JP2016166066A - Elevator system - Google Patents

Elevator system Download PDF

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Publication number
JP2016166066A
JP2016166066A JP2015046616A JP2015046616A JP2016166066A JP 2016166066 A JP2016166066 A JP 2016166066A JP 2015046616 A JP2015046616 A JP 2015046616A JP 2015046616 A JP2015046616 A JP 2015046616A JP 2016166066 A JP2016166066 A JP 2016166066A
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blind spot
spot area
passengers
elevator
elevator system
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JP6435215B2 (en
Inventor
武央 西田
Takehisa Nishida
武央 西田
知明 前原
Tomoaki Maehara
知明 前原
貴大 羽鳥
Takahiro HATORI
貴大 羽鳥
孝道 星野
Takamichi Hoshino
孝道 星野
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Hitachi Ltd
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Hitachi Ltd
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Priority to JP2015046616A priority Critical patent/JP6435215B2/en
Priority to CN201610135957.6A priority patent/CN105967007B/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B1/00—Control systems of elevators in general
    • B66B1/02—Control systems without regulation, i.e. without retroactive action
    • B66B1/06—Control systems without regulation, i.e. without retroactive action electric
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B1/00—Control systems of elevators in general
    • B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B1/00—Control systems of elevators in general
    • B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
    • B66B1/3476—Load weighing or car passenger counting devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B1/00—Control systems of elevators in general
    • B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
    • B66B1/46—Adaptations of switches or switchgear
    • B66B1/468—Call registering systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B3/00—Applications of devices for indicating or signalling operating conditions of elevators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B2201/00—Aspects of control systems of elevators
    • B66B2201/10—Details with respect to the type of call input
    • B66B2201/103—Destination call input before entering the elevator car
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B2201/00—Aspects of control systems of elevators
    • B66B2201/20—Details of the evaluation method for the allocation of a call to an elevator car
    • B66B2201/222—Taking into account the number of passengers present in the elevator car to be allocated
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B2201/00—Aspects of control systems of elevators
    • B66B2201/40—Details of the change of control mode
    • B66B2201/402—Details of the change of control mode by historical, statistical or predicted traffic data, e.g. by learning
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B2201/00—Aspects of control systems of elevators
    • B66B2201/40—Details of the change of control mode
    • B66B2201/46—Switches or switchgear
    • B66B2201/4607—Call registering systems
    • B66B2201/4615—Wherein the destination is registered before boarding

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Mechanical Engineering (AREA)
  • Indicating And Signalling Devices For Elevators (AREA)
  • Elevator Control (AREA)

Abstract

PROBLEM TO BE SOLVED: To solve the problem that even if two-dimensional planar distributions or three-dimensional spatial distributions of elevator landings to be obtained through sensors are identical, the number of passengers existing in blind-angle areas differ depending on states in which an elevator is controlled.SOLUTION: A landing detection device 5 that detects the number of passengers and positions of the passengers in a landing is arranged at one or more landings where it is predicted that passengers crowd. Further, at a landing on each floor there are arranged elevator car notification devices 4A, 4B and 4C for notifying the passengers at the landing of arrival of each elevator car, reservation states and the like, which are controlled by elevator car control devices 3A, 3B and 3C respectively.SELECTED DRAWING: Figure 1

Description

本発明はカメラやレーザーなどのセンサにより、エレベータ乗場など、特定の領域内の人物の位置や数を高精度に推定する方法に関する。   The present invention relates to a method for accurately estimating the position and number of persons in a specific area such as an elevator hall using a sensor such as a camera or a laser.

エレベータ乗場などの特定の領域内の人物の位置や数を推定するため、人物の陰に隠れている他の人物を検出する方法として、特開２０１３−１３１１００号公報（特許文献１）に記載の技術がある。この特許文献１には、「本発明の人数予測方法は、所定空間内の所定の二次元平面あるいは三次元空間に存在する物体との距離をセンサによって検出することにより、前記距離の前記二次元平面分布あるいは前記三次元空間分布を取得する取得工程と、前記センサの検出により新たに取得した前記距離の前記二次元平面分布あるいは前記三次元空間分布を、前記所定空間内の人数と前記距離の前記二次元平面分布あるいは前記三次元空間分布との関係を機械学習した学習器に入力して、前記学習器から出力値として予測人数を出力することによって、前記所定空間内の人数を予測する人数予測工程とを含むようにした」という技術が開示されている。   As a method for detecting other persons hidden behind a person in order to estimate the position and number of persons in a specific area such as an elevator hall, it is described in JP2013-131100A (Patent Document 1). There is technology. This Patent Document 1 states that “the method for predicting the number of persons according to the present invention detects the distance to an object existing in a predetermined two-dimensional plane or a three-dimensional space in a predetermined space by means of a sensor. An acquisition step of acquiring a planar distribution or the three-dimensional spatial distribution, and the two-dimensional planar distribution or the three-dimensional spatial distribution of the distance newly acquired by detection of the sensor, the number of persons in the predetermined space and the distance of the distance The number of persons who predicts the number of persons in the predetermined space by inputting the relationship between the two-dimensional planar distribution or the three-dimensional spatial distribution to a machine learning machine and outputting the predicted number of persons as an output value from the learning apparatus. And a prediction process ”is disclosed.

特開２０１３−１３１１００号公報JP2013-131100A

特許文献１記載の技術では、機械学習した学習器により、二次元平面分布あるいは三次元空間分布と、所定空間内の人数が一対一で対応づけられる。しかし、エレベータの乗場等の空間においては、センサから得られる二次元平面分布または三次元空間分布が同じであっても、実際の死角となっている空間内の人数は、エレベータの制御状態に応じて異なる。   In the technique described in Patent Document 1, the machine learning learner associates the two-dimensional planar distribution or the three-dimensional spatial distribution with the number of people in the predetermined space on a one-to-one basis. However, in a space such as an elevator hall, even if the two-dimensional planar distribution or three-dimensional spatial distribution obtained from the sensor is the same, the number of people in the space that is the actual blind spot depends on the control state of the elevator. Different.

センサの死角領域内の乗客数を、エレベータの制御情報に基づいて、前記死角領域に存在する乗客数を推定する。   The number of passengers in the blind spot area is estimated based on the control information of the elevator.

本発明によれば、死角領域内に存在する乗客数を、フロアの交通状況やエレベータの状態に即して推定することができる。   According to the present invention, it is possible to estimate the number of passengers existing in the blind spot area according to the traffic situation of the floor and the state of the elevator.

