EP3512791A1 - Procédé de détection de l'entrée d'un passager dans une cabine d'ascenseur d'un système d'ascenseur - Google Patents

Procédé de détection de l'entrée d'un passager dans une cabine d'ascenseur d'un système d'ascenseur

Info

Publication number
EP3512791A1
EP3512791A1 EP17758571.8A EP17758571A EP3512791A1 EP 3512791 A1 EP3512791 A1 EP 3512791A1 EP 17758571 A EP17758571 A EP 17758571A EP 3512791 A1 EP3512791 A1 EP 3512791A1
Authority
EP
European Patent Office
Prior art keywords
elevator car
passenger
measured values
pattern
mobile terminal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP17758571.8A
Other languages
German (de)
English (en)
Other versions
EP3512791B1 (fr
Inventor
Christian Studer
Martin KUSSEROW
Reto Tschuppert
Zack ZHU
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Inventio AG
Original Assignee
Inventio AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
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Application filed by Inventio AG filed Critical Inventio AG
Priority to PL17758571T priority Critical patent/PL3512791T3/pl
Publication of EP3512791A1 publication Critical patent/EP3512791A1/fr
Application granted granted Critical
Publication of EP3512791B1 publication Critical patent/EP3512791B1/fr
Revoked legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • B66B1/3492—Position or motion detectors or driving means for the detector
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/0006—Monitoring devices or performance analysers
    • B66B5/0012—Devices monitoring the users of the elevator system
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/0006—Monitoring devices or performance analysers
    • B66B5/0037—Performance analysers

