Elevator load factor determination

Abstract

According to one aspect, an apparatus for determining a load factor of an elevator car is provided. The apparatus may obtain a first set of sensor data associated with an empty elevator car from at least one time-of-flight sensor disposed in the elevator car, the first set of sensor data including a first data point; removing data points associated with a wall of the elevator car from the first data points to obtain floor data points; determining a boundary volume around the floor data points; obtaining a second set of sensor data associated with the elevator car from the at least one time-of-flight sensor, the second set of sensor data including a second data point; and calculating a load factor of the elevator car based at least in part on the second data point within the boundary volume.

Description

Technical Field

[0001] The various examples generally relate to the field of elevator systems. In particular, some examples involve solutions for determining the fill rate of an elevator car based on sensor data. Background Technology

[0002] Modern elevator systems are characterized by multiple sensors (e.g., cameras or other sensor types) for various tasks, such as detecting any obstructions between closed doors. The elevator's occupancy rate can also be calculated based on sensor data. When the occupancy rate is known, passenger flow can be optimized, unnecessary stops at floors can be avoided, and the overall user experience can be improved. Summary of the Invention

[0003] The scope of protection sought by the various exemplary embodiments of this disclosure is set forth in the independent claims. Exemplary embodiments and features (if any) described in this specification that are not within the scope of the independent claims should be interpreted as examples of various exemplary embodiments that may aid in understanding the content of this disclosure.

[0004] According to a first aspect, an apparatus for determining the load factor of an elevator is provided. The apparatus includes at least one processor and at least one memory storing instructions, which, when executed by the at least one processor, cause the apparatus to at least: obtain a first set of sensor data associated with an empty elevator car from at least one time-of-flight sensor arranged in the elevator car, the first set of sensor data including first data points; obtain floor data points based on the first data points; determine a boundary volume surrounding the floor data points; obtain a second set of sensor data associated with the elevator car from the at least one time-of-flight sensor, the second set of sensor data including second data points; and calculate the load factor of the elevator car based at least partially on the second data points within the boundary volume. This provides a simple, accurate, and cost-effective solution for determining the load factor.

[0005] In one implementation of the first aspect, the boundary volume is a three-dimensional rectangular box. This allows for the determination of the desired volume that will include floor data points.

[0006] In an embodiment of the first aspect, at least one memory stores instructions that, when executed by at least one processor, cause the device to at least: remove data points associated with the walls of the elevator car from a first data point set to obtain floor data points. This provides an efficient solution for determining floor data points.

[0007] In an implementation of the first aspect, a deep learning algorithm for image reconstruction is used to remove data points associated with the walls of the elevator car from the first data points. This provides an efficient solution for determining floor data points.

[0008] In the first implementation, the load factor is calculated based on the following equation:

[0009] .

[0010] In an embodiment of the first aspect, at least one memory stores an instruction that, when executed by at least one processor, causes the device to at least: transmit the calculated load factor. This provides an efficient solution for determining the load factor via a device associated with the elevator car and then transmitting the load factor for further use.

[0011] According to a second aspect, an elevator system is provided, comprising: the device according to the first aspect, and at least one time-of-flight sensor disposed in an elevator car and configured to provide sensor data to the device.

[0012] In the second embodiment, the elevator system also includes an elevator controller, which is configured to obtain a load factor and control the elevator car at least partially based on the load factor.

[0013] In the second embodiment, the elevator system also includes an elevator group controller, which is configured to obtain the load factor and control the elevator car based on the load factor.

[0014] In the second embodiment, the device is configured to transmit the load factor to the elevator controller or elevator group controller.

[0015] According to a third aspect, a method for calculating the occupancy rate of an elevator car is provided. The method includes: obtaining a first set of sensor data associated with an empty elevator car from at least one time-of-flight sensor arranged in the elevator car, the first set of sensor data including first data points; obtaining floor data points based on the first data points; determining a boundary volume surrounding the floor data points; obtaining a second set of sensor data associated with the elevator car from at least one time-of-flight sensor, the second set of sensor data including second data points; and calculating the occupancy rate of the elevator car based at least partially on the second data points within the boundary volume.

[0016] In the third implementation, the boundary volume is a three-dimensional rectangular box.

[0017] In a third embodiment, the method further includes removing data points associated with the walls of the elevator car from the first data points to obtain floor data points.