本発明の一実施の形態によるエレベータシステムの概略を示すブロック構成図。1 is a block configuration diagram showing an outline of an elevator system according to an embodiment of the present invention. エレベータシステムにおける乗場の状況を示す図。The figure which shows the condition of the hall in an elevator system. 死角領域内人数推定手段の動作を示すフローチャート。The flowchart which shows operation | movement of the blind spot area number of persons estimation means. 号機報知装置の報知の有無による乗客の特性を示す図。The figure which shows the characteristic of the passenger by the presence or absence of alerting | reporting of a machine alerting | reporting apparatus. 号機放置の有無によるエレベータシステムにおける乗場の状況を示す図。The figure which shows the condition of the hall in the elevator system by the presence or absence of No. 1 unit leaving. 三次元空間を検出するセンサを用いる計算例の説明図。Explanatory drawing of the example of calculation using the sensor which detects three-dimensional space.

以下、実施例を図面を用いて説明する。   Hereinafter, examples will be described with reference to the drawings.

図１は、本発明の一実施の形態によるエレベータシステムの概略を示すブロック構成図である。かご２Ａ、２Ｂ、２Ｃは、各かごに対応した号機制御装置３Ａ、３Ｂ、３Ｃにより制御される。多数の階へ向かう乗客が集中するロビー階などの乗場には、かごへの乗車前に乗客の行先階を検出し、乗場行先階呼びとして登録するための乗場行先階検出装置６が設置され、その他の階の乗場には、乗場呼び装置７が設置される。ここで、乗場行先階検出装置６は、乗場設置の行先階登録装置や、乗客個人またはその行先階を特定するセンサ、乗客の携帯端末、カードリーダーなどで構成される。また、乗客の混雑が予測される一つ以上の階の乗場には、乗場内の乗客数や乗客の位置を検出する乗場検出装置５が設置される。さらに、各階の乗場には、各かごの到着の報知や、予約状況などを乗場の乗客に報知するための号機報知装置４Ａ、４Ｂ、４Ｃが設置され、それぞれ号機制御装置３Ａ、３Ｂ、３Ｃにより制御される。複数台のかご２Ａ、２Ｂ、２Ｃを制御する群管理制御装置１は、乗場行先階検出装置６からの行先階呼び情報や、乗場呼び装置７からの乗場呼び情報などのホール情報と、かご２Ａ、２Ｂ、２Ｃをそれぞれ制御している各号機制御装置３Ａ、３Ｂ、３Ｃから、かご位置や走行方向などのかご情報を取り込んでいる。群管理制御装置１は、上記ホール情報とかご情報を取り込んで管理するホール・かご情報管理部１０１と、ホール・かご情報管理部１０１からの情報及び日常の交通量に基づいて交通量を少なくとも階床別及び方向別に予測する交通量予測部１０２と、乗場検出装置５により検出された乗場内の乗客数を検出する乗客検出部１０３と、乗場検出装置５により検出された乗場内の死角領域の少なくとも位置と大きさを演算する死角領域演算部１０４と、乗客数検出部１０３により検出された乗場内の乗客数と、死角領域演算部１０４により演算された死角領域の位置及び大きさ及び交通量予測部１０２により演算された死角領域の発生階の交通量と、ホール・かご情報管理部１０１により管理されるかご情報とから、死角領域内に存在する乗客数を推定する死角領域内人数推定部１０５と、乗客検出部１０３により検出された乗場内で検出された乗客数、死角領域内人数推定部１０５により推定された死角領域内の推定人数、交通量予測部１０２により予測される少なくとも階床別・方向別に予測された交通量、ホール・かご情報管理部に管理されるホール情報及びかご情報に基づいて割当てかごを決定し、割当てたかごの号機制御装置へ割当て信号を送信する割当て制御部１０６とを有している。   FIG. 1 is a block diagram showing an outline of an elevator system according to an embodiment of the present invention. The cars 2A, 2B, and 2C are controlled by number machine control devices 3A, 3B, and 3C corresponding to the cars. At landings such as lobby floors where passengers heading to a large number of floors are concentrated, a landing destination floor detection device 6 is installed for detecting the passenger's destination floor before boarding the car and registering it as a landing destination floor call. A hall call device 7 is installed at halls on other floors. Here, the landing destination floor detection device 6 includes a destination floor registration device installed at the landing, a sensor that identifies the individual passenger or the destination floor, a portable terminal of the passenger, a card reader, and the like. A landing detection device 5 that detects the number of passengers in the landing and the position of the passengers is installed at a landing on one or more floors where passenger congestion is predicted. In addition, at the landings on each floor, number notification devices 4A, 4B, and 4C are installed for notification of arrival of each car and reservation status to passengers at the landings, respectively, and by means of number control devices 3A, 3B, and 3C, respectively. Be controlled. The group management control device 1 that controls a plurality of cars 2A, 2B, 2C includes hall information such as destination floor call information from the landing destination floor detection device 6 and hall call information from the hall call device 7, and the car 2A. Car information such as car position and traveling direction is taken in from each of the machine control devices 3A, 3B, and 3C that respectively control 2B and 2C. The group management control device 1 includes a hall / car information management unit 101 that takes in and manages the hall information and car information, and at least determines the traffic volume based on information from the hall / car information management unit 101 and daily traffic. Traffic volume prediction unit 102 that predicts by floor and direction, passenger detection unit 103 that detects the number of passengers in the landing detected by the landing detection device 5, and blind spot area in the landing detected by the landing detection device 5 The blind spot area calculation unit 104 that calculates at least the position and size, the number of passengers in the landing detected by the passenger number detection unit 103, the position, size, and traffic volume of the blind spot area calculated by the blind spot area calculation unit 104 Passengers present in the blind spot area from the traffic volume of the generation floor of the blind spot area calculated by the prediction unit 102 and the car information managed by the hall / car information management part 101 The number of passengers detected in the blind spot area detected by the passenger detection unit 103, the estimated number of persons in the blind spot area estimated by the number of blind area estimation unit 105, and the traffic volume prediction The assigned car is determined based on the traffic volume predicted by at least the floor and the direction predicted by the unit 102, the hall information and the car information managed by the hall / car information management unit, and the assigned car number controller And an allocation control unit 106 for transmitting an allocation signal to the network.

ここで、交通量予測部１０２は、各階や各方向での交通パターンを記憶する。そして、現在の交通状況が過去のどの交通パターンの特徴近いかを判定して、結果に基づいて交通量を予測する。または、時刻や曜日毎に各階や各方向の交通量を記憶し、現在の曜日・時刻に基づいて交通量を予測しても良い。また上記の予測を組み合わせても良い。これらの予測結果は、日常のエレベータの利用状況に基づいて予測されるものである。   Here, the traffic volume prediction unit 102 stores a traffic pattern in each floor and each direction. Then, it is determined which traffic pattern in the past is close to the current traffic situation, and the traffic volume is predicted based on the result. Alternatively, the traffic volume of each floor and each direction may be stored for each time or day of the week, and the traffic volume may be predicted based on the current day of the week / time. Moreover, you may combine said prediction. These prediction results are predicted based on the daily use situation of the elevator.

