Patent Description:
Elevator cars may be controlled to skip new floor call(s) upon reaching an occupational capacity limit. Reduced capacity limit may be based on a number a number of calls. However, passengers may avoid entering an elevator car even when it is below the reduced capacity limit, reducing overall system efficiency. <CIT> discloses a technique for managing the operation of an elevator according to the situation in a car.

According to a first aspect of the present invention there is provided an elevator system as claimed in claim <NUM>.

In some embodiments, the controller, the first sensor and the second sensor are configured to communicate with each other over a wireless network.

In some embodiments, the controller is configured to determine the reduced capacity limit by applying a predetermined multiplier to the capacity parameter.

In some embodiments, the capacity parameter further includes one or more of time of day, season, geographic location, occupancy type and building utilization.

In some embodiments, the occupancy type is one or more of cargo and passenger.

In some embodiments, the controller is configured to control the elevator car to disregard calls for service when the elevator car is at or above the reduced capacity limit.

In some embodiments, the first sensor is located onboard the elevator car and is configured to communicate with the controller directly or via a cloud service, and the first sensor data is processed in whole or part at one or more of the first sensor, the cloud service and the controller.

In some embodiments, one or more of passenger count, the volume of available space and volume of occupied space is derived from processing the first sensor data.

In some embodiments, the second sensor is onboard the elevator car or located at the landing; and the second sensor communicates with the controller directly or via the cloud service, and the second sensor data is processed in whole or part at one or more of the second sensor, the cloud service and the controller.

In some embodiments, the second sensor is a motion sensor or depth sensor located on the landing.

In some embodiments, when determining the reduced capacity limit, controller is configured for: determining whether passengers enter the elevator car in response to hall calls; and, upon determining that passengers enter the elevator in response to hall calls, increasing the reduced capacity limit by half the tolerance range to an upper capacity tolerance, otherwise decreasing the reduced capacity limit by half the tolerance range to a lower capacity tolerance.

According to a second aspect there is provided a method of controlling an elevator car of an elevator system with a controller that is operationally connected to the elevator car, as claimed in claim <NUM>.

In some embodiments, the controller, the first sensor and the second sensor communicate with each other over a wireless network.

In some embodiments, determining the reduced capacity limit includes applying a predetermined multiplier to the capacity parameter.

In some embodiments, the occupancy type is one or more of cargo and passengers.

In some embodiments, the method further includes: controlling the elevator car, by the controller, to disregard calls for service when the elevator car is at or above the reduced capacity limit.

In some embodiments, the first sensor is located onboard the elevator car; and the method includes: communicating, between the controller and the first sensor, directly or via a cloud service, and processing the first sensor data in whole or part at one or more of the first sensor, the cloud service and the controller.

In some embodiments, the method further includes: determining, from the first sensor data, one or more of passenger count, the volume of available space and volume of occupied space.

In some embodiments, the second sensor is onboard the elevator car or located at the landing; and the method further includes: communicating, between the second sensor and the controller, directly or via the cloud service, and processing the second sensor data in whole or part at one or more of the second sensor, the cloud service and the controller.

In some embodiments, the second sensor includes a motion sensor or depth sensor located on the landing.

In some embodiments, when determining the reduce capacity limit, the method includes the controller: determining whether passengers enter the elevator car in response to hall calls; and, upon determining that passengers enter the elevator in response to hall calls, increasing the reduced capacity limit by half the tolerance range to an upper capacity tolerance, otherwise decreasing the reduced capacity limit by half the tolerance range to a lower capacity tolerance.

<FIG> is a perspective view of an elevator system <NUM> including an elevator car <NUM>, a counterweight <NUM>, a tension member <NUM>, a guide rail (or rail system) <NUM>, a machine (or machine system) <NUM>, a position reference system <NUM>, and an electronic elevator controller (controller) <NUM>. The counterweight <NUM> is configured to balance a load of the elevator car <NUM> and is configured to facilitate movement of the elevator car <NUM> concurrently and in an opposite direction with respect to the counterweight <NUM> within an elevator shaft (or hoistway) <NUM> and along the guide rail <NUM>.

Embodiments may also be employed in ropeless elevator systems using self-propelled elevator cars (e.g., elevator cars equipped with friction wheels, pinch wheels or traction wheels).

