METHOD AND DEVICE FOR DETERMINING A MAPPING OF A NUMBER OF FLOORS TO BE SERVED BY AN ELEVATOR AND FOR DETERMINING RELATIVE TRIP-DEPENDENT DATA OF AN ELEVATOR CAR

Abstract

A method for determining a mapping of a number of floors to be served by an elevator includes the steps of: (a) determining, during a multiplicity of trips of an elevator car of the elevator, a trip-dependent physical parameter value which unambiguously depends on at least one of a trip duration and a trip distance; and (b) clustering the determined trip-dependent physical parameter values to clusters to define each of the floors in the mapping. In a training phase, the method automatically determines the number of floors served and then, in an operation phase, classifies each of the observed trips and finally detects and tracks a current position of the elevator car. An elevator monitoring device implementing such method may be retrofitted into existing elevators for e.g. remotely monitoring the elevator operation and does not necessarily require any data transfer between components of the elevator and the elevator monitoring device.

Claims

1-15. (canceled)

16. A method for determining a mapping of a number of floors to be served by an elevator, the method comprising the steps of: determining, during each of a multiplicity of trips of an elevator car of the elevator, a trip-dependent physical parameter value that depends on at least one of a trip duration and a trip distance of the trips; and clustering the determined trip-dependent physical parameter values to define each of the floors in the mapping.

17. The method according to claim 16 including performing the clustering using a density-based clustering algorithm.

18. The method according to claim 16 including measuring the physical parameter value using an acceleration sensor.

19. the method according to claim 18 wherein a beginning of the at least one of a trip duration and a trip distance is triggered upon a physical parameter value relating to an acceleration measured by the acceleration sensor exceeding a first threshold value and an end of the at least one of a trip duration and a trip distance is triggered upon the physical parameter value relating to the measured acceleration falling below a second threshold value after exceeding a third threshold value.

20. The method according to claim 16 including measuring the physical parameter value using an air pressure sensor.

21. The method according to claim 20 wherein a beginning of the at least one of a trip duration and a trip distance is triggered upon a physical parameter value relating to a gradient of an air pressure measured by the air pressure sensor exceeding a first threshold value and an end of the at least one of a trip duration and a trip distance is triggered upon the physical parameter value relating to the gradient of the measured air pressure falling below a second threshold value.

22. The method according to claim 16 wherein the trip distance physical parameter value is determined by double integration of measured acceleration values during the elevator car trip.

23. The method according to claim 16 wherein the trip distance is determined based upon a pressure difference between air pressures measured at a beginning and at an end of the elevator car trip.

24. The method according to claim 16 wherein a beginning of the at least one of a trip duration and a trip distance is triggered based on a measurement of a first physical parameter value and wherein the trip-dependent physical parameter value is determined based on a measurement of a second physical parameter value.

25. A method for determining relative trip-dependent data of an elevator car, the method comprising the steps of: determining a trip-dependent physical parameter value that depends on at least one of a trip duration and a trip distance; classifying the determined trip-dependent physical parameter value to exactly one type of trip between floors defined in a mapping of a number of floors to be served by the elevator, the mapping being determined using a method according to claim 16; and determining the relative trip-dependent data of the elevator car based on the classification.

26. The method according to claim 25 including tracking the relative trip-dependent data to determine whether the elevator car has travelled along all of the number of floors in a consecutive order and setting an initial car position information of the elevator car to one of an uppermost floor and a lowermost floor of the number of floors depending on a travelled direction of the elevator car.

27. The method according to claim 26 wherein, upon each trip of the elevator car, setting a current position information of the elevator car to one of the number of floors to be served by the elevator based on the initial car position information and based on the trip-dependent data determined since the setting of the initial car position information.

28. An elevator monitoring device adapted to perform the method according to claim 16 to determine a mapping of a number of floors to be served by an elevator car of an elevator and/or to determine relative trip-dependent data of the elevator car.

29. The device according to claim 28 including at least one sensor for generating a signal representing the physical parameter value.

30. The device according to claim 29 wherein the at least one sensor is an acceleration sensor or an air pressure sensor.

31. A computer program product comprising computer readable instructions which, when performed by a processor of an elevator monitoring device, instruct the elevator monitoring device to at least one of perform and control the method according to claim 16.

32. A non-transitory computer readable medium comprising the computer program product according to claim 31 stored thereon.

Description

DESCRIPTION OF THE DRAWINGS

[0068] FIG. 1 shows an elevator in which a method according to an embodiment of the present invention may be implemented.

[0069] FIG. 2 visualizes various possible trips between floors served by an elevator.