乗客検出部１０３と、死角領域演算部１０４と、死角領域内人数推定部１０５によって乗場人数推定部を構成しており、この乗場人数推定部は、死角領域も含めた乗場内の総乗客数を推定する。割当て制御部１０６は、この乗場人数推定部によって推定される乗場内の総乗客数に基づいて適切なかごの割当てを行う。   The passenger detection unit 103, the blind spot area calculation unit 104, and the blind spot area number estimation unit 105 constitute a hall number estimation part, and the hall number estimation unit calculates the total number of passengers in the hall including the blind spot area. presume. The assignment control unit 106 assigns an appropriate car based on the total number of passengers in the hall estimated by the hall number estimating unit.

本実施例では、死角領域の大きさと、エレベータの日常の交通量と、直前に出発したかごの出発してからの経過時間に基づいて、死角領域内に含まれる乗客数を推定する。   In the present embodiment, the number of passengers included in the blind spot area is estimated based on the size of the blind spot area, the daily traffic of the elevator, and the elapsed time since the departure of the car that departed immediately before.

図２は本実施例における乗場の状況を示す図である。乗場には乗場検出装置５が設置されている。本実施例では、乗場検出装置５が、乗場内の水平方向に関する二次元平面分布を検出するレーザー測域センサであるとする。乗場検出装置５の出力する信号に基づいて、乗客検出部１０３は検出領域Ａ１から４人の乗客８を検出する。この検出は、例えばレーザー測域センサで、平均的な腰の高さで水平方向に探索し、予め定めた所定の範囲内の幅となるものを乗客と見なすことで乗客を検出可能である。また、死角領域演算部１０４は、４人の乗客８により発生した死角領域Ａ２の大きさ、位置、形状を演算する。また、図２では、実際には３人の乗客９がいるが、４人の乗客８が乗場検出装置５に対して死角領域Ａ２を形成しているため、３人の乗客９を検出できない状況となっている。   FIG. 2 is a diagram showing a landing situation in the present embodiment. A landing detection device 5 is installed at the landing. In this embodiment, it is assumed that the hall detection device 5 is a laser range sensor that detects a two-dimensional plane distribution in the horizontal direction within the hall. Based on a signal output from the landing detection device 5, the passenger detection unit 103 detects four passengers 8 from the detection area A1. For this detection, for example, with a laser range sensor, a passenger can be detected by searching in the horizontal direction at an average waist height and considering a passenger having a width within a predetermined range as a passenger. In addition, the blind spot area calculation unit 104 calculates the size, position, and shape of the blind spot area A2 generated by the four passengers 8. In FIG. 2, there are actually three passengers 9, but four passengers 8 form a blind spot area A <b> 2 with respect to the landing detection device 5, so that the three passengers 9 cannot be detected. It has become.

図３は死角領域内人数推定部１０５の動作を示すフローチャートである。死角領域内人数推定部１０５は、周期的、または割当て制御部１０６からの要求に応じて動作する。本推定は死角領域の推定ため、死角がない場合には動作させる必要はない。この場合、センサ４が検知した人数Ｐ１をホールにいる人数として、割当て制御部１０６はかごの割当を決定する。ステップＦＣ１０１で、死角領域Ａ２内の人数が推定済みか否かをチェックし、未推定の場合はステップＦＣ１０３へ進み、推定済みの場合はステップＦＣ１０２に進む。ステップＦＣ２で、死角領域内人数推定部１０５は、死角領域Ａ２が変化したか否かを検出して、変化した場合はステップＦＣ１０３へ進み、変化していない場合は、推定済みの死角領域Ａ２内の人数に変化無しとして終了する。ここで、死角領域の変化とは、周期的または割当て制御部１０６の要求に応じて複数回動作する時に、前回動作時における死角領域の位置・大きさ・形状が変化した場合や、死角領域へ流入または流出する乗客を検出した場合を、死角領域が変化したとみなす。次いで、ステップＦＣ１０３では、死角領域内人数推定部１０５は、乗客検出部１０３が検出した乗場の検出領域Ａ１内の乗客数をＰ１として取得する。次のステップＦＣ１０４では、死角領域内人数推定部１０５は死角領域Ａ２内に入る最大人数Ｐ２を算出する。ここで、Ｐ２の算出に当たっては、少なくとも死角領域Ａ２の大きさが考慮される。例として、死角領域Ａ２が２メートル四方の正方形で有る場合、死角領域の面積は４平方メートルとなる。乗客一人当たりの必要な面積を０．５平方メートルとすると、死角領域Ａ２内には、最大で８人の乗客が入るとして、Ｐ２＝８とする。   FIG. 3 is a flowchart showing the operation of the blind spot area number estimation unit 105. The blind spot area number estimation unit 105 operates periodically or in response to a request from the allocation control unit 106. Since this estimation is a blind spot area estimation, there is no need to operate when there is no blind spot. In this case, the allocation control unit 106 determines the allocation of the car, with the number of people P1 detected by the sensor 4 as the number of people in the hall. In step FC101, it is checked whether or not the number of people in the blind spot area A2 has been estimated. If it has not been estimated, the process proceeds to step FC103, and if it has been estimated, the process proceeds to step FC102. In step FC2, the blind spot area number estimation unit 105 detects whether or not the blind spot area A2 has changed. If changed, the process proceeds to step FC103. If not, the estimated area in the estimated blind spot area A2 is detected. Finish with no change in the number of people. Here, the change in the blind spot area is a case where the position, size, and shape of the blind spot area at the time of the previous operation changes when operating multiple times periodically or in response to a request from the assignment control unit 106, or to the blind spot area. When a passenger who flows in or out is detected, it is considered that the blind spot area has changed. Next, in step FC103, the blind spot area number estimation unit 105 acquires the number of passengers in the landing detection area A1 detected by the passenger detection unit 103 as P1. In the next step FC104, the blind spot area number estimation unit 105 calculates the maximum number P2 of persons within the blind spot area A2. Here, in calculating P2, at least the size of the blind spot area A2 is considered. As an example, when the blind spot area A2 is a 2 meter square, the area of the blind spot area is 4 square meters. Assuming that the required area per passenger is 0.5 square meters, P2 = 8, assuming that a maximum of 8 passengers will enter the blind spot area A2.