[0018] In the third implementation, a deep learning algorithm is used to remove data points associated with the walls of the elevator car from the first data point.

[0019] In the third implementation, the load factor is calculated based on the following equation:

[0020] .

[0021] In the third implementation, the method also includes calculating the full load rate of the transmission.

[0022] According to the fourth aspect, a computer program including instructions is provided, which, when executed by at least one processor, cause a device or apparatus to perform the method of the third aspect.

[0023] According to a fifth aspect, a computer-readable medium is provided that includes a computer program comprising instructions that, when executed by at least one processor, cause a device or apparatus to perform the method of the third aspect.

[0024] According to a sixth aspect, an apparatus for determining the load factor of an elevator is provided. The apparatus includes means for: obtaining a first set of sensor data associated with an empty elevator car from at least one time-of-flight sensor arranged in the elevator car, the first set of sensor data including first data points; obtaining floor data points based on the first data points; determining a boundary volume surrounding the floor data points; obtaining a second set of sensor data associated with the elevator car from at least one time-of-flight sensor, the second set of sensor data including second data points; and calculating the load factor of the elevator car based at least partially on the second data points within the boundary volume. This provides a simple, accurate, and cost-effective solution for determining the load factor. Attached Figure Description

[0025] The accompanying drawings are included to provide a further understanding of the invention and form part of this specification. The drawings illustrate examples of the invention and, together with the description, help to explain the principles of the invention. In the drawings:

[0026] Figure 1 A flowchart of a method according to an example embodiment is shown.

[0027] Figure 2 A block diagram of an apparatus according to an example embodiment is shown.

[0028] Figure 3 A system according to an example embodiment is shown.

[0029] Figure 4 An elevator car and a time-of-flight sensor installed in the ceiling of the elevator car are shown according to an example embodiment.

[0030] Figure 5AAn image representation of time-of-flight sensor data related to an elevator car, according to an example embodiment, is shown.

[0031] Figure 5B An image representation of time-of-flight sensor data related to an elevator car, according to an example embodiment, is shown, wherein data points related to the wall have been cropped.

[0032] Figure 5C An image representation of time-of-flight sensor data related to an elevator car is shown according to an example embodiment, where a boundary volume has been defined around the floor data points.

[0033] Figure 5D An image presentation of preprocessed time-of-flight sensor data from an empty elevator is shown according to an example embodiment.

[0034] Figure 5E An image presentation of preprocessed time-of-flight sensor data related to an elevator car with two people inside, according to an example embodiment, is shown. Detailed Implementation

[0035] The various examples and embodiments discussed below illustrate a solution in which time-of-flight (ToF) sensor data can be used to determine the occupancy rate of an elevator car. The occupancy rate can be used to control the elevator car, such as skipping floors. Furthermore, the sensor data provided by the time-of-flight sensor does not distinguish between human faces, thus providing passenger privacy.

[0036] Figure 1 A flowchart illustrating an example of calculating the occupancy rate of an elevator car is shown. This method can be implemented, for example, by a device associated with the elevator car (e.g., an elevator controller or elevator group controller).

[0037] At point 100, a first set of sensor data associated with an empty elevator car can be obtained from at least one time-of-flight (ToF) sensor arranged in the elevator car. This first set of sensor data includes a first data point. In a smaller elevator car, a single ToF sensor may be able to cover the interior space of the elevator car. In a larger elevator, more than one ToF sensor may be used. In an example embodiment, ToF sensors can be synchronized with each other. For example, if the elevator car is large and a single sensor cannot cover the entire floor area, multiple ToF sensors can be mounted on the ceiling and synchronized together to provide better accuracy and coverage for the entire floor area. A ToF sensor is a 3D sensor that uses pulses of invisible infrared laser light to illuminate an object and measures the time it takes for the reflected light to reach the image sensor to determine depth information. A ToF sensor can illuminate an entire scene and record depth information within it using a single laser pulse.

[0038] At point 102, floor data points can be obtained based on the first data point. In an example embodiment, floor data points can be obtained by removing data points associated with the walls of the elevator car from the first data point. In another example embodiment, floor data points can be manually defined as provisioning parts during the installation phase. For example, a technician can manually create rectangles based on the corners of the floors using a graphical user interface (GUI).

[0039] The first data point may also include data points associated with the walls of the elevator car. Since these data points are not needed in the load factor calculation, they can be removed. This removal of data points associated with the elevator car walls from the first data point can be performed, for example, using a deep learning algorithm.

