is, the condition generation unit 43 executes the following processing in S102.
-Determine the type of sensor used for condition judgment-Determine the order of condition judgment (timing to perform condition judgment) -Set the data range where the condition is satisfied
　本実施の形態に示す例であれば、リード挟みを検出するための検出条件を、ペット又は飼い主の動作パターンを学習することによって生成することができる。なお、本実施の形態に示す例では、学習部４２の機能と条件生成部４３の機能とは、人工知能（ＡＩ）を利用して実現しても良い。 In the example shown in the present embodiment, the detection condition for detecting the lead pinch can be generated by learning the movement pattern of the pet or the owner. In the example shown in this embodiment, the function of the learning unit 42 and the function of the condition generation unit 43 may be realized by using artificial intelligence (AI).
　また、本実施の形態に示す例では、エレベーター装置１において、制御装置９及び複数のセンサから取得した、上記検出条件を生成するための学習用データが記憶部３０に記憶される。そして、通信部３１は、記憶部３０に記憶された学習用データを学習装置４０に送信し、学習装置４０から検出条件を受信する。本実施の形態に示す例であれば、学習装置４０で生成された検出条件をエレベーター装置１において利用することができる。また、学習装置４０で生成された最新の検出条件をエレベーター装置１に反映させることができる。 Further, in the example shown in the present embodiment, in the elevator device 1, the learning data for generating the detection conditions acquired from the control device 9 and the plurality of sensors is stored in the storage unit 30. Then, the communication unit 31 transmits the learning data stored in the storage unit 30 to the learning device 40, and receives the detection conditions from the learning device 40. In the example shown in the present embodiment, the detection conditions generated by the learning device 40 can be used in the elevator device 1. Further, the latest detection conditions generated by the learning device 40 can be reflected in the elevator device 1.
　本実施の形態では、動作パターンに含まれる複数の動作が、ペット或いは飼い主の一方のみに行われる例について説明した。これは一例である。動作パターンに含まれる複数の動作は、ペット及び飼い主の双方によって行われても良い。例えば、動作パターンに第１動作及び第２動作が含まれる場合、第１動作がかご３の中でペットにより行われる動作であり、第２動作が乗場１２で飼い主により行われる動作であっても良い。同様に、第１動作が乗場１２でペットにより行われる動作であり、第２動作がかご３の中で飼い主により行われる動作であっても良い。上記例において、第１動作が飼い主により行われる動作であり、第２動作がペットにより行われる動作であっても良い。 In the present embodiment, an example in which a plurality of movements included in the movement pattern are performed only on one of the pet and the owner has been described. This is just an example. The plurality of movements included in the movement pattern may be performed by both the pet and the owner. For example, when the motion pattern includes the first motion and the second motion, even if the first motion is an motion performed by the pet in the car 3 and the second motion is performed by the owner at the landing 12. good. Similarly, the first action may be an action performed by the pet at the landing 12, and the second action may be an action performed by the owner in the car 3. In the above example, the first operation may be an operation performed by the owner, and the second operation may be an operation performed by the pet.
　本実施の形態において、符号４１～４４は、学習装置４０が有する機能を示す。図７は、学習装置４０のハードウェア資源の例を示す図である。学習装置４０は、ハードウェア資源として、例えばプロセッサ５１とメモリ５２とを含む処理回路５０を備える。記憶部４１が有する機能はメモリ５２によって実現される。学習装置４０は、メモリ５２に記憶されたプログラムをプロセッサ５１によって実行することにより、符号４２～４４に示す各部の機能を実現する。 In the present embodiment, reference numerals 41 to 44 indicate the functions of the learning device 40. FIG. 7 is a diagram showing an example of hardware resources of the learning device 40. The learning device 40 includes a processing circuit 50 including, for example, a processor 51 and a memory 52 as hardware resources. The function of the storage unit 41 is realized by the memory 52. The learning device 40 realizes the functions of the respective parts indicated by reference numerals 42 to 44 by executing the program stored in the memory 52 by the processor 51.