ここでは死角領域Ａ２の大きさのみを考慮したが、死角領域Ａ２の形状を考慮しても良い。この場合、死角領域Ａ２の大きさが同じであったとしても、死角領域Ａ２の形状に応じて適切なＰ２を算出することができる。また、一人当たりの必要な面積は、交通状況に応じて可変な値としても良い。例えば、出勤時間帯などの乗場が混雑する時間帯においては、乗客は互いの間隔詰めると考え、一人当たりの必要な面積を小さくし、閑散となる時間帯においては、乗客は互いの間隔を広げると考え、一人当たりの必要な面積を大きくしても良い。   Although only the size of the blind spot area A2 is considered here, the shape of the blind spot area A2 may be considered. In this case, even if the size of the blind spot area A2 is the same, an appropriate P2 can be calculated according to the shape of the blind spot area A2. Further, the necessary area per person may be a variable value according to traffic conditions. For example, in times when the halls are crowded, such as working hours, passengers think that the distance between each other is reduced, so that the area required per person is reduced, and in times when it is quiet, passengers increase the distance between each other. Therefore, the required area per person may be increased.

次いで、ステップＦＣ１０５で、死角領域内人数推定部１０５は、交通量予測部１０２から、日常の交通量に基づいた当該階の予測交通量を取得する。この予測交通量は、当該階に対して、上方向、下方向ともに乗客がいる場合は、両方向の予測交通量の合計を用いれば良く、どちらか一方の方向のみの乗客がいる場合は、当該方向の予測交通量のみを用いれば良い。   Next, in step FC105, the blind spot area number estimation unit 105 acquires the predicted traffic volume of the floor based on the daily traffic volume from the traffic volume prediction unit 102. The predicted traffic volume can be calculated by using the total predicted traffic volume in both directions if there are passengers in both the upward and downward directions, and if there are passengers in only one direction, Only the predicted traffic volume in the direction should be used.

次いで、ステップＦＣ１０６で、死角領域内人数推定部１０５は、エレベータの制御情報として、当該階で直前にかごが出発してからの経過時間を算出する。ステップＦＣ１０７では、死角領域内人数推定部１０５は、ステップＦＣ１０５で取得した当該階の予測交通量と、ステップＦＣ１０６で算出した直前にかごが出発してからの経過時間との積に基づいて、直前にかごが出発してからの予測発生人数Ｐ３を算出する。当該階に対して、上方向、下方向ともに乗客がいる場合，それぞれの方向に行くかごが出発してからの時間と、それぞれの方向の予測交通量をそれぞれ乗算し、それらを足し合わせることでＰ３を求めても良い。次いで、ステップＦＣ１０８では、死角領域内人数推定部１０５は、Ｐ１とＰ３の大きさを比較し、Ｐ１がＰ３より小さい時は、ステップＦＣ１０９へ進み、Ｐ１がＰ３以上の場合は、ステップＦＣ１１２で死角領域Ａ２内の人数としてＰ２と推定し終了する。これは、検出領域にいる人数が、当該階で日常発生し得る人数を超えているため、予測交通量より多い人数がホールにいるとして、死角領域Ａ２内の人数を、最大となるＰ２と見していることによる。これにより、突発的に多数の乗客が発生した場合には、普段の交通に基づく予測交通量にとらわれずに、死角領域の人数を推定することができる。   Next, in step FC106, the blind spot area number estimation unit 105 calculates the elapsed time since the last departure of the car on the floor as the elevator control information. In step FC107, the blind spot area number estimation unit 105 calculates the previous traffic volume based on the product of the predicted traffic volume of the floor acquired in step FC105 and the elapsed time from the departure of the car immediately before calculated in step FC106. Calculate the predicted number of people P3 since the car left. If there are passengers on the floor in both the upward and downward directions, multiply the time from the departure of the car in each direction by the estimated traffic volume in each direction and add them together. P3 may be obtained. Next, in step FC108, the blind spot area number estimation unit 105 compares the sizes of P1 and P3. If P1 is smaller than P3, the process proceeds to step FC109. If P1 is greater than or equal to P3, the blind spot is determined in step FC112. The number of people in area A2 is estimated as P2, and the process ends. This is because the number of people in the detection area exceeds the number of people who can occur on the floor on a daily basis. It depends on what you are doing. As a result, when a large number of passengers occur unexpectedly, the number of blind spots can be estimated without being constrained by the predicted traffic volume based on normal traffic.

Ｐ１がＰ３より小さくステップＦＣ１０９に進んだ場合、死角領域内人数推定部１０５は、Ｐ３とＰ１の差分と、Ｐ２を比較する。Ｐ２がＰ３とＰ１の差分より小さい時は、ステップＦＣ１１０で死角領域Ａ２内の人数としてＰ２を出力して終了する。これは、予想交通量より人がある程度少ない場合、死角エリアＡ２に人がいる期待値が高いと考え、Ａ２にＰ２程度人がいると考える。また、死角エリアＡ２が小さいとも考えられる。このため、最大人数Ｐ２として算出しても大きな誤差が生じる可能性は低い。Ｐ２がＰ３とＰ１の差分以上の場合、死角領域内人数推定部１０５はステップＦＣ１１１で死角領域Ａ２内の人数としてＰ３とＰ１の差分を出力する。この場合、発生人数Ｐ３と検出した乗客数Ｐ１に大きな差がないか、死角Ａ２の範囲大きく、実質的にセンシング出来てない事が想定される。このような場合、Ａ１にいる人数は予測通りＰ３程度であると推定する。そのため、死角Ａ２内にはＰ３とＰ１の差分程度の人数がいると推定する。上記の死角領域内人数推定部１０５動作により、死角領域Ａ２内の人数が推定されたため、これと乗客検出部１０３が検出した乗客数Ｐ１を足し合わせ、ホールにいる全乗客数として、割当て制御部１０６はかごの割当を決定する。   When P1 is smaller than P3 and the process proceeds to step FC109, the blind spot area number estimation unit 105 compares P2 with the difference between P3 and P1. When P2 is smaller than the difference between P3 and P1, in step FC110, P2 is output as the number of people in the blind spot area A2, and the process ends. This is because when the number of people is somewhat less than the expected traffic volume, it is considered that the expected value of people in the blind spot area A2 is high, and there are about P2 people in A2. It is also considered that the blind spot area A2 is small. For this reason, even if it is calculated as the maximum number of people P2, there is a low possibility that a large error will occur. When P2 is greater than or equal to the difference between P3 and P1, the blind spot area number estimation unit 105 outputs the difference between P3 and P1 as the number of persons in the blind spot area A2 in step FC111. In this case, it is assumed that there is no large difference between the number of people P3 generated and the number of detected passengers P1, or the range of the blind spot A2 is large and the sensing is not practically possible. In such a case, it is estimated that the number of people in A1 is about P3 as predicted. For this reason, it is estimated that there are as many people as the difference between P3 and P1 in the blind spot A2. Since the number of people in the blind spot area A2 is estimated by the operation of the blind spot area number estimation unit 105 described above, the number of passengers P1 detected by the passenger detection unit 103 is added and the number of passengers in the hall is assigned. 106 determines the allocation of the car.