For optimizing elevator car dispatching performance, it may be valuable for the system to know the actual available space (volume) or weight in the elevator car (otherwise referred to as a cab). The system may utilize this information to estimate a passenger count or passenger or cargo volume, and to estimate an available space, so as not to assign passengers to a car that they will not enter due to over-crowding. A capacity (or occupancy) limit may be a function of geography, building type (e.g., commercial/residential), time of day, season, etc., as indicated below.

Specifically, turning to <FIG>, the elevator system <NUM> is located in a building <NUM> and includes the elevator car <NUM> that travels in the shaft <NUM> between landings generally labeled <NUM>, and including e.g., first and second landings 125a, 125b, to pick up and drop off passengers <NUM>. The elevator system <NUM> includes the controller <NUM> and the elevator car <NUM> operationally connected to the controller <NUM>. The controller <NUM> may be onboard the elevator car or may be a dispatch controller located remotely as show in <FIG>.

A first sensor <NUM> is configured to provide first sensor data, indicative of current elevator car capacity or occupancy as indicated below, to the controller <NUM>. The first sensor <NUM> may be located in the car <NUM> and may be, for example, a camera, a depth sensor, a floor pressure sensor, etc. Alternatively, the first sensor <NUM> may be located elsewhere. For example, the first sensor <NUM> may be a tension measuring system on hoist rope, illustrated as the tension member <NUM> in <FIG>.

From the first sensor data, the controller <NUM> is configured to identify a capacity parameter (or occupancy parameter) of the elevator car <NUM>. The capacity parameter may represent information about the conditions of the elevator utilization that enable the controller <NUM> to determine a modified or reduced capacity limit. The capacity parameter may be loaded weight, volume of available space or volume of occupied space.

For example, if the first sensor <NUM> is a pressure or weight sensing implement, then the first sensor data may be utilized to identify loaded weight in the elevator car <NUM>. Alternatively, if the first sensor <NUM> is a camera or depth sensor, then the first sensor data may be utilized to identify an occupied volume in the elevator car <NUM>. The first sensor <NUM> may be configured to communicate with the controller <NUM> directly, e.g., via a wired or wireless network connection <NUM> as indicated below, or via a cloud service <NUM>. The first sensor data may be completely or partially processed at the first sensor <NUM> by edge computing, or at the cloud service <NUM> or controller <NUM>. The first sensor data may be transmitted entirely or partially in a raw format and portions of the first sensor data may be stitched together at different processing points along the transmission path between the first sensor <NUM> and the controller <NUM>.

A second sensor <NUM> is configured to provide second sensor data, indicative of passenger activity at a landing <NUM> reached by the car <NUM>, to the controller <NUM>. The second sensor <NUM> may also be camera, depth sensor or floor pressure sensor, located at the landing <NUM>. Alternatively, the second sensor <NUM> may be a light curtain in the elevator door and/or hall door, etc., for example elevator doors <NUM> in <FIG>. In one embodiment, the first sensor is utilized to obtain this information instead of a second sensor.

From the second sensor data, the controller <NUM> is configured to determine that at least one passenger remains outside the elevator car <NUM>, e.g., at the landing <NUM>, when the elevator car <NUM> is stopped at the landing <NUM>, throughout the period that the elevator doors <NUM> are open and when the doors close. For example the controller <NUM> may utilize standard protocols for determining when to open and close the elevator doors <NUM> at the landing <NUM>. The controller <NUM>, with the second sensor data, may then determine that at least one passenger at the landing <NUM> did not board the elevator car <NUM>. This may be a binary determination, e.g., identifying whether or not a passenger is at the landing <NUM> when the doors close. The determination may also account for whether and how many passengers entered and exited the elevator car while the elevator car is at the landing <NUM>. These determinations may account for, e.g., transient changes or differentials between initial and final passenger volume or weight at the landing while the elevator doors are opened. The second sensor <NUM> may be configured to communicate with the controller <NUM> directly, e.g., via the wired or wireless network connection <NUM> as indicated below, or via the cloud service <NUM>. The second sensor data may be completely or partially processed at the second sensor <NUM> by edge computing, or at the cloud service <NUM> or controller <NUM>. The second sensor data may be transmitted entirely or partially in a raw format and portions of the second sensor data may be stitched together at different processing points along the transmission path between the second sensor <NUM> and the controller <NUM>.