　図８は、学習装置４０のハードウェア資源の他の例を示す図である。図８に示す例では、学習装置４０は、例えばプロセッサ５１、メモリ５２、及び専用ハードウェア５３を含む処理回路５０を備える。図８は、学習装置４０が有する機能の一部を専用ハードウェア５３によって実現する例を示す。学習装置４０が有する機能の全部を専用ハードウェア５３によって実現しても良い。専用ハードウェア５３として、単一回路、複合回路、プログラム化したプロセッサ、並列プログラム化したプロセッサ、ＡＳＩＣ、ＦＰＧＡ、又はこれらの組み合わせを採用できる。 FIG. 8 is a diagram showing another example of the hardware resource of the learning device 40. In the example shown in FIG. 8, the learning device 40 includes, for example, a processing circuit 50 including a processor 51, a memory 52, and dedicated hardware 53. FIG. 8 shows an example in which a part of the functions of the learning device 40 is realized by the dedicated hardware 53. All the functions of the learning device 40 may be realized by the dedicated hardware 53. As the dedicated hardware 53, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof can be adopted.
　本実施の形態において、符号３０～３２は、監視装置１１が有する機能を示す。監視装置１１のハードウェア資源は、図７に示す例と同様である。監視装置１１は、ハードウェア資源として、例えばプロセッサとメモリとを含む処理回路を備える。監視装置１１は、メモリに記憶されたプログラムをプロセッサによって実行することにより、符号３１～３２に示す各部の機能を実現する。 In the present embodiment, reference numerals 30 to 32 indicate the functions of the monitoring device 11. The hardware resources of the monitoring device 11 are the same as those shown in FIG. 7. The monitoring device 11 includes a processing circuit including, for example, a processor and a memory as hardware resources. The monitoring device 11 realizes the functions of the respective parts shown by reference numerals 31 to 32 by executing the program stored in the memory by the processor.
　監視装置１１のハードウェア資源は、図８に示す例と同様でも良い。例えば、監視装置１１は、プロセッサ、メモリ、及び専用ハードウェアを含む処理回路を備える。監視装置１１が有する機能の一部は、専用ハードウェアによって実現されても良い。監視装置１１が有する機能の全部が専用ハードウェアによって実現されても良い。 The hardware resources of the monitoring device 11 may be the same as the example shown in FIG. For example, the monitoring device 11 includes a processing circuit including a processor, a memory, and dedicated hardware. Some of the functions of the monitoring device 11 may be realized by dedicated hardware. All the functions of the monitoring device 11 may be realized by dedicated hardware.
　また、制御装置９のハードウェア資源は、図７に示す例と同様である。制御装置９は、ハードウェア資源として、例えばプロセッサとメモリとを含む処理回路を備える。制御装置９は、メモリに記憶されたプログラムをプロセッサによって実行することにより、符号３１～３２に示す各部の機能を実現する。 The hardware resources of the control device 9 are the same as those shown in FIG. 7. The control device 9 includes a processing circuit including, for example, a processor and a memory as hardware resources. The control device 9 realizes the functions of the respective parts shown by reference numerals 31 to 32 by executing the program stored in the memory by the processor.
　制御装置９のハードウェア資源は、図８に示す例と同様でも良い。例えば、制御装置９は、プロセッサ、メモリ、及び専用ハードウェアを含む処理回路を備える。制御装置９が有する機能の一部は、専用ハードウェアによって実現されても良い。制御装置９が有する機能の全部が専用ハードウェアによって実現されても良い。 The hardware resources of the control device 9 may be the same as the example shown in FIG. For example, the control device 9 includes a processing circuit including a processor, a memory, and dedicated hardware. Some of the functions of the control device 9 may be realized by dedicated hardware. All the functions of the control device 9 may be realized by dedicated hardware.
　この発明に係る学習装置は、エレベーター装置と通信可能な管理センター等で利用できる。 The learning device according to the present invention can be used at a management center or the like capable of communicating with an elevator device.