[0070] FIG. 3 shows a clustering of measured trip-dependent physical parameter values in the form of trip durations for various elevator trips.

[0071] FIG. 4 shows a clustering of measured trip-dependent physical parameter values in the form of trip durations and trip distances for various elevator trips.

[0072] FIG. 5 shows a flow diagram for the method according to an embodiment of the present invention.

[0073] FIG. 6 shows a flow diagram for a positioner phase in a method according to an embodiment of the present invention.

[0074] The figures are only schematic and not to scale. Same reference signs refer to same or similar features.

DETAILED DESCRIPTION

[0075] FIG. 1 shows an elevator 1 in which an elevator car 3 may travel along an elevator shaft 5. The elevator car 3 may be stopped at each of a number F of k floors 7 (F=1, 2, 3, . . . , k−1, k) such as to serve all of the k floors 7. Upon opening a corresponding elevator door 9, passengers may enter and exit the elevator car 3 at each of the k floors 7.

[0076] A problem to be solved may be seen in obtaining information about characteristics of the elevator 1 and in estimating an absolute floor position of the elevator car 3 during operation of the elevator 1. Particularly, such obtaining of information and estimating of floor positions should be implemented in an automated manner. Preferably, both procedures may be implemented without a necessity of infrastructure deployed on every floor 7.

[0077] In order to solve such problem, an approach is proposed in which information about characteristics of the elevator 1 is obtained and an absolute floor position of the elevator car 3 is obtained upon learning and tracking from relative trip-dependent data.

[0078] For such purpose, an elevator monitoring device 11 is provided and is mechanically attached to the elevator car 3 such as to be moved throughout the elevator shaft 5 together with the car 3. The elevator monitoring device 11 comprises one or more sensors 17 such as an acceleration sensor 13 and/or an air pressure sensor 15. The sensors 17 are configured for measuring trip-dependent physical parameter values such as e.g. an acceleration acting onto the car 3 and/or an air pressure at the altitude of the car 3. Furthermore, the elevator monitoring device 11 comprises some signal processing capability using a central processing unit and some data memory.

[0079] The elevator monitoring device 11 is configured for independently determining a mapping of a number of floors 7 to be served by the elevator 1 such as to obtain the required information about characteristics of the elevator 1 and to obtain information about the absolute floor position of the elevator car 3. For this purpose, the elevator monitoring device 11 may determine trip-dependent physical parameter values obtained from sensors 17, such as e.g. acceleration values obtained from the acceleration sensor 13 and/or air pressure values obtained from the barometric air pressure sensor 15.

[0080] The elevator monitoring device 11 is then configured, in a learning phase (sometimes also referred to as training phase), to process the determined trip-dependent physical parameter values by conducting a clustering procedure. Upon clustering the trip-dependent physical parameter values, each of the number of floors 7 in the mapping may be defined. Accordingly, in the learning phase, the number k of floors 7 may be determined.

[0081] Furthermore, the elevator monitoring device 11 is configured, in an operation phase, to classify determined trip-dependent physical parameter values to exactly one trip between floors 7 defined in the previously obtained mapping of the number of floors 7 to be served by the elevator 1. As a result of such classification procedure, relative trip-dependent data of the elevator car 3 may be determined from which, upon further processing, information about the current absolute floor position of the elevator car 3 may be derived.

[0082] Before discussing details of procedures and algorithms to be performed upon implementing the method described herein with respect to FIGS. 5 and 6, an example of the clustering procedure for determining the mapping of the number of floors 7 will be explained with reference to FIGS. 2, 3 and 4.

[0083] FIG. 2 shows an example in which five floors 7 numbered “0” to “4” are served by an elevator 1. Various types of trips may be travelled by the elevator car 3. For example, short trips indicated as “±1” bring the car 3 from one of the floors 7 to a neighboring floor 7 above or below, i.e. a number ΔF of floors travelled is ±1. Longer trips indicated as “±2”, “±3” or “±4” bridge more of the floors 7 in an upwards direction and a downwards direction, respectively, up to a maximum floor distance between the outermost floors.

[0084] When travelling such trips, a trip duration Δt and/or a trip distance Δs or trip-dependent physical parameter values unambiguously correlating with such trip duration or trip distance may be determined.

