System and method for assisting passengers detected at elevator bank

Abstract

An elevator system is provided with: a call station located in a hall; a device controller; a proximity sensor operatively coupled to the device controller, located in the hall and configured to generate proximity sensor data utilized by the device controller; a speaker operatively coupled to the device controller and located in the hall; wherein, based on the proximity sensor data, the device controller is configured to: detect a presence of a passenger; determining that the passenger needs help to locate the call station in the hall; and emitting audible sound from the speaker to guide the passenger to the call station.

Description

TECHNICAL FIELD

[0001] Embodiments described herein relate to passenger conveyor systems, such as elevators, escalators, moving walkways, and other automated people movers, and more particularly to systems and methods for assisting passengers detected at an elevator bank. BACKGROUND

[0002] Calling an elevator car to a hall can require use of an elevator call station within the hall proximate to the elevator bank. For individuals with disabilities that affect their vision (blindness, low vision), finding an elevator call station at an elevator bank can be challenging. SUMMARY

[0003] Disclosed is an elevator system comprising: a call station located in a hall; a device controller; a proximity sensor operatively coupled to the device controller, located in the hall, and configured to generate proximity sensor data utilized by the device controller; a speaker operatively coupled to the device controller and located in the hall; wherein, in accordance with the proximity sensor data, the device controller is configured to: detect a presence of a passenger; determine that the passenger needs assistance to locate the call station in the hall; and emit an audible sound from the speaker to guide the passenger to the call station.

[0004] In addition to one or more aspects of the system, or as an alternative, the system is configured to detect movement of the passenger toward an elevator car at the hall, and to control the elevator car to remain at the hall with elevator doors open until the passenger has boarded the elevator car.

[0005] In addition to one or more aspects of the system, or as an alternative, the call station comprises the device controller, the proximity sensor, and the speaker.

[0006] In addition to one or more aspects of the system, or as an alternative, the proximity sensor is one or more of: a video proximity sensor; a motion detector; a LIDAR; or a RADAR.

[0007] In addition to one or more aspects of the system, or as an alternative, the device controller is configured to: compare the proximity sensor data to pre-recorded proximity sensor data to identify a characteristic of the passenger that includes an implement accompanying the passenger, to determine that the passenger needs assistance to locate the call station in the hall.

[0008] In addition to one or more aspects of the system described above, or as an alternative, the device controller is configured to: apply the proximity sensor data to a trained machine learning model to identify a characteristic of the passenger that includes a tool accompanying the passenger to determine that the passenger needs assistance to locate the call station in the lobby.

[0009] In addition to one or more aspects of the system described above, or as an alternative, the device controller is configured to determine that the passenger needs assistance to locate the call station in the lobby with the proximity sensor data by: identifying a characteristic of a guide dog accompanying the passenger; and / or identifying a characteristic of a cane accompanying the passenger.

[0010] In addition to one or more aspects of the system described above, or as an alternative, the device controller is configured to determine that the passenger is wandering in the lobby with the proximity sensor data by: determining that the passenger is moving within the lobby at a speed below a threshold value; or determining that the passenger is moving along one or more predetermined paths near the call station without utilizing the call station.

[0011] In addition to one or more aspects of the system described above, or as an alternative, the device controller is configured to determine that the passenger is wandering in the lobby with the proximity sensor data by: determining that the passenger is moving within the lobby at a speed below a threshold value; or determining that the passenger is moving along one or more predetermined paths near the call station without utilizing the call station.

[0012] In addition to one or more aspects of the system described above, or as an alternative, the audible sound includes: a verbal indication to the call station relative to a current location of the passenger; and / or a "ping" that increases in one or more of amplitude and frequency as the passenger moves closer to the call station.

[0013] Disclosed is a method of controlling an elevator system, comprising: detecting, by a device controller that receives proximity sensor data from a proximity sensor, a presence of a passenger, the proximity sensor operatively coupled to the device controller and located in a lobby; determining, by the device controller, that the passenger needs assistance to locate a call station in the lobby; and emitting, by a speaker, an audible sound to guide the passenger to the call station, the speaker operatively coupled to the device controller and located in the lobby.