Definitions

  • the invention relates to a method for detecting an entry of an elevator car of an elevator installation by a passenger according to the preamble of claim 1.
  • WO 2013/130040 A1 describes a method for monitoring the use of an elevator installation.
  • the passengers of the elevator system are equipped with marking devices, so-called tags.
  • tags At landing doors or in
  • Lift cabins of the elevator system are mounted readers, which can detect if and if so, which day is in their vicinity. This can also be recognized when a passenger enters an elevator car.
  • the readers forward the information to a traffic evaluation unit based on this
  • US 201/4330535 A1 describes a method for detecting a movement of a passenger in an elevator car. The process involves a series of
  • Acceleration measurements evaluated to detect a start and an end of a ride of the elevator car are not suitable for detecting an entry of an elevator car of an elevator installation by a passenger.
  • the terminal has at least one, in particular but several sensors with which the mobile terminal records and evaluates measured values. The recognition of the entry of the elevator car is then based on the above measured values.
  • a "detection of an entry of an elevator car of an elevator installation by a passenger” means that the time of entry of the elevator car is detected. Entering the elevator car and thus the time of entry is preceded in time by a movement of the passenger in an elevator car or a movement and thus an acceleration of the passenger and the elevator car in the vertical direction. From the detection of a movement or acceleration of the passenger and the elevator car in the vertical direction can not be closed to the time of entering the elevator car. The time between entering the elevator car and starting to drive the passenger in the elevator car may be several seconds or several minutes.
  • Elevator cabin to be further evaluated.
  • the erfmdungsgemtreu method is thus inexpensive to carry out.
  • the information that a passenger with a mobile terminal enters an elevator car can be evaluated in a variety of ways or used further, or initiate a wide variety of actions.
  • the terminal may, for example, in particular pass the information wirelessly to a traffic evaluation unit, which is then comparable to the traffic evaluation unit of FIG.
  • WO 2013/130040 AI can analyze a traffic flow in the elevator system.
  • the terminal can also be brought into a predetermined mode, for example, for example, a specific program, launched a so-called app or the app are brought into a predetermined state.
  • a predetermined mode for example, for example, a specific program, launched a so-called app or the app are brought into a predetermined state.
  • an app can be started which displays certain contents or a game can be started, which allows an interaction with other passengers in the elevator car.
  • the terminal is to record with its sensors measured variables during the upcoming elevator ride, which are to be evaluated for monitoring the elevator system. As soon as an entry into an elevator car is detected, the terminal can be brought into a measuring mode and thus prepared for a measurement.
  • an exit from an elevator car can be detected.
  • the departure is basically the other way around as the entry of an elevator car.
  • the evaluation of the detected data and thus the recognition of an entry of the elevator car is carried out in particular by the mobile terminal.
  • the acquired data is transmitted to an evaluation device and the detection of an entry of the elevator car is carried out by the evaluation device.
  • the evaluation of the data by the terminal is limited to the forwarding of the data to the off-value device.
  • at least part of the evaluation is carried out both by the mobile terminal and by the evaluation device.
  • the mobile terminal can be embodied, for example, as a mobile phone, a smartphone, a tablet computer, a smartwatch, a so-called wearable, for example in the form of an electronic, smart textile or as another portable terminal.
  • the sensor of the mobile terminal may be embodied, for example, as a microphone, an acceleration sensor, a rotation rate sensor, a magnetic field sensor, a camera, a barometer, a brightness sensor, an air humidity sensor or a carbon dioxide sensor.
  • the acceleration, rotation rate and magnetic field sensors are designed in particular as so-called three-dimensional or 3D sensors. Such sensors provide three measured values in the x, y and z directions, the x, y and z directions being arranged perpendicular to one another.
  • the terminal has several and in particular different types of sensors, for example via a microphone, a three-dimensional acceleration sensor, a three-dimensional rotation rate sensor and a three-dimensional magnetic field sensor.
  • acceleration, rotation rate and magnetic field sensors are understood to mean three-dimensional acceleration, rotation rate and magnetic field sensors.
  • the passenger may carry the terminal in completely different orientations, so that in the first approach it is not clear how the acceleration, yaw rate or magnetic field sensors are aligned in space.
  • the vertical direction ie the absolute z-direction
  • the measured values of the acceleration and rotation rate and magnetic field sensors can be converted into values that are aligned along the absolute z direction and absolute x and y directions.
  • the absolute x, y and z directions are each arranged perpendicular to each other.
  • the mobile terminal detects measured values with the sensor (s) or movements of the passenger and evaluates them.
  • the measured values mentioned are, in particular, accelerations, that is to say transverse accelerations and / or yaw rates, wherein in each case three accelerations and / or yaw rates in the x, y and z direction are measured in each case. From the movements of the passenger characterizing measured values can be concluded on the movements of the passenger and from the movements of the passenger can be seen that the passenger enters an elevator car. In principle, it is assumed that the passenger carries the terminal with him so that the measured values measured by the terminal not only characterize the movements of the terminal but also of the passenger.
  • a movement pattern of the passenger is derived from the measured values and compared with at least one stored signal pattern.
  • the recognition of the entry of the elevator car is then based on the aforementioned comparison. This can be detected particularly reliably entering a elevator car.
  • the named stored signal patterns are in this case movement patterns.
  • a sequence of movements is to be understood as meaning, for example, a chronological sequence, in particular of accelerations, or
  • a movement pattern can also be described with a so-called feature or in particular a plurality of features.
  • Such features may, for example, be statistical parameters such as mean values,