　１　エレベーター装置、　２　監視センター、　３　かご、　４　つり合いおもり、　５　昇降路、　６　主ロープ、　７　巻上機、　８　駆動綱車、　９　制御装置、　１０　機械室、　１１　監視装置、　１２　乗場、　１３　カメラ、　１４　マイクロホン、　１５　ボタン、　１６　秤装置、　１７　光電装置、　１８　操作盤、　１９　ドア、　２０　カメラ、　２１　マイクロホン、　２２　ボタン、　２３　光電装置、　２４　操作盤、　２５　ドア、　３０　記憶部、　３１　通信部、　３２　検出部、　４０　学習装置、　４１　記憶部、　４２　学習部、　４３　条件生成部、　４４　送信部、　５０　処理回路、　５１　プロセッサ、　５２　メモリ、　５３　専用ハードウェア 1 elevator device, 2 monitoring center, 3 basket, 4 balanced weight, 5 hoistway, 6 main rope, 7 hoisting machine, 8 drive sheave, 9 control device, 10 machine room, 11 monitoring device, 12 landing, 13 camera , 14 microphone, 15 button, 16 weighing device, 17 photoelectric device, 18 operation panel, 19 door, 20 camera, 21 microphone, 22 button, 23 photoelectric device, 24 operation panel, 25 door, 30 storage unit, 31 communication unit, 32 detection unit, 40 learning device, 41 storage unit, 42 learning unit, 43 condition generation unit, 44 transmission unit, 50 processing circuit, 51 processor, 52 memory, 53 dedicated hardware

Claims (10)

  1. 　特定の複数のセンサと、
    　リード挟みを検出する検出手段と、
    を備えたエレベーター装置と通信可能な学習装置であって、
    　前記エレベーター装置のドアが閉じてからかごが走行を開始した直後を含む第１期間に前記複数のセンサのそれぞれによって取得された時系列データを記憶する記憶手段と、
    　前記記憶手段に記憶された時系列データのうちの第１時系列データ、第２時系列データ、及び第３時系列データに基づいて、リード挟みが発生した時のペット又はそのペットを連れている利用者の少なくとも一方の動作パターンを学習する学習手段と、
    　前記学習手段によって学習された動作パターンに基づいて、前記検出手段がリード挟みを検出するための検出条件を生成する生成手段と、
    を備え、
    　前記第１時系列データは、リード挟みが実際に発生し、且つ前記検出手段がリード挟みを検出した時に前記複数のセンサのそれぞれが取得した時系列データを含み、
    　前記第２時系列データは、リード挟みが実際には発生していなかったが、前記検出手段がリード挟みを検出した時に前記複数のセンサのそれぞれが取得した時系列データを含み、
    　前記第３時系列データは、リード挟みが実際に発生したが、前記検出手段がリード挟みを検出しなかった時に前記複数のセンサのそれぞれが取得した時系列データを含む学習装置。     With multiple specific sensors
    A detection means for detecting lead pinching and
    It is a learning device that can communicate with an elevator device equipped with
    A storage means for storing time-series data acquired by each of the plurality of sensors during the first period including immediately after the door of the elevator device is closed and immediately after the car starts traveling.
    Based on the first time-series data, the second time-series data, and the third time-series data of the time-series data stored in the storage means, the pet at the time of the lead pinching or the pet is taken. A learning method for learning at least one of the user's movement patterns,
    A generation means for generating a detection condition for the detection means to detect a lead pinch based on an operation pattern learned by the learning means, and a generation means.
    With
    The first time-series data includes time-series data acquired by each of the plurality of sensors when the lead pinch actually occurs and the detection means detects the lead pinch.
    The second time-series data includes time-series data acquired by each of the plurality of sensors when the detection means detects the lead pinch, although the lead pinch did not actually occur.
    The third time-series data is a learning device including time-series data acquired by each of the plurality of sensors when the lead pinching actually occurs but the detecting means does not detect the lead pinching.
  2. 　前記生成手段によって生成された検出条件を前記エレベーター装置に送信する送信手段を更に備えた請求項１に記載の学習装置。 The learning device according to claim 1, further comprising a transmission means for transmitting the detection conditions generated by the generation means to the elevator device.
  3. 　前記複数のセンサは、
    　前記かごに設けられた第１カメラと、
    　前記かごに設けられた第１マイクロホンと、
    　前記かごに設けられた第１操作盤のボタンと、
    　前記かごの負荷を検出する秤装置と、
    　前記かごのドアに挟まれる異物を検出するための光電装置と、
    の何れかの組み合わせを含む請求項１又は請求項２に記載の学習装置。     The plurality of sensors
    The first camera provided in the car and
    The first microphone provided in the car and
    The buttons on the first operation panel provided in the car and
    A weighing device that detects the load on the car and
    A photoelectric device for detecting foreign matter caught in the car door, and
    The learning device according to claim 1 or 2, which comprises any combination of the above.