[0014] In addition to one or more aspects of the method described above, or as an alternative, the method includes the system detecting movement of the passenger toward an elevator car at the lobby and controlling the elevator car to remain at the lobby with an elevator door open until the passenger has boarded the elevator car.

[0015] In addition to one or more aspects of the method, or as an alternative, the call station includes the device controller, the proximity sensor, and the speaker.

[0016] In addition to one or more aspects of the method, or as an alternative, the proximity sensor is one or more of: a video proximity sensor; a motion detector; a LIDAR; or a RADAR.

[0017] In addition to one or more aspects of the method, or as an alternative, in determining that the passenger needs assistance to locate the call station in the lobby, the method includes: comparing, by the device controller, the proximity sensor data to pre-recorded proximity sensor data to identify a characteristic of the passenger that includes a tool accompanying the passenger.

[0018] In addition to one or more aspects of the method, or as an alternative, in determining that the passenger needs assistance to locate the call station in the lobby, the method includes: applying, by the device controller, the proximity sensor data to a trained machine learning model to identify a characteristic of the passenger that includes a tool accompanying the passenger to determine that the passenger needs assistance to locate the call station in the lobby.

[0019] In addition to one or more aspects of the method, or as an alternative, in determining that the passenger needs assistance to locate the call station in the lobby, the method includes: identifying, by the device controller, a characteristic of a guide dog accompanying the passenger from the proximity sensor data; and / or identifying, by the device controller, a characteristic of a cane accompanying the passenger from the proximity sensor data.

[0020] In addition to one or more aspects of the method, or as an alternative, in determining that the passenger needs assistance to locate the call station in the lobby, the method includes: identifying, by the device controller, a characteristic of the passenger loitering in the lobby from the proximity sensor data.

[0021] In addition to one or more aspects of the method, or as an alternative, in determining that the passenger is loitering in the lobby, the method includes: determining, by the device controller, that the passenger is moving at a speed below a threshold within the lobby from the proximity sensor data; or determining, by the device controller, that the passenger is moving along one or more predetermined patterns near the call station without utilizing the call station from the proximity sensor data.

[0022] In addition to one or more aspects of the method, or as an alternative, when the audible sound is emitted to direct the passenger to the call station, the method includes: emitting, by the speaker, a verbal indication to the call station relative to the passenger's current location; and / or emitting, by the speaker, a "ping" sound that increases in one or more of amplitude and frequency as the passenger moves closer to the call station. BRIEF DESCRIPTION OF DRAWINGS

[0023] The present disclosure is illustrated by way of example and not limitation in the accompanying drawings in which like reference numbers indicate similar elements.

[0024] Figure 1 is a schematic diagram of a passenger conveyor system (and particularly an elevator system) that can employ various embodiments of the present disclosure;

[0025] Figure 2 additional aspects of the system of Figure 1 ; and

[0026] Figure 3 is shown. DETAILED DESCRIPTION

[0027] Figure 1 is a perspective view of a passenger conveyor system (and particularly an elevator system) 101 in a building, including an elevator car 103 (often a passenger conveyor), a counterweight 105, a tensile member 107, a guide rail (or rail system) 109, a machine (or machine system) 111, a position reference system 113, and an electronic elevator controller (often an elevator controller) 115. As non-limiting examples, the elevator controller 115 can be connected directly to the car 103 or located separately in the building, or can be part of an elevator management system (EMS) in a control room in the building. The elevator car 103 and the counterweight 105 are connected to each other by the tensile member 107. The tensile member 107 can include or be configured as, for example, a rope, a steel cable, and / or a coated steel belt. The counterweight 105 is configured to balance the load of the elevator car 103 and to facilitate simultaneous and opposite direction movement of the elevator car 103 relative to the counterweight 105 within an elevator shaft (or hoistway) 117 and along the guide rail 109.