  • a movement pattern in this case may also be referred to as a so-called feature vector.
  • the features mentioned can be determined, in particular, for individual time segments, in which case particular values or gradients of individual measured values are formed. For example, such a temporal section may be characterized in that the passenger does not move, so he waits, for example, in front of the shaft door.
  • not only a single acceleration or yaw rate is considered, but the combination of several accelerations and / or yaw rates, in particular of three accelerations and yaw rates.
  • a stored signal pattern can, for example, characteristic courses of accelerations, rotation rates and / or magnetic fields or features when walking a person to a landing door, waiting in front of the shaft door until the elevator car is available and access is possible, entering the elevator car and turning towards the car door contain.
  • the signal patterns can be generated by specialists on the basis of their experience or in particular determined by one or more experiments. In particular, methods of so-called machine learning are used to detect or classify movement patterns.
  • a support vector machine For example, a support vector machine, a random forest algorithm or a deep learning algorithm can be used.
  • Classification procedures must first be trained.
  • typical movement patterns in particular based on the mentioned features, are generated in attempts to enter an elevator car and are added to the said algorithms for training Provided.
  • the algorithms After the algorithms have been trained with a sufficient number of training patterns, they can decide whether or not an unknown movement pattern marks entering an elevator car. In this case, the signal pattern is stored in the parameters of the algorithm.
  • the generation of the typical movement patterns for the training can be performed by a passenger who uses the mobile terminal in daily use. He only has to mark the beginning and the end of the entry into the elevator car. It is also possible that, after completion of the actual training, the passenger gives a feedback as to whether an entry into an elevator car was not recognized or a wrong entry of an elevator car was detected. These feedbacks can be used to further train the algorithm.
  • the measured movement pattern is not limited to one
  • the mobile terminal detects measured values indicative of the activities of the elevator installation or sensors and evaluates these.
  • Activities of the elevator installation are understood here to mean, for example, movements of individual components of the elevator installation, such as movements of the elevator car, a landing door, a car door or an activation of a door drive.
  • the terminal detects noises and / or magnetic fields, wherein in particular three magnetic fields in the x, y and z directions are measured.
  • the changes in the measured magnetic fields can be caused, for example, by the activity of the door drive having an electric motor and / or by the cabin door and / or shaft door having the ferromagnetic material. For example, it can be concluded from the measured values mentioned that the car door of an elevator car has opened in front of a passenger and closed behind him.
  • an activity pattern of the elevator system is derived from the measured values and compared with at least one stored signal pattern. The recognition of the entry of the elevator car is then based on the mentioned comparison. This can be particularly reliable entering a
  • Elevator cab be detected.
  • the named stored signal patterns are activity patterns in this case.
  • an activity pattern should be understood to mean, for example, a chronological sequence, in particular of measured noises and / or magnetic fields.
  • An activity pattern can also be described with a feature described in connection with movement patterns or, in particular, with a plurality of features. In particular, not only a single measurement of a magnetic field in one direction is considered, but the combination of several measurements of magnetic fields in several, in particular three directions.
  • a signal pattern may, for example, describe a noise of a car door when opening or a noise when the elevator car enters a floor or features derived therefrom.
  • the signal patterns can be generated by specialists on the basis of their experience or in particular determined by one or more experiments. For the determination of the signal patterns, in particular methods of so-called machine learning, analogous to the above description, can be used in connection with motion patterns.
  • the signal patterns can also be divided into temporal sections and individual features can be determined for each section.
  • the measured activity pattern is compared in particular not only with a signal pattern but with a whole series of slightly different signal patterns.
  • the mobile terminal detects with the sensor
  • Characteristics of the environment of the mobile terminal characteristic measurements and evaluates them. It can, for example, magnetic fields, the air pressure, the
  • a property pattern of the elevator installation is derived from the measured values and compared with at least one stored signal pattern. The recognition of the entry of the elevator car is then based on the mentioned comparison. This can be particularly reliable entering a
  • Elevator cab be detected.
  • the named stored signal patterns are in this case property patterns.
  • a property pattern should be understood as meaning, for example, a chronological sequence of measured values which describe the surroundings of the terminal, ie in this case properties of the elevator installation.
  • a property pattern can also be described with a feature described in connection with movement patterns or in particular a plurality of features. In particular, not only the course of a single measurement of one of the mentioned properties is considered, but the combination of several measurements.
  • a signal pattern may, for example, describe the change in the magnetic field from outside to inside the elevator car or features derived therefrom. Changes in the magnetic field, for example, by different use
  • the ferromagnetic materials may themselves generate a magnetic field and / or affect the earth's magnetic field.
  • a signal pattern may describe the change in C02 content of the air from outside to inside the elevator car or features derived therefrom.
  • the CO 2 content of the air rises through the air exhaled by the passengers in the closed elevator cabin.
  • the CO 2 content of the air in the cabin is higher than outside.
  • the C02 content increases slowly while driving, with which a ride in an elevator car can be detected. Although this increase is a rather slow process, it can be detected on longer trips.
  • a signal pattern may describe the change in humidity from outside to inside the elevator car or features derived therefrom. This rises slowly, analogously to the CO 2 content within the cabin due to the exhaled air, so that the evaluation can proceed analogously to the CO 2 content.