[0040] At point 104, the boundary volume surrounding the floor data points can be determined. This boundary volume can have a three-dimensional rectangular shape. In other examples, the shape can be different.

[0041] At point 106, a second set of sensor data associated with the elevator car can be obtained from at least one time-of-flight sensor. This second set of sensor data includes a second set of data points. For example, the second set of sensor data can be obtained when there are passengers in the elevator car.

[0042] At point 108, the elevator car's load factor can be calculated, at least in part, based on a second data point within the boundary volume. When there are passengers in the elevator car, the second data point does not fall within the boundary volume. This information can be used when calculating the load factor. Based on the load factor, unnecessary stops of the elevator car can be avoided. The calculated load factor can be transmitted to one or more entities, such as the elevator controller or elevator group controller. In one example, the load factor can be calculated based on the following equation:

[0043] .

[0044] Therefore, the load factor can be calculated as the difference between the total area of ​​the boundary volume and the total area within the boundary volume containing the second data point. The result can then be normalized to a percentage using a final multiplication.

[0045] In another example embodiment, the above equation can be modified depending on the installation configuration. For example, depending on the shape of the elevator and / or the performance metrics of the ToF sensor, the load factor can be determined based on volume differences rather than area differences. In the example embodiment, the data points can be normalized before using volume-based differences.

[0046] Figure 2A block diagram of an apparatus 200 according to an example embodiment is shown. The apparatus 200 includes one or more processors 202, one or more memories 204 including computer program code 206, and / or a communication interface 208 for wired and / or wireless communication. Although the apparatus 200 is depicted as including only one processor 202, the apparatus 200 may include more than one processor. In the example, the memory 204 is capable of storing instructions, such as an operating system and / or various applications.

[0047] Furthermore, processor 202 is capable of executing stored instructions. In example embodiments, processor 202 may be implemented as a multi-core processor, a single-core processor, or a combination of one or more multi-core processors and one or more single-core processors. For example, processor 202 may be embodied as one or more of various processing devices, such as a coprocessor, microprocessor, controller, digital signal processor (DSP), processing circuitry with or without an accompanying DSP, or various other processing devices including integrated circuits, such as, for example, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontroller units (MCUs), hardware accelerators, dedicated computer chips, etc. In example embodiments, processor 202 may be configured to perform hard-coded functions. In example embodiments, processor 202 is embodied as an executor of software instructions, wherein the instructions may specifically configure processor 202 to perform the algorithms and / or operations described herein when the instructions are executed, for example, regarding Figure 1 The steps to be discussed.

[0048] Memory 204 may be embodied as one or more volatile memory devices, one or more non-volatile memory devices, and / or a combination of one or more volatile memory devices and non-volatile memory devices. For example, memory 204 may be embodied as a semiconductor memory (such as mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flash ROM, RAM (random access memory), etc.).

[0049] At least one memory 204 may store program instructions that, when executed by at least one processor 202, cause the device 200 to perform the functions of the various embodiments discussed herein. Furthermore, in embodiments, at least one of the processor 202 and the memory 204 may constitute a device for implementing the functions discussed. Additionally, the device 200 may be configured to obtain a first set of sensor data associated with an empty elevator car from at least one time-of-flight sensor arranged in the elevator car, the first set of sensor data including first data points; obtain floor data points based on the first data points; determine a boundary volume surrounding the floor data points; obtain a second set of sensor data associated with the elevator car from at least one time-of-flight sensor, the second set of sensor data including second data points; and calculate the elevator car's occupancy rate based at least partially on the second data points within the boundary volume.

[0050] A computer program for calculating the occupancy rate of an elevator car may include instructions that, when executed by at least one processor 202, cause the device 200 to perform any of the methods described above. Additionally, a computer-readable medium may include a computer program.

[0051] Figure 3 This demonstrates how to utilize device 200 in an example system. Figure 3 In this configuration, the Time-of-Flight (ToF) sensor 300 provides real-time sensor data to the device 200. The device 200 can be powered by an external power supply 302, as it can be installed inside an elevator car. However, the device 200 can also be installed in any other suitable location. Figure 2 In the example shown, device 200 can transmit the calculated load factor to, for example, elevator controller 304 or elevator group controller 306. Alternatively or additionally, device 200 can transmit the calculated load factor via wireless connectivity block 308 (e.g., a 4G / 5G modem or a WLAN modem). Through wireless transmission, the calculated load factor can be further transmitted directly to elevator controller 304, elevator group controller 306, or a server located outside the elevator system. If the server manages communication with multiple elevator groups, such as in the case of a large shopping mall, the server can communicate with elevator controller 304 / elevator group controller 306 or multiple elevator controllers / elevator group controllers.