[0028] The tension member 107 engages a machine 111, which is part of the overhead structure of the elevator system 101. The machine 111 is configured to control movement between the elevator car 103 and the counterweight 105. A position reference system 113 can be mounted on a stationary portion at the top of the elevator shaft 117, such as on a sill or a guide rail, and can be configured to provide a position signal related to the position of the elevator car 103 within the elevator shaft 117. In other embodiments, the position reference system 113 can be mounted directly to a moving assembly of the machine 111, or can be positioned in other locations and / or configurations as are well known in the art. The position reference system 113 can be any device or mechanism for monitoring the position of the elevator car and / or counterweight, as is well known in the art. For example and not limitation, the position reference system 113 can be an encoder, a sensor, or other system, and can include speed sensing, absolute position sensing, etc., as will be appreciated by those skilled in the art.

[0029] As shown, an elevator controller 115 can be positioned in a controller room 121 of the elevator shaft 117, and is configured to control the operation of the elevator system 101 and, in particular, the operation of the elevator car 103. It is to be appreciated that the elevator controller 115 need not be in the controller room 121, but can be in a hoistway or other location or position in the elevator system 101. In one embodiment, the controller 115 can be remotely located or in the cloud. The elevator controller 115 can provide drive signals to the machine 111 to control acceleration, deceleration, leveling, stopping, etc. of the elevator car 103. The elevator controller 115 can also be configured to receive position signals from the position reference system 113 or any other desired position reference device. The elevator car 103 can stop at one or more landings 125 as controlled by the elevator controller 115 as it moves up or down the guide rails 109 within the elevator shaft 117.

[0030] The machine 111 can include a motor or similar drive mechanism. According to embodiments of the present disclosure, the machine 111 is configured to include an electric drive motor. The power supply for the motor can be any power source, including a power grid, which is supplied to the motor in conjunction with other components. The machine 111 can include a traction sheave that imparts force to the tension member 107 to move the elevator car 103 within the elevator shaft 117.

[0031] Although shown and described with a roping system including a tensile member 107, elevator systems employing other methods and mechanisms for moving an elevator car within an elevator shaft can also employ embodiments of the present disclosure. For example, embodiments can be employed in a ropeless elevator system that uses linear motors to move the elevator car. Embodiments can also be employed in a ropeless elevator system that uses hydraulic hoists to move the elevator car. Embodiments can also be employed in a ropeless elevator system that uses a self-propelled elevator car (e.g., an elevator car equipped with friction wheels, pinch wheels, or traction wheels). Figure 1 The non-limiting examples presented are for purposes of illustration and explanation only.

[0032] Although elevator systems are disclosed in depth herein as non-limiting examples, the present disclosure is equally applicable to other forms of passenger conveyor systems. Passenger conveyor systems include moving walkways and escalators as well as other automated people movers as non-limiting alternatives to elevator systems, all of which move people between and along different levels in a building.

[0033] Turning to Figure 2 , additional aspects of the system 110 of Figure 1 are shown. Within a building 205, the system 101 can have elevator shafts 117A-117C. Within the shafts 117A-117C, a set of cars 103A-103C (generally set 104) are driven by machines 111A-111C via belts 107A-107C to move passengers 210 between floors 125. The passengers 210 can utilize a call station 211 on a first floor 125A to request service for transport to a second floor 125B. The cars 103A-103C can be powered via travel and hoistway cables (for simplicity, cables) 119A-119C and can communicate with an elevator controller 115 and can also be powered by on-board batteries 231A-231C. The cars 103A-103C can also communicate wirelessly with the elevator controller 115 through a network 235 via a communication access point 225, which can include a cloud service 240. The cars 103A-103C have doors 230A-230C and sensors 220A-220C that can communicate with the elevator controller 115 via wired or wireless communication. The sensors 220A-220C can sense the rate, acceleration, vibration of the cars 103A-103C, which can be generated by the motion of the cars 103A-103C and the operation of the doors 230A-230C. From these communications, the elevator controller 115 can track the health of the cars 103A-103C.

[0034] As indicated in more detail below, the system 101 is configured to identify whether the passenger 210 is in the lobby 125A at the elevator group 104. If it is determined that the passenger 210 needs assistance to find the call station 211, the call station 211 will emit an audible sound to help the passenger 210 locate the elevator call station 211.