  • a signal pattern may describe the change in temperature from outside to inside the elevator car or features derived therefrom. Due to the heat emitted by the passengers, the temperature rises slowly so that the evaluation can proceed analogously to the C02 content.
  • a signal pattern may describe the change in brightness from outside to inside the elevator car or features derived therefrom. Inside an elevator car, it is usually less bright than outside.
  • a signal pattern may describe the change in acoustics from outside to inside the elevator car or features derived therefrom. Since an elevator car is a comparatively narrow, enclosed space, the echo or the sound attenuation, for example, changes. In particular, special test signals can be used to determine this change.
  • the signal patterns can be generated by specialists on the basis of their experience or in particular determined by one or more experiments. To determine the signal pattern can analogous to the above description in connection with
  • Movement patterns in particular methods of so-called machine learning applied
  • the signal patterns can also be divided into temporal gates and features for each section individually.
  • a travel in an elevator car is detected from the measured values measured by at least one of the sensors of the mobile terminal.
  • movement, activity and / or property patterns detected before the journey are compared with stored signal patterns and the stored signal patterns are adjusted on the basis of the comparison.
  • the stored signal patterns are changed in the direction of the movement, activity and / or property patterns acquired before the drive.
  • the above-described methods of so-called machine learning can be used. For a particularly effective learning and thus a particularly accurate detection of entering an elevator car by a passenger is possible.
  • a departure from the elevator car can also be detected with a very high probability of being hit.
  • the passenger moves significantly transversely to the vertical direction, that is to say either in the x and / or y direction, it can be assumed that he will leave the elevator car.
  • This movement can be detected, for example, by means of the acceleration sensor.
  • the above-described resulting vector of the accelerations in the x, y and z directions can also be used.
  • the elevator car is first accelerated up or down, then usually drives a while with a quasi-constant
  • Acceleration progress can be detected with high accuracy in the measured values of one or more accelerometer sensors of the mobile terminal. In this way, a secure detection of a ride of the passenger and thus the mobile terminal in an elevator car is possible. Based on this secure detection is a reliable adaptation of the stored signal pattern possible, which finally leads to a particularly secure detection of boarding a passenger in one
  • Elevator cabin leads.
  • the air pressure measured by a barometer can also be evaluated to detect a journey in an elevator car.
  • the gradient of the change in terms of amount is significantly greater than when climbing stairs or weather-related changes in air pressure.
  • Fig. 1 is a very schematic representation of an elevator system with a
  • Fig. 3a, b, c temporal course of magnetic field strengths when boarding a passenger in an elevator car
  • Fig. 4 shows a time course of an acceleration in the vertical direction when driving an elevator car.
  • an elevator installation 10 has an elevator car 11, which can be moved up and down in an elevator shaft 12 in the vertical direction 13.
  • the elevator car 11 is connected via a flexible support means 14 and a drive roller 15 of a drive not shown with a counterweight 16.
  • the drive can via the drive roller 15 and the support means 14, the elevator car 11 and the
  • the elevator shaft 12 has three shaft openings 17a, 17b, 17c and thus three floors, which are closed with shaft doors 18a, 18b, 18c.
  • the elevator car 11 is located at the shaft opening 17a, ie in the lowest floor.
  • the corresponding shaft door 18a, 18b, 18c can be opened together with a car door 19 and thus allowing the elevator car 11 to enter.
  • To open the cabin door 19 and the corresponding shaft door 18a, 18b, 18c not shown door segments are pushed laterally, so that a displacement of the door segments takes place to the side.
  • the car door 19 and the corresponding landing door 18a, 18b, 18c are actuated by a door drive 20, which is controlled by a door control unit 21.
  • the door control unit 21 is in signal communication with an elevator control unit 22 which controls the entire elevator installation 10.
  • the elevator control unit 22 controls, for example, the drive and can thus move the elevator car 11 to a desired floor. It can also, for example, send the door control unit 21 a request to open the car door 19 and the corresponding landing door 18a, 18b, 18c, which then executes the door control unit 21 by means of a corresponding activation of the door drive 20.
  • a passenger 23 which carries a mobile terminal in the form of a mobile phone 24 with it.
  • the mobile phone 24 has a plurality of sensors, of which only a microphone 25 is shown.
  • the mobile telephone 24 also has in each case three-dimensional acceleration, yaw rate and magnetic field sensors which can acquire measured values in the x, y and z directions.
  • the measured values acquired by the acceleration, yaw rate and magnetic field sensors can easily be converted into values with respect to absolute x, y and z directions. All the following statements on accelerations, rotation rates or magnetic field strengths thus refer to measured values converted in this way and statements on x, y and z directions in absolute x, y and z directions.
  • the mobile telephone 24 continuously records measured values and evaluates them.
  • the mobile telephone 24 detects, for example, the rotation rates about the x-, y- and z-axis. These measured rotation rates characterize not only movements of the mobile phone 24 but also movements of the passenger 23. Measurements are continuously recorded and by combination of the individual
  • Measured values of the various acceleration sensors a continuous Movement pattern of the passenger 23 generated.
  • the measured values are filtered in particular by means of a low-pass filter.
  • the said movement pattern thus contains in this case the gradients of the rotation rates about the x, y and z axes.
  • the mobile phone 24 compares the continuous motion pattern thus generated with stored ones
  • features in the form of mean values, standard deviations and minimum / maximum values of the individual rotation rates or time segments of the rotation rates are determined and compared with stored values. Are the differences between the characteristics of the measured gradients and the stored characteristics smaller than determinable