上記では、かごへの乗車時に、乗客の積残しが発生しないことを仮定しているが、乗客の積残しが発生する場合は、直前に出発したかごが乗場に到着前の、乗場内の総乗客数の推定値から、直前に出発したかごへの乗車人数を引いたものを、Ｐ３に加えても良い。この場合かごへの乗車人数はかごに備えつけられる重量センサなどによって計測する事が出来る。このように、かごの乗降人数に基づいて、死角領域内の推定人数の補正が可能である。また上記では、ステップＦＣ１１２において、死角領域内の人数としてＰ２を出力したが、乗場内の総人数を予測交通量と近づけるため、死角領域Ａ２内の人数として０を出力しても良い。   In the above, it is assumed that there is no passenger leftover when boarding the car, but if there is a passenger leftover, the total number of passengers in the hall before the arrival of the car at the last landing A value obtained by subtracting the number of passengers in the car that has just left from the estimated value of the number of passengers may be added to P3. In this case, the number of passengers in the car can be measured by a weight sensor provided in the car. In this manner, the estimated number of people in the blind spot area can be corrected based on the number of people getting on and off the car. In the above description, P2 is output as the number of people in the blind spot area in step FC112. However, 0 may be output as the number of persons in the blind spot area A2 in order to bring the total number of people in the hall close to the predicted traffic volume.

本実施例では、日常の交通量に基づいて、死角領域内に存在する乗客を予測するため、乗場検出装置５から得られる二次元平面分布または三次元空間分布が同一であっても、日常の交通量に即して高精度に推定することができる。この結果、設置可能なセンサの数が構造やコストの問題で限られていても、乗場の乗客数に最適なかごを割当てたり、予約及び報知されたかごのみでは乗場の乗客全てを輸送できないと判断される場合に、追加でかごを割当てたりするなど、適切なかごの割当てを行うことが可能となる。   In the present embodiment, passengers existing in the blind spot area are predicted based on daily traffic, so even if the two-dimensional planar distribution or the three-dimensional spatial distribution obtained from the landing detection device 5 is the same, It can be estimated with high accuracy according to the traffic volume. As a result, even if the number of sensors that can be installed is limited due to structural and cost issues, it is necessary to allocate an optimal number of cars to the number of passengers at the hall, or to transport all passengers at the hall using only the reserved and informed cars. When the determination is made, it is possible to perform an appropriate car assignment such as an additional car assignment.

本実施例は、死角領域Ａ２内の人数の予測にあたり、エレベータの制御情報としてかごの乗場やかごの報知装置の報知情報に基づいて、より高精度に予測を行うものである。エレベータシステムの要部のブロック構成については、図１の構成に加え、さらに乗場情報記憶部１０７を備える。乗場情報記憶部１０７は、乗場におけるかご２Ａ、２Ｂ、２Ｃの出入り口の位置や、乗場検出装置５の位置、乗場への出入り口の位置、号機報知装置４の位置などを記憶している。また、死角領域内人数推定部１０５は、乗場情報記憶部１０７に記憶された情報も活用して死角領域内の人数を推定する。死角領域内人数推定部１０５は、乗客数検出部１０３により検出された乗場内の乗客数と、死角領域演算部１０４により演算された死角領域の位置及び大きさと、交通量予測部１０２により演算された死角領域の発生階の交通量と、ホール・かご情報管理部１０１により管理されるかご情報に加え、乗場情報記憶部１０７に記憶された乗場における各かごの出入り口の位置や、乗場検出装置５の位置、乗場への出入り口の位置、号機報知装置４の位置などに基づいて、死角領域内に存在する乗客数を推定する。   In the present embodiment, in predicting the number of people in the blind spot area A2, prediction is performed with higher accuracy on the basis of the information on the elevator hall and information on the notification device of the car as elevator control information. The block configuration of the main part of the elevator system is further provided with a hall information storage unit 107 in addition to the configuration of FIG. The hall information storage unit 107 stores the entrance / exit positions of the cars 2A, 2B, 2C at the hall, the position of the hall detection device 5, the position of the entrance / exit to the hall, the position of the machine notification device 4, and the like. In addition, the blind spot area number estimation unit 105 estimates the number of persons in the blind spot area using the information stored in the hall information storage unit 107. The blind spot area number estimating unit 105 calculates the number of passengers in the landing detected by the passenger number detecting unit 103, the position and size of the blind spot area calculated by the blind spot area calculating unit 104, and the traffic volume predicting unit 102. In addition to the traffic volume on the floor where the blind spot area is generated and the car information managed by the hall / car information management unit 101, the position of the entrance / exit of each car at the hall stored in the hall information storage unit 107 and the hall detection device 5 The number of passengers existing in the blind spot area is estimated on the basis of the position of the doorway, the position of the entrance to the landing, the position of the machine notification device 4, and the like.

図４は乗場に複数台のかごが有る場合の、号機報知装置による報知状況に応じた乗客の乗場位置を示す図である。一般的に、乗場に複数台のエレベータが有る場合、いずれのかごの号機報知装置も未報知である場合、乗場内の乗客は、複数台のかごの号機報知装置を見渡せる場所に位置することが多い。例えば、３台のかごが一列に並んでいる配置の場合、乗場の乗客は図５（ａ）に示すように号機報知装置４Ａ、４Ｂ、４Ｃが何れも未報知の状況では、乗客はかご出入り口から離れて待機する。号機報知装置４Ａ、４Ｂ、４Ｃの何れかが報知済みの状況では、図５（ｂ）に示すように、乗客は対応したかごの出入り口に近づいて待機しやすい特性がある。本実施例では、上記乗客の特性を活用して、より高精度に死角領域内の乗客数を推定する。   FIG. 4 is a diagram showing the passenger landing positions according to the notification status by the number machine notification device when there are a plurality of cars at the landing. In general, when there are multiple elevators at the landing, and when any of the car notification devices of any car is unreported, passengers in the landing may be located in a place where they can overlook the car notification devices of the plurality of cars. Many. For example, in the case of an arrangement in which three cars are arranged in a row, the passengers at the landing are in the state where all of the machine notification devices 4A, 4B, and 4C are not informed as shown in FIG. Wait away from. In the situation where any one of the machine notification devices 4A, 4B, 4C has been notified, as shown in FIG. 5 (b), there is a characteristic that the passenger tends to stand by approaching the entrance / exit of the corresponding car. In the present embodiment, the number of passengers in the blind spot area is estimated with higher accuracy by utilizing the characteristics of the passengers.