[0035] In addition to the call station 211 located in the lobby 125A, the system 110 includes a device controller 116, which can be the same or different from the elevator controller 115. A proximity sensor 226, which is operatively coupled to the device controller 116, can be located in the lobby 125A and is configured to generate proximity sensor data 228 that is utilized by the device controller 116. The proximity sensor 226 can be one or more of a video proximity sensor, a motion detector, LIDAR, or RADAR. A speaker 227, which is operatively coupled to the device controller 116, can also be located in the lobby 125A. From the proximity sensor data 228, the device controller 116 is configured to detect the presence of the passenger 210. The device controller 116 is configured to determine that the passenger 210 needs assistance to locate the call station 211 in the lobby 125A. The device controller 116 is configured to emit an audible sound 223 (or prompt) from the speaker 227 to direct the passenger 210 to the call station 211.

[0036] In one embodiment, one of the call station 211, the speaker 227, and the proximity sensor 226 includes the device controller 116. For example, the processing performed herein can be via edge computing. The device controller 116 can be different from one or more of the call station 211, the speaker 227, and the proximity sensor 226 and communicate using a wireless or wired protocol. In one embodiment, the call station 211 includes each of the device controller 116, the proximity sensor 226, and the speaker 227.

[0037] In one embodiment, the device controller 116 communicates with a cloud service 240 via a network 235 when running the processing performed herein. The cloud service 240 can perform a portion of the processing of the data 228, which can be stitched together when analyzed by the cloud service 240 or the device controller 116.

[0038] In one embodiment, the device controller 116 is configured to compare the proximity sensor data 228 to pre-recorded proximity sensor data 228. From the comparison, the device controller 116 can identify a characteristic of the passenger 210 that includes a tool (e.g., a wheelchair, a cane, a walking stick, or a crutch) that is accompanying the passenger 210 that indicates that the passenger 210 can need assistance.

[0039] In one embodiment, the device controller 116 is configured to apply the proximity sensor data 228 to a trained machine learning model 229. By processing the proximity sensor data 228 via the model 229, the device controller 116 can identify characteristics of the passenger 210, including tools accompanying the passenger 210, that indicate that the passenger 210 needs assistance.

[0040] For example, in one embodiment, with the proximity sensor data 228, the device controller 116 is configured to identify characteristics of a guide dog 231 accompanying the passenger 210. In one embodiment, with the proximity sensor data 228, the device controller 116 is configured to identify characteristics of a cane 232 accompanying the passenger 210. From these characteristics, the device controller 116 can be configured to determine that the passenger 210 needs assistance to locate the call station 211 in the lobby 125A.

[0041] With the proximity sensor data 228, the device controller 116 can also be configured to identify characteristics of the passenger 210 wandering in the lobby 125A. From these characteristics, the device controller 116 is configured to determine that the passenger 210 needs assistance. For example, with the proximity sensor data 228, the device controller 116 is configured to determine that the passenger 210 is moving within the lobby 125A at a speed 233 below a threshold. With the proximity sensor data 228, the device controller 116 can be configured to determine that the passenger 210 is moving along one or more predetermined paths 234 (e.g., complete or partial loops) near the call station 211 without utilizing the call station 211.

[0042] The audible sound 223 can include verbal guidance to the call station 211 relative to the current location of the passenger 210. The audible sound 223 can include a “ping” sound that increases in magnitude and frequency in one or more of these dimensions as the passenger 210 moves closer to the call station 211. These are non-limiting examples of such sounds.

[0043] In one embodiment, the system 119 detecting that the passenger 210 moves to the elevator car 103 on the lobby 125A holds the control of the elevator car 103 on the lobby 125A with the doors of the elevator car 103 open until the passenger 210 has boarded.

[0044] Turning to Figure 3 , a flowchart illustrates a method of controlling an elevator system 110. In Figure 3 , the blocks in the flowchart that employ dashed lines represent further explanation of one or more previous steps and are not intended to limit the scope of the embodiments.

[0045] As shown in block 310, the method includes detecting, by the device controller 116 receiving proximity sensor data 228 from a proximity sensor 226 operatively coupled to the device controller 116 and located in the lobby 125A, the presence of the passenger 210.