  • Threshold values a sufficient match of a movement pattern is detected with a stored signal pattern. From this, the mobile phone 24 concludes that the passenger 23 has entered the elevator car 11. The mobile phone 24 can do this
  • the comparison between a measured movement pattern and a stored signal pattern and thus the recognition or classification of movement patterns can also be carried out with methods of so-called machine learning.
  • machine learning For example, a support vector machine, a random forest algorithm or a deep learning algorithm can be used.
  • transversal accelerations in the x, y and z directions can also be taken into account so that the movement pattern additionally contains the courses of the accelerations in the x, y and z directions.
  • the mobile phone is the detection of entering a
  • FIGS. 2a, 2b and 2c show a measured movement pattern and a stored signal pattern over time, wherein in FIG. 2a the rotation rates ⁇ are about the x-axis, in FIG. 2b about the y-axis and in FIG. 2c is shown around the z-axis.
  • the measured rate of rotation is represented in each case by a solid line and the stored rates of rotation of the signal pattern in each case by a dashed line.
  • the solid lines 26a, 26b, 26c thus represent the measured rotation rates and the dashed lines 27a, 27b, 27c represent the stored rotation rates about the x, y and z axes.
  • the measured values are shown as smoothed.
  • the stored signal pattern (dashed lines 27a, 27b, 27c) contains typical gradients of rotation rates, such as occur when entering an elevator car. From the time t0 to the time tl, the passenger approaches the landing door to stop at the time t1 and wait for the opening of the manhole and car door until time t2. There are virtually no rotation rates. From the time t2, the passenger enters the elevator car and then turns in the direction
  • Movement pattern with stored signal patterns runs as described above. Due to this agreement, the mobile phone concludes that the passenger has entered the elevator car.
  • the measured movement pattern is not limited to one
  • the accelerations in the x, y and z directions can also be taken into account in a comparable manner.
  • running in the direction of the shaft door and into the elevator car, as well as the waiting in front of and in the elevator car can be identified more easily.
  • further measured values detected by sensors of the mobile telephone are evaluated.
  • the mobile telephone 24 detects, in particular with the three-dimensional magnetic field sensor, the magnetic field strength in the x, y and z directions. The measured values thus characterize a property of the elevator installation. It is very difficult to conclude from measurements at a single time that the mobile phone and thus the passenger is in an elevator car.
  • a property pattern is created from the temporal progressions of the three field strengths, the measured values being filtered in particular by means of a low-pass filter.
  • the mobile phone 24 compares the continuous property pattern thus generated with stored signal patterns which are typical of a property pattern upon entering an elevator car 11. Will a sufficient match of a
  • a measured property pattern and a stored signal pattern over time is shown, wherein in Fig. 3a, the magnetic field strength H in the x direction, in Fig. 3b in the y direction and in Fig. 3 c are shown in the z direction.
  • the measured field strengths are each shown with a solid line and the stored field strengths of the signal pattern each with a dashed line.
  • the solid lines 28a, 28b, 28c thus represent the measured field strengths and the dashed lines 29a, 29b, 29c represent the stored field strengths in the x, y and z directions.
  • the measured values are shown smoothed.
  • the stored signal pattern (dashed lines 29a, 29b, 29c) contains typical courses of field strengths, such as occur when entering an elevator car. Shortly before until shortly after time t2 at which the passenger enters the elevator car, the field strengths in the y and z directions show a significant increase, whereas the field strength in the x direction remains virtually unchanged throughout the entire time. The change in field strengths is due in particular to the use of ferromagnetic materials in the elevator car. As can be seen in Figs. 3a, 3b and 3c, the measured property pattern (solid lines 28a, 28b, 28c) follows quite closely stored signal pattern. This match is another indication to the mobile phone that the passenger has entered the elevator car. The comparison of
  • Property pattern with stored signal patterns is analogous to the above-described comparison of the movement pattern with stored signal patterns.
  • a further increase in the reliability of detecting an entry of an elevator car can be achieved by additionally taking into account measured values which characterize an activity of the elevator installation.
  • Activity patterns are compared, which is compared with a signal pattern that is typical for opening the cabin and landing door. Another possibility is to derive an activity pattern from sounds measured with the microphone and to compare this with a signal pattern that is typical for opening the booth and landing door. As with the motion and property patterns, it may be useful to compare the activity patterns to multiple, slightly different signal patterns. A reasonable match between the measured
  • Activity patterns and a stored signal pattern can in turn be interpreted as an indication that the passenger has entered an elevator car.
  • the mobile phone can be designed so that it is already an entry
  • the elevator car recognizes when there is a single sufficient match of a motion pattern, a property pattern, or an activity pattern with a stored signal pattern. But it is also possible that an entrance is recognized only when there are at least two, three or more matches.
  • the stored signal patterns can be adapted.
  • the method can be adapted in particular to the behavior of the owner of the mobile phone. For this purpose, the mobile phone recognizes in particular a ride in an elevator car. This can be detected very reliably by monitoring the acceleration in the z direction and thus in the vertical direction 13. In FIG. 4, by way of example with the line 30, a profile of the acceleration a in the z-direction upwards is shown, wherein the gravitational acceleration is disregarded.
  • the elevator car 11 and thus also the passenger 23 with its mobile telephone 24 are accelerated from the time t4 with a nearly constant acceleration. Just before the desired speed of the elevator car 11 is reached, the acceleration decreases to reach the zero line at time t5. The elevator car 11 then travels at a constant speed until time t6, and then with a quasi-constant negative
  • motion, activity and / or property patterns acquired prior to the trip are compared with stored signal patterns and, based on the comparison, the stored signal patterns are adjusted using machine learning methods.
  • the stored signal pattern in the direction of the motion, activity and / or