図５は本実施例における乗場の状況を示す図である。乗場には乗場検出装置５が設置されている。本実施例においては、乗場検出装置５が、乗場内の水平方向に関する二次元平面分布を検出するレーザー測域センサであるとする。乗場検出装置５の出力する信号に基づいて、乗客検出部１０３は検出領域Ａ１から４人の乗客８を検出する。また、死角領域演算部１０４は、４人の乗客８により発生した死角領域Ａ２の大きさ及び位置を演算する。また、２Ｂ号機に対応した号機報知装置４Ｂが報知しており、２Ｂ号機の予約、またはかごの到着を報知している状況となっている。従って、図４に示した乗客の特性から、死角領域演算部１０４は、死角領域Ａ２のうち、２Ａ号機の出入り口付近の死角領域内に乗客が存在する可能性は低いとして、無効領域Ａ３を演算する。また図５では、また、２人の乗客９がいるが、４人の乗客８が乗場検出装置５に対して死角領域Ａ２を形成するため、２人の乗客９を検出できない状況となっている。   FIG. 5 is a diagram showing a landing situation in the present embodiment. A landing detection device 5 is installed at the landing. In the present embodiment, it is assumed that the hall detection device 5 is a laser range sensor that detects a two-dimensional plane distribution in the horizontal direction in the hall. Based on a signal output from the landing detection device 5, the passenger detection unit 103 detects four passengers 8 from the detection area A1. Also, the blind spot area calculation unit 104 calculates the size and position of the blind spot area A2 generated by the four passengers 8. In addition, the machine notification device 4B corresponding to the No. 2B machine is informing, and the reservation of the No. 2B machine or the arrival of the car is being reported. Therefore, from the characteristics of the passenger shown in FIG. 4, the blind spot area calculation unit 104 calculates the invalid area A3, assuming that there is a low possibility that a passenger is present in the blind spot area near the entrance / exit of Unit 2A in the blind spot area A2. To do. In FIG. 5, there are also two passengers 9, but the four passengers 8 form a blind spot area A <b> 2 with respect to the landing detection device 5, so that the two passengers 9 cannot be detected. .

実質的な死角領域は、Ａ２の領域からＡ３の領域を除いた領域となり狭められ、この領域に対して、図３で示したフローチャートにより、死角領域内の人数を推定する。   The substantial blind spot area is narrowed to be an area obtained by removing the A3 area from the A2 area, and the number of people in the blind spot area is estimated based on the flowchart shown in FIG.

次に、図５と同様に乗客８が乗場検出装置５に対して死角領域Ａ２を形成しており、さらに、号機報知装置４Ｃが報知している場合について述べる。乗場検出装置５の出力する信号に基づいて、乗客検出部１０３は検出領域Ａ１から４人の乗客８を検出する。また、死角領域演算部１０４は、４人の乗客８により発生した死角領域Ａ２の大きさ及び位置を演算する。また、２Ｃ号機に対応した号機報知装置４Ｃが報知しており、２Ｃ号機の予約、またはかごの到着を報知している状況となっている。図４に示した乗客の特性から、死角領域演算部１０４は、死角領域Ａ２のうち、２Ａ号機及び２Ｂの出入り口付近の死角領域内に乗客が存在する可能性は低いとして、死角領域Ａ２全てが無効領域Ａ３で有るとして演算する。この場合死角領域Ａ２内の乗客数を０人と推定する。   Next, a case where the passenger 8 forms a blind spot area A2 with respect to the landing detection device 5 as in FIG. Based on a signal output from the landing detection device 5, the passenger detection unit 103 detects four passengers 8 from the detection area A1. Also, the blind spot area calculation unit 104 calculates the size and position of the blind spot area A2 generated by the four passengers 8. In addition, the machine notification device 4C corresponding to the No. 2C machine is informing, and the reservation of the No. 2C machine or the arrival of the car is informed. From the characteristics of the passengers shown in FIG. 4, the blind spot area calculation unit 104 assumes that there is a low possibility that passengers are present in the blind spot area in the vicinity of the entrance and exit of Unit 2A and 2B in the blind spot area A2. The calculation is performed assuming that the area is the invalid area A3. In this case, the number of passengers in the blind spot area A2 is estimated to be zero.

本実施例では、日常の交通量に基づいて、死角領域内に存在する乗客を予測するにあたって、号機報知装置による報知の状況を考慮して、乗客のいる可能性のある死角領域を調整しているため、報知状況に即してより高精度に推定することができ、この結果、適切なかごの割当て行うことが可能となる。なお、本実施例ではＡ３の領域を無効領域としたが、Ａ２より人がいる可能性が低い領域、つまり、面積当たりの最大人数がＡ２より少ない領域として扱い、図３のフローに従いＡ３の領域にいる人数を推定してもよい。   In this embodiment, when predicting passengers existing in the blind spot area based on daily traffic, the blind spot area where passengers may be present is adjusted in consideration of the situation of notification by the machine notification device. Therefore, it is possible to estimate with higher accuracy in accordance with the notification situation, and as a result, it becomes possible to perform appropriate car assignment. In the present embodiment, the area A3 is set as an invalid area. However, the area A3 is less likely to have people than A2, that is, the area having the maximum number of people per area is smaller than A2, and the area A3 according to the flow of FIG. You may estimate the number of people in

本実施例は、センサから得られる３次元空間情報を用いて乗場の乗客数をさらに高精度に予測を行うものである。   In the present embodiment, the number of passengers at the hall is predicted with higher accuracy using the three-dimensional spatial information obtained from the sensor.