[0046] As shown in block 320, the method includes determining, by the device controller 116, that the passenger 210 needs assistance to locate the call station 211 in the lobby 125A.

[0047] As shown in block 320A1, when determining that the passenger 210 needs assistance to locate the call station 211 in the lobby 125A, the method includes comparing, by the device controller 116, the proximity sensor data 228 to pre-recorded proximity sensor data 228. From the comparison, the device controller 116 identifies a characteristic of the passenger 210, including a tool accompanying the passenger 210, that indicates that the passenger 210 needs assistance.

[0048] As shown in block 320A2, when determining that the passenger 210 needs assistance to locate the call station 211 in the lobby 125A, the method includes applying, by the device controller 116, the proximity sensor data 228 to a trained machine learning model. By applying the model 119, the device controller 116 can identify a characteristic of the passenger 210, including a tool accompanying the passenger 210, that indicates that the passenger 210 needs assistance. The model 119 can be trained on legacy data in which such characteristics are identified.

[0049] As shown in block 320B, when determining that the passenger 210 needs assistance to locate the call station 211 in the lobby 125A, the method includes identifying, by the device controller 116 from the proximity sensor data 228, a characteristic of the passenger 210 being a guide dog or cane 232 or the passenger meandering in the lobby 125A. The characteristic of meandering includes, for example, the passenger 210 moving within the lobby 125A at a speed 233 below a threshold value or moving along one or more predetermined paths 234 near the call station 211 without utilizing the call station 211.

[0050] As shown in block 330, the method includes emitting, by a speaker 227 operatively coupled to the device controller 116 and located in the lobby 125A, an audible sound 223 to direct the passenger 210 to the call station 211. As shown in block 330A, when emitting the audible sound 223 to direct the passenger 210 to the call station 211, the method includes emitting, by the speaker 227, a verbal indication to the call station 211 relative to a current location of the passenger 210 and / or emitting, by the speaker 227, a “ping” sound that increases in one or more of amplitude and frequency as the passenger 210 moves closer to the call station 211.

[0051] As shown in block 340, the method includes the system 119 detecting the passenger 210 moving to the elevator car 103 on the hall 125A and controlling the elevator car 103 to remain on the hall 125A with the door of the elevator car 103 open until the passenger 210 has boarded.

[0052] As indicated, embodiments provide a proximity sensor 226, which can be a camera or a motion detection device, with a loudspeaker 227 configured to emit an audible sound, which can be a low decibel audible sound, when a passenger 210 in need of assistance is detected. The proximity sensor 226 is fixed in a position in close proximity to the call station 211 or integrated on the call station 211. Machine learning can be implemented such that the device controller 116 in communication with the proximity sensor 226 is configured to detect tools accompanying the passenger 210, such as a cane 232 and a guide dog 231, and emit the audible sound 223 when these tools are present. Furthermore, a machine learning model 229 can be trained to detect a passenger 210 in need of assistance by analyzing motion patterns and behavior, including but not limited to its speed 233 and movement path 234 compared to training data representing historical passenger activity. Depending on the behavior of the passenger 210, the volume of the audible sound 223 can be increased and / or detailed verbal instructions can be provided to guide the passenger 210 to the call station 211.

[0053] With embodiments, a passenger 210 who is blind or visually impaired can more easily find a call station 211 at an elevator group 104. Such a solution can be improved within existing systems to increase accessibility without requiring a more extensive upgrade to the interface of the call station 211.

[0054] With respect to the implementation of artificial intelligence (AI) identified herein, explicitly or inherently, machine learning models, e.g., part of an artificial intelligence (AI) system, can be utilized in embodiments. AI systems use digital computers or machines controlled by digital computers to simulate human intelligence to sense the environment, e.g., using available proximity sensors including speed, acceleration, vibration, sound, video, etc., and acquire knowledge and use that knowledge to obtain optimal results. AI infrastructure includes technologies such as proximity sensors, specialized artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Some implementations of AI according to embodiments utilize computer vision technology, speech processing technology, natural language processing technology, machine learning / deep learning, etc.