[0085] For example, acceleration data provided by the acceleration sensor 13 may be continuously monitored. Upon such acceleration exceeding a predetermined first threshold value or, alternatively, upon such acceleration showing a gradient or a duration exceeding a predetermined first threshold value, the beginning of an elevator trip is detected and a measurement of the trip duration and/or trip distance is started. Such measurement is continued until the end of the elevator trip is detected, e.g. upon the acceleration falling below a second threshold value after exceeding a third threshold value, whereby the second and third threshold values are of opposite sign than the first threshold value. During such measurement, for example the duration Δt of the trip is determined. Alternatively or additionally, the distance Δs of the trip is determined for example by integrating twice the acceleration values obtained from the acceleration sensor 13 during the measurement or by calculating a difference in air pressures measured by the air pressure sensor 15 at the beginning and at the end of the trip.

[0086] FIG. 3 shows a one-dimensional representation of measured trip durations Δt determined during the learning or training phase. FIG. 4 shows a two-dimensional representation of measured trip durations Δt and corresponding trip distances Δs determined during the learning or training phase. It may be seen that most of the measured duration values (Δt) and duration-distance value pairs (Δt, Δs) are within one of a plurality of clusters 19. A center position of these clusters corresponds approximately to the trip distance (Δt) and the trip distance-duration pair (Δt, Δs) for trips of one of the possible types of trips between floors 7 in the monitored elevator 1. Only a few measurement data do not fall into such clusters 19 and will therefore be treated a noise data 21.

[0087] In order to determine the mapping of the number of floors 7 and to finally provide relative trip-dependent data and information about a current position of the elevator car 3, the elevator monitoring device 11 is configured to perform several algorithms including a clustering algorithm, a classification algorithm and a positioner algorithm.

[0088] The clustering algorithm is adapted for learning the number k of floors 7 that the elevator serves. The clustering algorithm may rely on density-based clustering (DBSCAN).

[0089] The classification algorithm is adapted for estimating the number of floors ΔF travelled by the elevator car 3 during a trip and may be trained on the clustered data.

[0090] The positioner algorithm is adapted for tracking the current floor position based on relative trip data.

[0091] Details of a possible embodiment of a method according to the present invention shall be described with reference to FIG. 5 and FIG. 6. FIG. 5 and FIG. 6 show exemplary diagrams of the procedure of the entire method and of the positioner phase comprised therein, respectively.

[0092] In a training phase S.sub.T, the system trains itself before then entering an operation phase S.sub.O.

[0093] During the training phase S.sub.T, the system estimates the number k of floors 7 that the elevator 1 serves from training data D.sub.t, i.e. from data from various previous trips over a period T. Such estimation is based on a clustering procedure 23 applied to determined trip-dependent physical parameter values serving as training data D.sub.t such as accelerations values and/or air pressures values. The clustering 23 may be performed using density-based clustering techniques such as DBSCAN. Therein, an up and down travelling direction is not necessarily distinguished, i.e. for example a sign of a trip distance may be ignored. As a result of the clustering, so-called components may be defined. The components are those observations that have been assigned a cluster label, i.e. are not noise. In other words, each cluster 19 is represented by a component.

[0094] The clusters 19 are then submitted to a sorting procedure 25. Therein, the clusters 19 may be sorted e.g. in an ascending order of distance travelled so that a cluster label of e.g. “1”, “2”, etc. represents the number of floors travelled or bridged during a trip.

[0095] Subsequently, a classifier 27 is trained based on operation data D.sub.o such that each of future trips may be assigned a distinct cluster number, i.e. a distinct number ΔF of floors travelled. Such classification may be implemented using e.g. Naïve Bayes or k-Nearest Neighbor (KNN) classifiers. Accordingly, each observed trip is assigned to one type of possible trips bridging ΔF floors as represented by the clusters 19, including those data of trips which appear to lie outside of all clusters 19.

[0096] Then, in the positioner phase 29, the system follows the movement of the elevator car 3 inside the elevator shaft 5, i.e. tracks the relative trip-dependent data classified based on the determined trip-dependent physical parameter values. Therein, information about the current position of the elevator car 3 may be derived as soon as it is detected that the elevator car 3 has travelled along the entire height of the elevator shaft, i.e. the elevator car 3 has travelled along all of the number k of floors 7 served by the elevator 1. Such travelling should be in a consecutive order and could be in one run or in several stages. If such consecutive travelling along the entire height is observed, the information about the current position P.sub.F of the elevator car 3 may be set to the uppermost floor (F=k) or to the lowermost floor (F=1), depending on whether the travelling direction of the consecutive travel was upwards or downwards. In other words, the position P.sub.F of the car 3 may be locked-in at the highest floor or at the lowest floor, respectively.