図６は三次元空間を検出するセンサを用いる計算例の説明図である。検出領域Ａ１内に乗客８を検出しており、この乗客８により、死角領域Ａ２が発生している。三次元空間を検出する場合は、死角領域Ａ２の水平面上の各位置における高さを計算することができる。
乗場内の三次元空間を検出する乗場検出装置５は、高さＨ１の乗場天井からＨ２の高さに俯角θ１で据え付けられている。このＨ２は短くし、天井に近い位置に乗場検出装置５を備え付けた方がより望ましい。これは、死角の発生面積をなるべく小さくするためである。
乗場検出装置５から得られるデータ、または乗場検出装置５から得られるデータの変換により、乗場検出装置５から乗客８の最頂部までの距離がＲ１で、乗場検出装置５の対する乗客８の最頂部の垂直方向の角度がθ２であることを検出している。このとき、また、乗客８の乗場検出装置５からの水平方向の距離Ｄ１は、次式で算出できる。
Ｄ１ ＝ Ｒ１×ｃｏｓ（θ１−θ２）・・・・・・・・・・・・・・・・・（式１）
上記より、図６において乗客８と乗客９水平方向の距離をＤ２とすると、乗客９の許容される高さＨ３は、次式で算出できる。
Ｈ３ ＝ Ｈ１−Ｈ２−（Ｄ１＋Ｄ２）×ｔａｎ（θ１−θ２）・・・・・・（式２）
上記のように、死角領域内の水平方向の位置に応じて、乗客の高さを算出することができる。 FIG. 6 is an explanatory diagram of a calculation example using a sensor for detecting a three-dimensional space. A passenger 8 is detected in the detection area A1, and a blind spot area A2 is generated by the passenger 8. When detecting a three-dimensional space, the height at each position on the horizontal plane of the blind spot area A2 can be calculated.
The hall detection device 5 for detecting the three-dimensional space in the hall is installed at a depression angle θ1 from the hall ceiling at the height H1 to the height H2. It is more desirable to shorten this H2 and to provide the hall detector 5 at a position close to the ceiling. This is to make the blind spot generation area as small as possible.
By the conversion of the data obtained from the landing detection device 5 or the data obtained from the landing detection device 5, the distance from the landing detection device 5 to the top of the passenger 8 is R1, and the top of the passenger 8 with respect to the landing detection device 5 It is detected that the angle in the vertical direction is θ2. At this time, the horizontal distance D1 of the passenger 8 from the landing detection device 5 can be calculated by the following equation.
D1 = R1 × cos (θ1-θ2) (Equation 1)
From the above, assuming that the distance in the horizontal direction of the passenger 8 and the passenger 9 in FIG. 6 is D2, the allowable height H3 of the passenger 9 can be calculated by the following equation.
H3 = H1-H2- (D1 + D2) × tan (θ1-θ2) (Equation 2)
As described above, the height of the passenger can be calculated according to the horizontal position in the blind spot area.

算出した死角領域Ａ２に存在し得る乗客の高さと、学校やオフィスなど、ビルの用途に応じた想定される乗客の身長とを比較することにより、死角領域Ａ２内の乗客が存在し得る領域を制限できるため、より死角領域内の乗客数の推定精度の高精度化を図ることができる。例えば、オフィスビルの乗場に発生した死角領域Ａ２のある位置において、その位置に存在し得る乗客の高さが１３０ｃｍ程度であると算出された場合、オフィスビルで想定される身長よりも低いため、その位置に乗客が存在する可能性が低いとして、その位置に対応する領域を無効領域Ａ３とする。一方で、同様の状況が小学校やファミリー向けマンション等で発生した場合は、その位置に乗客が存在する可能性が高いとし、無効領域Ａ３としない。   By comparing the calculated height of passengers that can exist in the blind spot area A2 with the height of passengers that are assumed to be used for buildings such as schools and offices, the area in which the passengers in the blind spot area A2 can exist is calculated. Since it can restrict | limit, the high precision of the estimation precision of the number of passengers in a blind spot area | region can be achieved more. For example, in a position where there is a blind spot area A2 generated at the landing of an office building, when the height of a passenger who can exist at that position is calculated to be about 130 cm, because it is lower than the assumed height of the office building, Assuming that there is a low possibility that a passenger is present at that position, the area corresponding to that position is designated as an invalid area A3. On the other hand, when the same situation occurs in an elementary school, a family apartment, etc., it is assumed that there is a high possibility that a passenger exists at that position, and the invalid area A3 is not set.

本実施例では、日常の交通量に基づいて、死角領域内に存在する乗客を予測するにあたって、乗場検出装置から得られる三次元空間情報から、死角領域の各位置における高さと、乗客の想定される身長の比較から、死角領域を調整しているため、より高精度に死角領域内の乗客数を推定することができ、この結果、適切なかごの割当て行うことが可能となる。   In this embodiment, when predicting passengers existing in the blind spot area based on daily traffic, the height at each position in the blind spot area and the passengers are assumed from the three-dimensional spatial information obtained from the landing detection device. Since the blind spot area is adjusted from the comparison of the heights, the number of passengers in the blind spot area can be estimated with higher accuracy, and as a result, an appropriate car can be allocated.

また、発明の実施形態として死角領域が単数の場合について説明したが、死角領域が複数ある場合にも適用可能である。この場合、２つ目以降の死角領域の人数を推定する際には、図３のステップＦＣ１０７の直前にかごが出発してからの発生人数Ｐ３から、それ以前に推定した死角領域の人数の合計を減じたをＰ３として扱う。この様に扱う事で、複数の死角が存在存在しても、その死角内の乗客数を推定する事が出来、適切なかごの割当て行うことが可能となる。   Moreover, although the case where there was a single blind spot area has been described as an embodiment of the invention, the present invention can also be applied when there are a plurality of blind spot areas. In this case, when estimating the number of people in the blind spot area after the second, the total number of people in the blind spot area estimated before that from the number of people P3 generated since the car left immediately before step FC107 in FIG. Is treated as P3. By handling in this way, even if there are a plurality of blind spots, the number of passengers within the blind spots can be estimated, and an appropriate car can be assigned.

本発明の各実施例においては、乗場内の二次元平面分布または三次元空間分布を検出するためのセンサとして、レーザー測域センサを利用した場合を例にとったが、センサは温度センサやカメラなど、乗場内の二次元平面分布または三次元空間分布を検出できるものであれば本発明を同様に適用できる。   In each embodiment of the present invention, a laser range sensor is used as an example of a sensor for detecting a two-dimensional planar distribution or a three-dimensional spatial distribution in the hall. However, the sensor may be a temperature sensor or a camera. The present invention can be similarly applied as long as it can detect a two-dimensional planar distribution or a three-dimensional spatial distribution in the hall.

なお、本発明は上記した実施例に限定されるものではなく、さまざまな変形例が含まれる。例えば、上記した実施例は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに限定されるものではない。また、ある実施例の構成の一部を他の実施例の構成に置き換えることが可能であり、また、ある実施例の構成に他の実施例の構成を加えることも可能である。また、各実施例の構成の一部について、他の構成の追加・削除・置換をすることが可能である。また、上記の各構成、機能、演算部、推定部等は、それらの一部または全部を、例えば集積回路で設計するなどによりハードウェアで実現しても良い。また、上記の各構成、機能などは、プロセッサがそれぞれの機能を実現するプログラムを解釈し、実行することによりソフトウェアで実現しても良い。各機能を実現するプログラム、テーブル、ファイル等の情報は、メモリや、ハードディスク、ＳＳＤ（Ｓｏｌｉｄ Ｓｔａｔｅ Ｄｒｉｖｅ）等の記録装置、または、ＩＣカード、ＳＤカード、ＤＶＤ等の記録媒体に置くことができる。   In addition, this invention is not limited to an above-described Example, Various modifications are included. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment. Each of the above-described configurations, functions, calculation units, estimation units, and the like may be realized by hardware by designing a part or all of them with, for example, an integrated circuit. Further, each of the above-described configurations, functions, and the like may be realized by software by the processor interpreting and executing a program that realizes each function. Information such as programs, tables, and files for realizing each function can be stored in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.