[0055] Some implementations of the AI according to embodiments utilize a pre-trained (PT) machine translation model that employs a neural network-based sequence-to-sequence (sequence-to-sequence or S-S) framework. The S-S framework is a framework that includes an encoder-decoder structure. The encoder-decoder structure converts an input sequence into another sequence output. In this framework, the encoder converts the input sequence into a vector, and the decoder takes the vector and generates the output sequence in a time sequence. The encoder and the decoder can utilize the same type of neural network model, or can utilize different types of neural network models. The neural network model can be a CNN (convolutional neural network) model, an RNN (recurrent neural network) model, a long short-term memory (LSTM) model, a delay network model, a gated CNN model, etc.

[0056] Once trained, the trained machine learning model can analyze input data and predict and / or characterize features included in the sensed data in one or more aspects. In the case of video, in one non-limiting example, the sensed data can include sequential images and / or encoded video data (e.g., using a digital video file / stream format and / or codec, such as MP4, MOV, AVI, WEBM, AVCHD, OGG, etc., including combinations and / or multiples thereof). The prediction and / or characterization of features can include segmenting the video data. In some instances, the one or more trained machine learning models include or are associated with pre-processing or augmentation performed prior to segmenting the video data (e.g., intensity normalization, resizing, cropping, etc., including combinations and / or multiples thereof). The output of the one or more trained machine learning models can include predictions of aspects of the video data, localization and / or location of aspects within the video data, and / or states of aspects. The localization can be a set of coordinates in an image / frame in the video data. In one or more examples, the trained machine learning model is trained to perform higher level predictions and tracking.

[0057] By analyzing proximity sensor data captured while the device is in use and applying that data to a trained machine learning model, similar predictions can be made about the operational state of the device. For example, using service learning techniques, a model is trained on known inputs and outputs from legacy events to predict future outputs from future inputs. The model can be evaluated such that variables can be weighted or re-weighted to more accurately correlate inputs and outputs, and the model can be retrained as more inputs and outputs are collected. For example, predictions of the state of multiple devices of an operatively integrated system of devices can be obtained using a trained model. Data can be captured for one of the devices (or less than all), including operational sounds, vibrations, etc., and the captured data can be run through a trained model trained to identify the impact (constructive and destructive) of the devices on each other in their respective operational states, including when they are functioning within and outside of acceptable tolerances.

[0058] The wireless connections identified above can apply protocols including local area network (LAN, or WLAN for wireless LAN) protocols and / or private area network (PAN) protocols. LAN protocols include WiFi technology based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards. PAN protocols include, for example, Bluetooth Low Energy (BTLE), which is a wireless technology standard designed and marketed by the Bluetooth Special Interest Group (SIG) for exchanging data over short distances using short-wavelength UHF radio waves from a variety of personal area networks. PAN protocols also include Zigbee, a technology based on the IEEE 802.15.4 protocol, representing a set of high-level communication protocols designed to create personal area networks with small, low-power digital wireless equipment for low-power low-bandwidth needs. Such protocols also include Z-Wave, which is a wireless communication protocol supported by the Z-Wave Alliance that uses a mesh network to apply low-energy radio waves to communicate between devices such as appliances, allowing them to be controlled wirelessly.

[0059] Other applicable protocols include Low Power WAN (LPWAN), which is a wireless wide area network (WAN) designed to allow long-range communication at low bit rates to enable terminal devices to operate for extended periods of time (years) using battery power. Long Range WAN (LoRaWAN) is a type of LPWAN maintained by the LoRa Alliance, and is a media access control (MAC) layer protocol for the transport of management and application messages between a network server and an application server, respectively. Such wireless connections can also include Radio Frequency Identification (RFID) technology, which is used for communication with an integrated chip (IC) on, for example, an RFID smart card. Further, Sub-1Ghz RF devices operate in the ISM (Industrial, Scientific and Medical) spectrum band below Sub 1Ghz (typically in the 769-935 MHz, 315 Mhz, and 468 Mhz frequency ranges). This spectrum band below 1Ghz is particularly useful for RF IOT (Internet of Things) applications. Other LPWAN-IOT technologies include Narrow Band Internet of Things (NB-IOT) and M1 Category Internet of Things (Cat M1-IOT). Wireless communication for the disclosed systems can include cellular, such as 2G / 3G / 4G (and the like). The above is not intended to limit the scope of applicable wireless technologies.