  4. 　前記複数のセンサは、
    　前記かごが停止する乗場に設けられた第２カメラと、
    　前記乗場に設けられた第２マイクロホンと、
    　前記乗場に設けられた第２操作盤のボタンと、
    　前記乗場のドアに挟まれる異物を検出するための光電装置と、
    の何れかの組み合わせを含む請求項１から請求項３の何れか一項に記載の学習装置。     The plurality of sensors
    A second camera installed at the landing where the car stops,
    The second microphone provided at the landing and
    The buttons on the second operation panel provided at the landing and
    A photoelectric device for detecting foreign matter caught in the door of the landing, and
    The learning device according to any one of claims 1 to 3, which includes any combination of the above.
  5. 　前記学習手段によって学習された動作パターンに、第１動作と前記第１動作の後に行われる第２動作とが含まれ、
    　前記検出条件は、
    　前記第１動作が行われているか否かを、前記複数のセンサの少なくとも何れか一つが取得したデータに基づいて判定するための第１条件と、
    　前記第２動作が行われているか否かを、前記複数のセンサの少なくとも何れか一つが取得したデータに基づいて判定するための第２条件と、
    を含む請求項１から請求項４の何れか一項に記載の学習装置。     The motion pattern learned by the learning means includes a first motion and a second motion performed after the first motion.
    The detection condition is
    A first condition for determining whether or not the first operation is performed based on data acquired by at least one of the plurality of sensors, and
    A second condition for determining whether or not the second operation is performed based on the data acquired by at least one of the plurality of sensors, and
    The learning device according to any one of claims 1 to 4.
  6. 　前記第１動作は、前記かごの中で行われる動作又は前記かごが停止する乗場で行われる動作の一方であり、
    　前記第２動作は、前記かごの中で行われる動作又は前記かごが停止する乗場で行われる動作の他方である請求項５に記載の学習装置。     The first operation is one of an operation performed in the car or an operation performed at the landing where the car stops.
    The learning device according to claim 5, wherein the second operation is the other of the operation performed in the car and the operation performed at the landing where the car stops.
  7. 　利用者をかごで運ぶための運転を制御する制御装置と、
    　特定の複数のセンサと、
    　検出条件に基づいて、リード挟みを検出する検出手段と、
    　前記制御装置及び前記複数のセンサから取得した、前記検出条件を生成するための学習用データを記憶する記憶手段と、
    　前記検出条件を生成する学習装置に前記学習用データを送信し、前記学習装置から前記検出条件を受信する通信手段と、
    を備えたエレベーター装置。     A control device that controls driving to carry the user in a basket,
    With multiple specific sensors
    A detection means that detects lead pinching based on the detection conditions,
    A storage means for storing learning data for generating the detection conditions acquired from the control device and the plurality of sensors.
    A communication means that transmits the learning data to the learning device that generates the detection condition and receives the detection condition from the learning device.
    Elevator device equipped with.
  8. 　前記制御装置は、前記検出手段によってリード挟みが検出されると、前記かごが走行していなければ前記かごのドアを開放し、その後に前記かごのドアを一定時間開放した状態にしておく請求項７に記載のエレベーター装置。 A claim that the control device opens the door of the car if the car is not running when the lead pinching is detected by the detection means, and then keeps the door of the car open for a certain period of time. 7. The elevator device according to 7.
  9. 　前記制御装置は、前記検出手段によってリード挟みが検出されると、前記かごが走行していれば前記かごを非常停止させる請求項７又は請求項８に記載のエレベーター装置。 The elevator device according to claim 7 or 8, wherein when the lead pinching is detected by the detection means, the control device makes an emergency stop of the car if the car is running.
  10. 　前記制御装置は、前記かごを非常停止させた際に前記かごのドアを開放することが可能な位置に前記かごが停止していれば、前記かごのドアを開放する請求項９に記載のエレベーター装置。 The elevator according to claim 9, wherein the control device opens the door of the car if the car is stopped at a position where the door of the car can be opened when the car is stopped in an emergency. apparatus.
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