１ 群管理制御装置、５ 乗場検出装置、１０１ ホール・かご情報管理部、１０２ 交通量予測部、１０３ 乗客検出部、１０４ 死角領域演算部、１０５ 死角領域内人数推定部、１０６ 割当て制御部 1 group management control device, 5 hall detection device, 101 hall / car information management unit, 102 traffic volume prediction unit, 103 passenger detection unit, 104 blind spot area calculation unit, 105 blind spot area number estimation unit, 106 allocation control part

Claims (11)

エレベータ乗場内の乗客数及び乗客の位置を検出するセンサを備えたエレベータシステムであって、
前記センサの死角領域内の乗客数を、エレベータの制御情報に基づいて推定する死角領域乗客数推定部を備えるエレベータシステム。 An elevator system comprising a sensor for detecting the number of passengers in the elevator hall and the position of the passengers,
An elevator system including a blind spot area passenger number estimation unit that estimates the number of passengers in the blind spot area of the sensor based on control information of the elevator. 請求項１記載のエレベータシステムであって、
エレベータシステムの交通量を予測する交通量予測部を更に備え、
前記死角領域乗客数推定部は、予測された前記交通量に基づいて
前記死角領域内の乗客数を推定するエレベータシステム。 The elevator system according to claim 1,
A traffic volume prediction unit for predicting the traffic volume of the elevator system;
The blind spot area passenger number estimating unit estimates the number of passengers in the blind spot area based on the predicted traffic volume. 請求項２記載のエレベータシステムであって、
前記エレベータの制御情報は前記エレベータ乗り場からかごが出発してからの時間を含み、
前記予測交通量と直前にかごが出発してからの時間との積に基づいて前記死角領域内の乗客数を推定するエレベータシステム。 The elevator system according to claim 2,
The elevator control information includes the time since the car departed from the elevator platform,
The elevator system which estimates the number of passengers in the blind spot area based on the product of the predicted traffic volume and the time since the last departure of the car. 請求項３記載のエレベータシステムであって、
前記交通量予測部は、エレベータの運行情報を記憶し、
記憶された前記エレベータの運行情報に基づき前記交通量を予測する、
エレベータシステム。 The elevator system according to claim 3,
The traffic volume prediction unit stores elevator operation information,
Predicting the traffic volume based on the stored operation information of the elevator;
Elevator system. 請求項４記載のエレベータシステムであって、
前記交通量予測部は、前記記憶されたエレベータの運行情報のうち、
現在のエレベータの運行状況又は運行時間帯の類似度の高い運行情報に基づいて、 The elevator system according to claim 4,
The traffic volume prediction unit includes the stored elevator operation information.
Based on the current elevator operation status or operation information with high similarity in operation time zone, 請求項１記載のエレベータシステムであって、
前記死角領域乗客数推定部は、前記死角領域内の乗客数を前記死角領域の面積と乗客一人当たりに必要な面積に基づいて推定するエレベータシステム。 The elevator system according to claim 1,
The blind spot area passenger number estimation unit estimates the number of passengers in the blind spot area based on the area of the blind spot area and the area required per passenger. 請求項６記載のエレベータシステムであって、
エレベータ混雑時は閑散事よりも前記乗客一人当たりに必要な面積小さく設定する、
エレベータシステム The elevator system according to claim 6, wherein
When the elevator is congested, the area required per passenger is set to be smaller than that of a quiet event.
Elevator system 請求項１記載のエレベータシステムであって、
複数のかごと、前記エレベータ乗場の各かごの乗降口に設置され前記各かごの状況の報知を行う複数の号機報知装置と、を備え、
前記エレベータの制御情報には前記かご報知状況の情報を含み、
前記死角領域乗客数推定部は、前記センサの死角領域内の乗客数を、前記号機報知装置の報知の有無に基づいて推定する
エレベータシステム。 The elevator system according to claim 1,
A plurality of cars, and a plurality of number machine notification devices installed at the entrance of each car of the elevator hall to notify the status of each car,
The elevator control information includes information on the car notification status,
The blind spot area passenger number estimation unit is an elevator system that estimates the number of passengers in the blind spot area of the sensor based on the presence / absence of notification of the number machine notification device. 請求項８記載のエレベータシステムであって、
前記死角領域乗客数推定部は、前記センサの死角領域のうち前記号機報知装置により報知されていない前記かごの出入り口前の領域は、乗客がいないと推定するエレベータシステム。 The elevator system according to claim 8, wherein
The blind spot area passenger number estimation unit estimates that there is no passenger in an area before the entrance of the car that is not informed by the car notification device in the blind spot area of the sensor. 請求項１に記載のエレベータシステムであって、
前記センサは３次元分布を測定できるセンサであり、
前記センサは前記エレベータ乗り場の天井近くに設置され、
前記死角領域乗客数推定部は、前記センサから得られる三次元空間分布に基づいて、前記死角領域内の各位置に存在し得る乗客の身長の最大値を求め、
前記身長の最大値と、当該エレベータシステムのの乗客の想定身長との比較し、
前記身長の最大値が当該エレベータシステムの乗客の想定身長より低い場合、
前記死角領域には乗客がいないと推定するエレベータシステム。 The elevator system according to claim 1,
The sensor is a sensor capable of measuring a three-dimensional distribution,
The sensor is installed near the ceiling of the elevator hall,
The blind spot area passenger number estimation unit obtains the maximum value of the height of a passenger that can exist at each position in the blind spot area based on the three-dimensional spatial distribution obtained from the sensor,
Comparing the maximum height with the assumed height of the passenger of the elevator system;
If the maximum height is lower than the expected height of the passenger of the elevator system,
An elevator system that estimates that there are no passengers in the blind spot area. 請求項１から１０いずれかに記載のエレベータシステムであって、
前記推定された死角領域の乗客数及び前記センサで検出した乗客数に基づいてかごの割り当てを決定する割当て制御部を備える、
エレベータシステム。 The elevator system according to any one of claims 1 to 10,
An allocation control unit that determines the allocation of a car based on the estimated number of passengers in the blind spot area and the number of passengers detected by the sensor;
Elevator system.
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