Based on the first sensor data and preprogrammed capacity data, the controller <NUM> may determine that the elevator car <NUM> has available capacity for more passengers. However, by also accounting for the second data, the controller <NUM> may determine that the elevator has reached its capacity base on passenger preference. From the first sensor data and the second sensor data, in comparison, the controller <NUM> is configured to determine a reduced capacity limit for the elevator car <NUM> as a function of the capacity parameter (actual loaded weight, occupied or available space). Thus, the controller <NUM> is configured to dynamically modify, e.g., reduce, the capacity limit of the elevator car <NUM> by utilizing the first sensor data and the second sensor data.

According to an embodiment, the controller <NUM> may be configured to determine the reduced capacity limit by applying a predetermined multiplier to the capacity parameter. In an illustrative example, turning to <FIG>, during an elevator rush hour, a reduced capacity limit G1 may be calculated based on a determined capacity parameter G, in which case the car is considered at or about FULL but not OVERLOADED. At block <NUM>, the car <NUM> is already at or about full but not over loaded status. At block <NUM>, someone at a landing presses the hall call in another floor and the car reaches to the floor and the elevator door opens. At block <NUM> a determination is made as to whether no one enters (or whether at least one passenger stays on the landing). If the determination is "no" because passengers enter (and no one stays on the landing) then the controller goes back to block <NUM>. If the determination is "yes" because passengers do not enter (or at least one passenger stays behind), then the capacity parameter for the elevator car <NUM> at that time (e.g., G) is obtained at block <NUM>. The elevator car <NUM> outputs a full signal at block <NUM> and ignores hall calls. The car <NUM> runs to the next car call destination per block <NUM>. When passengers leave the car <NUM>, and the elevator is still outputting a "FULL" signal, there is a determination at block <NUM> of whether the capacity parameter is less than the reduced capacity limit is (G1) is, for example, less than <NUM> (or another reduction multiple) of G. If the determination is "no" at block <NUM> then the elevator remains full per block <NUM> and will only run to car calls, e.g., not hall calls per block <NUM>. If the determination at block <NUM> is 'yes'', then per block <NUM> the operation status returns to normal operation, e.g., the car is not overloaded, even if at or near full. As can be appreciated, the controller <NUM> may be configured to determine that the reduced capacity limit is function (e.g., less than or equal to ninety percent, or other reduction multiple) of a previously programmed or determined reduced capacity limit instead of being a function of the then-measured capacity parameter.

As indicated above, the capacity parameter represents information about the conditions of the elevator utilization that enable the controller to determine a modified or reduced capacity limit. According to an embodiment, the capacity parameter may also include time of day, season, geographic location, occupancy type and building utilization. According to an embodiment, the occupancy type may be one or more of cargo, passenger and robot, which may be a cleaning bot or other robot. That is, the embodiments may consider more than merely the available space by weight or volume. Depending on the other identified variables, the system is able to more robustly identify when passengers in certain cohorts or passengers subject to certain environmental conditions may statistically feel the elevator car is too crowded to enter. Such embodiments may be configured to learn the practical limits which, even for the same-sized cab, varies geographically and by usage of the building (e.g. student dorm vs hospital). Thus, depending on these conditions, the controller <NUM> may reduce the capacity limit by a predetermined reduction multiple without first going through the process shown in <FIG>. Instead, the process shown in <FIG> may be utilized to fine-tune the reduced capacity limit that has been otherwise modified (reduced) based on time of day, season, geographic location, occupancy type and building utilization.

According to an embodiment, as indicated, the controller <NUM> may be configured to utilize the reduced capacity limit and control the elevator car <NUM> to disregard (e.g., not answer or bypass) calls for service (hall calls) during such times that the elevator car <NUM> is at or above the reduced capacity limit.

According to an embodiment, as indicated, the first sensor <NUM> may be onboard the elevator car <NUM> and may be configured to communicate with the controller <NUM> directly, e.g., via the wired or wireless network connection <NUM> as indicated below, or via the cloud service <NUM>. The first sensor data may be processed in whole or part at one or more of the first sensor <NUM>, the cloud service <NUM> and the controller <NUM>. Processed portions may be stitched together at the controller <NUM> for form compiled data. According to an embodiment, one or more of passenger count, volume of available space or volume of occupied space may be derived from processing the first sensor data.