[0060] The wired connections identified above can include connections (cables / interfaces) under RS (Recommended Standard)-422, also known as TIA / EIA-422, which is a technical standard supported by the Telecommunications Industry Association (TIA) and initiated by the Electronic Industries Alliance (EIA) that specifies electrical characteristics of digital signaling circuits. The wired connections can also include connections (cables / interfaces) under RS-232 standard that formally defines the signal connections between DTEs (Data Terminal Equipments) such as computer terminals and DCEs (Data Circuit-terminating Equipments or Data Communications Equipments) such as modems for serial communication transmission of data. The wired connections can also include connections (cables / interfaces) under Modbus serial communication protocol managed by the Modbus Organization. Modbus is a server / client protocol designed for use by programmable logic controller 116s (PLCs) and is a commonly available means of connecting industrial electronics. The wireless connections can also include connectors (cables / interfaces) under PROFibus (Process Field Bus) standard managed by PROFIBUS & PROFINET International (PI). PROFibus is a standard for fieldbus communication in automation technology, published as part of IEC (International Electrotechnical Commission) 61158. The wired communication can also be through a device controller 116 area network (CAN) bus. CAN is a vehicle bus standard that allows micro device controllers 116s and devices to communicate with each other in applications without a host computer. CAN is a message-based protocol published by the International Organization for Standardization (ISO). The above is not intended to limit the scope of applicable wired technologies.

[0061] As indicated, when data is transmitted over a network between terminal processors, the data can be transmitted in raw form, or can be processed in whole or in part at any of the terminal processors or intermediate processors, such as at a cloud service or other processor. The data can be parsed, partially or completely processed or compiled at any of the processors, and then can be stitched together or maintained as separate packets of information.

[0062] Each processor identified herein can be, but is not limited to, a single processor or multi-processor system of any of a variety of possible architectures including field programmable gate array (FPGA), central processing unit (CPU), application specific integrated circuit (ASIC), digital signal processor (DSP), or graphics processing unit (GPU) hardware arranged homogenously or heterogeneously. Memory identified herein can be, but is not limited to, random access memory (RAM), read only memory (ROM), or other electronic, optical, magnetic or any other computer readable medium. Embodiments can take the form of processor- implemented processes and apparatuses for practicing those processes, such as a processor. Embodiments can also take the form of computer program code containing instructions embodied in tangible media such as floppy diskettes, CD ROMs, hard drives, or any other non-transitory computer-readable medium, firmware carried in the memory of processors, or carried on any other non-transitory computer- readable medium, where, when the computer program code is loaded into a computer and executed, it becomes an apparatus for practicing an embodiment. The embodiments also can take the form of computer program code, for example, whether stored in a storage medium, loaded into and / or executed by a computer, or transmitted over some transmission medium, loaded into and / or executed by a computer, or transmitted over some transmission medium, where, again, the computer program code is executed by a computer to cause a computer to perform any of the steps described herein. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.

[0063] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The term "about" is intended to encompass a degree of error and / or variation consistent with measurements and / or manufacturing tolerances of the devices used at the time of filing the application. As used herein, the singular forms "a", "an" and "the" are intended to include plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

Claims

1. An elevator system, comprising: The call station is located in the lobby; Device controller; A proximity sensor, operatively coupled to the device controller, located in the hall, and configured to generate proximity sensor data for use by the device controller. A loudspeaker, operatively coupled to the device controller and located in the hall. Based on the proximity sensor data, the device controller is configured to: Detecting the presence of passengers; Determining that the passenger needs assistance locating the call station in the lobby; and An audible sound is emitted from the speaker to guide the passenger to the call station.

2. The system according to claim 1, wherein, The system is configured to detect the passenger moving toward the elevator car in the lobby and control the elevator car to remain in the lobby, with the elevator doors open until the passenger has boarded the elevator car.

3. The system according to claim 1, wherein, The call station includes the device controller, the proximity sensor, and the speaker.

4. The system according to claim 1, wherein, The proximity sensor is one or more of the following: Video proximity sensor; motion detector; LIDAR; or RADAR.