[0052] Figure 4 An example of an elevator car 400 is shown, which includes a ToF sensor 300 installed in the ceiling of the elevator car 400. The ToF sensor 300 is connected to the device 200. This connection can be wired or wireless.

[0053] Figure 5AAn example graphical representation of raw ToF sensor data generated by the ToF sensor 300 and plotted on a computer program's GUI is shown. Unwanted data point clusters 500, 502, and 504 can be removed, for example, using appropriate algorithms. Figure 5A In this analysis, data points in segments 500, 502, and 504 can be analyzed as being associated with walls or reflections that are not part of floor data point 506. Figure 5B This is followed by an example graphical representation of floor data point 506, where the walls have been removed.

[0054] Figure 5C An exemplary graphical representation is shown, in which a boundary volume 508 surrounds a floor data point 506. The height and width of the boundary volume 508 can be the same as the height and width of the area of ​​the floor data point 506. The depth of the boundary volume can be set to any suitable value. For example, the depth of the boundary volume can be ±X cm from the surface area of ​​the floor data point 506. Figure 5C In the example shown, the boundary volume 508 can have a three-dimensional rectangular shape. Other shapes are also possible in other examples.

[0055] Figure 5D This shows an example graphical representation of floor data point 506 when elevator car 400 is empty.

[0056] Figure 5E An example graphical representation of floor data points 510 from elevator car 400 is shown when two passengers are at positions 512 and 514 inside the elevator car. The signal emitted by ToF sensor 300 back to ToF sensor 300 and reflected from the passengers inside elevator car 400 (i.e., data points) is not within the boundary volume, and therefore, floor data points 510 are missing in the portion corresponding to the passengers.

[0057] In an example embodiment, the elevator system may include device 200, at least one ToF sensor 300 disposed in elevator car 400, and other system elements discussed above. The elevator system may include elevator controller 304 or elevator group controller 306 configured to obtain a load factor calculated by device 200, and elevator controller 304 or elevator group controller 306 may be configured to control elevator car 400 at least in part based on the load factor.

[0058] The examples discussed above can be implemented using software, hardware, application logic, or a combination of software, hardware, and application logic. The example devices can store information related to the various methods described herein. This information can be stored in one or more memories, such as hard disks, solid-state drives (SSDs), optical disks, magneto-optical disks, RAM, etc. One or more databases can store information used to implement the examples. The databases can be organized using data structures (e.g., records, tables, arrays, fields, graphs, trees, lists, etc.) included in one or more memories or storage devices listed herein. Appropriate data structures can be included with respect to the methods described in the examples for storing the data collected and / or generated by the methods of the example devices and subsystems in one or more databases.

[0059] The components of the example may include computer-readable media or storage for storing instructions programmed according to the teachings and for storing data structures, tables, records, and / or other data described herein. In the example, application logic, software, or instruction sets are maintained on any of a variety of conventional computer-readable media. In the context of this document, "computer-readable media" can be any medium or device that can contain, store, communicate, propagate, or transfer instructions for use by or in conjunction with an instruction execution system, device, or apparatus (such as a computer). Computer-readable media may include computer-readable storage media, which can be any medium or device that can contain or store instructions for use by or in conjunction with an instruction execution system, device, or apparatus (such as a computer). Computer-readable media may include any suitable medium that participates in providing instructions to a processor for execution. Such media can take many forms, including but not limited to non-volatile media, volatile media, transmission media, etc.

[0060] While the essential novel features applicable to preferred examples have been shown, described, and pointed out, it should be understood that various omissions, substitutions, and changes can be made to the form and details of the described apparatus and methods by those skilled in the art without departing from the spirit of this disclosure. For example, it is expressly intended that all combinations of those elements and / or method steps that perform substantially the same function in substantially the same manner to achieve the same result are within the scope of this disclosure. Furthermore, it should be recognized that structures and / or elements and / or method steps shown and / or described in conjunction with any of the forms or examples disclosed can be incorporated as a general matter of design choice into any other form of disclosure or description or suggestion. Moreover, in the claims, the device plus function clause is intended to cover the structures described herein as performing the functions, and includes not only structural equivalents but also equivalent structures.