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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)
  • Maintenance And Inspection Apparatuses For Elevators (AREA)

Abstract

L'invention concerne un procédé de détection de l'entrée d'un passager dans une cabine d'ascenseur d'un système d'ascenseur. Selon le procédé de l'invention pour détecter l'entrée d'un passager (23) dans une cabine d'ascenseur (11) d'un système d'ascenseur (10), ledit passager (23) porte un terminal mobile (24) sur lui. Le terminal mobile (24) présente au moins un capteur (25), en particulier plusieurs capteurs (25) au moyen desquels il acquiert et analyse des valeurs de mesure. La détection de l'entrée d'un passager (23) dans la cabine d'ascenseur (11) se fait sur la base desdites valeurs de mesure.
EP17758571.8A 2016-09-13 2017-09-04 Procede de detection de l'entree d'une personne dans une cabine d'ascenseur Revoked EP3512791B1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PL17758571T PL3512791T3 (pl) 2016-09-13 2017-09-04 Sposób wykrywania wchodzenia pasażera do kabiny dźwigowej instalacji dźwigowej

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP16188443 2016-09-13
PCT/EP2017/072106 WO2018050471A1 (fr) 2016-09-13 2017-09-04 Procédé de détection de l'entrée d'un passager dans une cabine d'ascenseur d'un système d'ascenseur

Publications (2)

Publication Number Publication Date
EP3512791A1 true EP3512791A1 (fr) 2019-07-24
EP3512791B1 EP3512791B1 (fr) 2020-08-12

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
EP17758571.8A Revoked EP3512791B1 (fr) 2016-09-13 2017-09-04 Procede de detection de l'entree d'une personne dans une cabine d'ascenseur

Country Status (11)

Country Link
US (1) US11634300B2 (fr)
EP (1) EP3512791B1 (fr)
KR (1) KR20190044635A (fr)
CN (1) CN109689551B (fr)
AU (1) AU2017327418B2 (fr)
BR (1) BR112019003450A2 (fr)
CA (1) CA3035433A1 (fr)
MX (1) MX2019002883A (fr)
PL (1) PL3512791T3 (fr)
SG (1) SG11201901485SA (fr)
WO (1) WO2018050471A1 (fr)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018050471A1 (fr) * 2016-09-13 2018-03-22 Inventio Ag Procédé de détection de l'entrée d'un passager dans une cabine d'ascenseur d'un système d'ascenseur
AU2017327417B2 (en) * 2016-09-13 2020-07-09 Inventio Ag Method for monitoring an elevator system
EP3299325B1 (fr) * 2016-09-26 2020-12-09 KONE Corporation Detection d'impact dans une porte d'ascenseur
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BR112019003450A2 (pt) 2019-05-21
PL3512791T3 (pl) 2021-02-08
MX2019002883A (es) 2019-07-04
US11634300B2 (en) 2023-04-25
US20190193986A1 (en) 2019-06-27
CN109689551A (zh) 2019-04-26
AU2017327418B2 (en) 2020-07-09
SG11201901485SA (en) 2019-03-28
EP3512791B1 (fr) 2020-08-12
AU2017327418A1 (en) 2019-04-04
WO2018050471A1 (fr) 2018-03-22
CN109689551B (zh) 2021-10-22
KR20190044635A (ko) 2019-04-30

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