5. The system according to claim 1, wherein, The device controller is configured to: The proximity sensor data is compared with pre-recorded proximity sensor data to identify characteristics of the passenger, including the vehicle accompanying the passenger, thereby determining that the passenger needs assistance to locate the call station in the lobby.

6. The system according to claim 1, wherein, The device controller is configured to: The proximity sensor data is applied to a trained machine learning model to identify characteristics of the passenger, including the vehicle accompanying the passenger, thereby determining that the passenger needs assistance to locate the call station in the lobby.

7. The system according to claim 5, wherein, The device controller is configured to determine, using the proximity sensor data, that the passenger needs assistance locating the call station in the lobby in the following manner: Identify the characteristics of the guide dog accompanying the passenger; and / or The characteristics of the cane accompanying the passenger are identified.

8. The system according to claim 5, wherein, The device controller is configured to use the proximity sensor data to identify the characteristics of the passenger loitering in the lobby, and thereby determine that the passenger needs assistance to locate the call station in the lobby.

9. The system according to claim 8, wherein, The device controller is configured to determine, using the proximity sensor data, that a passenger is loitering in the lobby in the following way: It is determined that the passenger is moving within the hall at a speed below a threshold; or It is determined that the passenger is moving along one or more predetermined paths near the call station without utilizing the call station.

10. The system according to claim 9, wherein, The audible sounds include: Verbal instructions to the call station relative to the passenger's current location; and / or As the passenger moves closer to the call station, one or more "ping" sounds are added in amplitude and frequency.

11. A method for controlling an elevator system, comprising: The presence of a passenger is detected by a device controller that receives proximity sensor data from a proximity sensor operatively coupled to the device controller and located in the lobby; The device controller determines that the passenger needs assistance to locate the call station in the lobby; as well as An audible sound is emitted by a speaker to guide the passenger to the call station, the speaker being operatively coupled to the device controller and located in the lobby.

12. The method of claim 11, further comprising the system detecting the passenger moving toward an elevator car in the lobby and controlling the elevator car to remain in the lobby, wherein the elevator doors are open until the passenger has boarded the elevator car.

13. The method according to claim 11, wherein, The call station includes the device controller, the proximity sensor, and the speaker.

14. The method according to claim 11, wherein, The proximity sensor is one or more of the following: Video proximity sensor; motion detector; LIDAR; or RADAR.

15. The method according to claim 11, wherein, When it is determined that the passenger needs assistance to locate the call station in the lobby, the method includes: The device controller compares the proximity sensor data with pre-recorded proximity sensor data to identify the characteristics of the passenger, including the vehicle accompanying the passenger.

16. The method according to claim 11, wherein, When it is determined that the passenger needs assistance to locate the call station in the lobby, the method includes: The proximity sensor data is applied by the device controller to a trained machine learning model to identify characteristics of the passenger, including the vehicle accompanying the passenger, thereby determining that the passenger needs assistance to locate the call station in the lobby.

17. The method according to claim 15, wherein, When it is determined that the passenger needs assistance to locate the call station in the lobby, the method includes: The device controller identifies the characteristics of the guide dog accompanying the passenger based on the proximity sensor data; and / or The device controller identifies the characteristics of the cane accompanying the passenger based on the proximity sensor data.

18. The method according to claim 15, wherein, When it is determined that the passenger needs assistance to locate the call station in the lobby, the method includes: The device controller identifies the characteristics of the passenger loitering in the hall based on the proximity sensor data.

19. The method according to claim 18, wherein, When it is determined that the passenger is loitering in the lobby, the method includes: The device controller determines, based on the proximity sensor data, that the passenger is moving within the hall at a speed below a threshold; or The device controller determines, based on the proximity sensor data, that the passenger is moving along one or more predetermined patterns near the call station without utilizing it.

20. The method according to claim 19, wherein, When the audible sound is emitted to guide the passenger to the call station, the method includes: The speaker issues a verbal instruction to the call station relative to the passenger's current location; and / or The speaker emits a "ping" sound with one or more increases in amplitude and frequency as the passenger moves closer to the call station.

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