[0097] A possible implementation of the positioner phase 29 may be understood from the flow diagram in FIG. 6. The positioner phase 29 is configured to track the position of the car 3 from the number ΔF of floors travelled. It detects when the car 3 has travelled the entire elevator shaft 5 to either the uppermost floor or the lowermost floor and sets its current position accordingly. The indices used in FIG. 6 are as follows: a=lower shaft end, b=upper shaft end, x=current position during search, Pos=car's position inside the shaft, ΔF=number of floors travelled with direction up (+) or down (−), k=number of accessible floors. The algorithm is initialized to “Pos=not” and “x=a=b=0”.

[0098] For example, at the beginning of the procedure, the starting floor is set to x=0. At that stage, the initial values for the lower shaft end and the upper shaft end are set to a=b=0. Then, in a first trip, the car is displaced e.g. towards the next floor in an upwards direction, i.e. a trip “+1” is travelled. At that stage, the value for the lower shaft end is still a=0, but the values for the upper shaft end as well as for the current floor are set to b=1 and x=1. Then, in a next trip, the car is moved three floors downwards, i.e. a trip “−3” is travelled. At that stage, the value for the lower shaft end is set to a=−2, the value for the upper shaft end stays at b=1 and the value for the current floor is set to b=−2. In the exemplary arrangement of FIG. 2 having five floors, similar processes are repeated preferably until all floors have been travelled to and all types of trips “±1”, “±2”, “±3” and “±4” have been executed at least once. Then, the operation of the elevator is monitored until a situation is observed where the car 3 has travelled to either the uppermost or the lowermost floor. At that point, the position of the car 3 may be determined on an absolute basis, i.e. it may be determined at which one of the known number of floors the car 3 is currently positioned.

[0099] During the operation phase, the system may then track the relative trip-dependent data and update the current position of the car 3 in accordance with such data. The system may read new trip-dependent physical parameter values relating to trip duration and/or trip distance, i.e. a feature vector, and may estimate the number of floors travelled, i.e. classify the feature vector. Furthermore, a direction of up- or down travel may be assigned from the sign of the trip distance measurement. Finally, the positioner algorithm may be updated with the estimated number of floors travelled. Accordingly, the information indicating the current position of the elevator car, i.e. indicating the floor at which the elevator car is currently located, may be continuously updated based on the initially set car position information and taking into account the relative trip-dependent data determined since setting this initial car position information.

[0100] It may be noted that, in some extraordinary cases, the positioner algorithm may detect wrong absolute floor estimations. For example, it may be detected that a newly estimated floor position is above the uppermost floor or below the lowermost floor. As such estimation must obviously be wrong, in such situation, the positioner resets itself and waits until the car has reached the lowermost or uppermost floor again and then correctly sets the initial car position information.

[0101] Embodiments of the described method may run on a dedicated sensing system or elevator monitoring device 11 inside the elevator 1. Alternatively, the method may be implemented inside a cloud environment which receives trip information such as trip duration and/or trip distance or suitable correlated trip-dependent physical parameter values from a system of sensors 17 in the elevator 1, i.e. in or at the elevator car 3.

[0102] Briefly summarized, the method allows to, in a training phase, automatically determining the number of floors served by an elevator and then, in an operation phase, classify each of observed trips and finally detect and track a current position of the elevator car. An elevator monitoring device implementing such method may be retrofitted into existing elevators for e.g. remotely monitoring the elevator operation and does not necessarily require any data transfer between components of the elevator and the elevator monitoring device.

[0103] Summarized in an alternative wording, prior art approaches for determining the position of an elevator car 3 generally require infrastructure on every floor 7 such as magnetic or optical flags that uniquely identify each of the floors 7. Alternatively, a sensor based floor estimation using barometric pressure sensors 15 (one pressure sensor being attached to the car 3 and one pressure sensor being arranged at a fixed and known reference height) may be used. As an alternative to such conventional approaches, embodiments of the invention do not need to deploy infrastructure on every floor 7 served by the elevator 1. Furthermore, the proposed solution may be independent of the sensing modality. Additionally, the proposed method may provide a probability value or noise indicator to indicate a level of certainty of the floor estimation. As a result, a set of a priori knowledge may be reduced when deploying sensor hardware. Furthermore, the approach proposed herein may be applied in modernization or new installations where additional sensing hardware is deployed without connection to the elevator shaft information system or to an elevator operation controller.

[0104] Finally, it should be noted that the term “comprising” does not exclude other elements or steps and the “a” or “an” does not exclude a plurality. Elements described in association with different embodiments may be combined.

[0105] In accordance with the provisions of the patent statutes, the present invention has been described in what is considered to represent its preferred embodiment. However, it should be noted that the invention can be practiced otherwise than as specifically illustrated and described without departing from its spirit or scope.