[0061] The applicant hereby independently discloses each individual feature described herein, as well as any combination of two or more such features, provided that such features or combinations can be performed based on this specification as a whole, in accordance with common general knowledge of those skilled in the art, regardless of whether such features or combinations of features solve any problem disclosed herein, and without limiting the scope of the claims. The applicant notes that the disclosed aspects / embodiments may consist of any such individual features or combinations of features. In view of the foregoing description, it will be apparent to those skilled in the art that various modifications may be made within the scope of this disclosure.

Claims

1. A device (200) for determining the load factor of an elevator car (400), comprising: At least one processor (202); and At least one memory (204) storing instructions that, when executed by the at least one processor (200), cause the device (200) to perform at least the following: A first set of sensor data associated with an empty elevator car (400) is obtained from at least one time-of-flight sensor (300) arranged in the elevator car (400), the first set of sensor data including a first data point; Obtain floor data points based on the first data point; Determine the boundary volume (508) surrounding the floor data points; A second set of sensor data associated with the elevator car (400) is obtained from the at least one time-of-flight sensor (300), the second set of sensor data including a second data point; and The load factor of the elevator car (400) is calculated at least in part based on the second data points within the boundary volume (508).

2. The apparatus (200) according to claim 1, wherein, The boundary volume (508) is a three-dimensional rectangular box.

3. The apparatus (200) according to any one of the preceding claims, wherein the at least one memory (204) stores instructions that, when executed by the at least one processor (200), cause the apparatus (200) to perform at least the following: The floor data point is obtained by removing the data point associated with the wall of the elevator car (400) from the first data point.

4. The apparatus (200) according to claim 3, wherein, The removal of data points associated with the wall of the elevator car (400) from the first data points is performed using a deep learning algorithm for image reconstruction.

5. The apparatus (200) according to any one of the preceding claims, wherein, The load factor is calculated based on the following equation: 。 6. The apparatus (200) according to any one of the preceding claims, wherein the at least one memory (204) stores instructions that, when executed by the at least one processor (200), cause the apparatus (200) to at least: transmit the calculated load rate.

7. An elevator system comprising: The apparatus (200) according to any one of claims 1-6; and At least one time-of-flight sensor (300) is arranged in the elevator car (400) and configured to provide sensor data to the device (200).

8. The elevator system of claim 7 further includes an elevator controller (304), wherein the elevator controller (304) is configured to obtain the load factor and, based on the load factor, at least partially control the elevator car (400).

9. The elevator system of claim 7 further includes an elevator group controller (306), wherein the elevator group controller (306) is configured to obtain the full load rate and control the elevator (400) car based on the full load rate.

10. The elevator system according to any one of claims 7 to 9, wherein, The device (200) is configured to transmit the load factor to the elevator controller (304) or the elevator group controller (306).

11. A method for calculating the full load rate of an elevator car, the method comprising: A first set of sensor data associated with an empty elevator car (400) is obtained from at least one time-of-flight sensor (300) arranged in the elevator car (400), the first set of sensor data including a first data point; Obtain floor data points based on the first data point; Determine the boundary volume (508) surrounding the floor data points; A second set of sensor data associated with the elevator car (400) is obtained from the at least one time-of-flight sensor (300), the second set of sensor data including a second data point; and The load factor of the elevator car (400) is calculated at least in part based on the second data points within the boundary volume (508).

12. The method according to claim 11, wherein, The boundary volume (508) is a three-dimensional rectangular box.

13. The method according to any one of claims 11 to 12, further comprising: The floor data point is obtained by removing the data point associated with the wall of the elevator car (400) from the first data point.

14. The method according to claim 13, wherein, A deep learning algorithm is used to remove data points associated with the wall of the elevator car (400) from the first data point.

15. The method according to any one of claims 11 to 14, wherein, The load factor is calculated based on the following equation: 。 16. The method according to any one of claims 11 to 15, further comprising transmitting the calculated load factor.

17. A computer program comprising instructions that, when executed by at least one processor, cause a device to perform the method according to any one of claims 11 to 16.

18. A computer-readable medium comprising a computer program, the computer program including instructions that, when executed by at least one processor, cause a device to perform the method according to any one of claims 11 to 16.

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