According to an embodiment, the second sensor <NUM> may be onboard the elevator car <NUM> or located at the landing <NUM>. The second sensor <NUM> may communicate with the controller <NUM>, directly or via the cloud service <NUM>. The second sensor data may be processed in whole or part at one or more of the second sensor, the cloud service <NUM> and the controller <NUM>. Processed portions may be stitched together at the controller <NUM> for form compiled data. According to an embodiment, the second sensor <NUM> may be a motion sensor or depth sensor located onboard the elevator car <NUM>, such as in the elevator doors <NUM>, or on the landing. The second sensor <NUM> may also be camera, depth sensor or floor pressure sensor, located at the landing <NUM>. Alternatively, the second sensor <NUM> may be a light curtain in the elevator door and/or hall door, etc., for example elevator doors <NUM> in <FIG>. The depth sensor may be configured to detect shapes of people, which the controller <NUM> identifies as people waiting at the landing.

The above embodiments provide for the system to self-learn the effective load limit. The system detects cases when at least one passenger does not board the car, utilizing for example volume sensors both in the car and the hall so the system can sense if some passengers were left behind in the hall. By detecting at substantially every boarding instance (i.e., hall call) whether or not at least one person is left behind, the system can build a probability curve <NUM> shown in <FIG>, which graphs the probably of passengers entering the elevator car (Y axis) based on, for example, the current passenger count or measured volume (used or available) (X axis) in the elevator car.

The system goal is to learn approximately where the curve takes a sharp downward, e.g., the learned limit (vertical line <NUM>), which is the reduced capacity limit, which may represent a <NUM>% boarding probability. When the car <NUM> is lightly loaded (left side <NUM> of graph <NUM>), there is a high probability that someone will board the car. As the car becomes fuller, the probability decreases. The goal may be to have passengers board the elevator car <NUM>% of the time a hall call is answered.

With each complete boarding the system may adjust the curve rightwards to an upper capacity tolerance or upper limit <NUM> to increase the reduced capacity limit. In addition, with each incomplete boarding, the system may adjust the curve leftwards by substantially the same amount as adjusted rightwards to a lower capacity tolerance or lower limit <NUM> to decrease the reduced capacity limit. The amount of adjusting to the left or right of the learned limit <NUM> may define an adjustment range or tolerance range <NUM> that may itself be learned and modified over time so that minimal overall adjustments are required to obtain the <NUM>% (or thereabout) boarding probability. If the differential size of the tolerance range <NUM>, between the reduced capacity limits <NUM>/<NUM>, is too large, then the probability of a passenger boarding may drop to an unacceptable level, in which case the tolerance range <NUM> may be made smaller. If the boarding probability goes too far above <NUM>%, the elevator may not be carrying enough passengers, which is also undesirable, in which case the tolerance range <NUM> may be made larger and/or the learned limit <NUM> may shift to increase or decrease the reduced capacity limit. The size of the tolerance range <NUM> may initially be +/-<NUM>% of the boarding probability. Adjustments to the tolerance range <NUM> and movement of the learned limit <NUM> may be in increments of single percentages of the boarding probability, as one example. It should be appreciated that the boarding probability and tolerance range identified herein are only exemplary, and the true values for each could be higher or lower than those identified herein.

Further disclosed is a method of controlling an elevator car <NUM> of an elevator system <NUM> with a controller <NUM> that is operationally connected to the elevator car <NUM>. Referring to <FIG>, as shown in block <NUM>, the method includes identifying, at the controller <NUM> from first sensor data communicated via a first sensor <NUM>, a capacity parameter of the elevator car <NUM>. As indicated, the capacity parameter may be: loaded weight; volume of available space; or volume of occupied space. In one embodiment, the capacity parameter may further include one or more of time of day, season, geographic location, occupancy type and building utilization. The occupancy type may be one or more of cargo and passengers. Thus, depending on these conditions, the controller <NUM> may reduce the capacity limit by a predetermined reduction multiple without first going through the process shown in <FIG>. Instead, the process shown in <FIG> may be utilized to fine-tune the capacity limit that has been otherwise modified (reduced) based on time of day, season, geographic location, occupancy type and building utilization. As shown in block <NUM> the method includes determining, at the controller <NUM> from second sensor data communicated via a second sensor <NUM>, that passengers remain outside the elevator car <NUM> when the elevator car <NUM> is stopped at a landing and its elevator doors <NUM> are open. In one embodiment, the controller <NUM>, the first sensor <NUM> and the second sensor <NUM> communicate with each other over a wireless network <NUM> of the type identified below. As shown in block <NUM>, the method includes determining, at the controller <NUM> from the first sensor data and the second sensor data, a reduced capacity limit (e.g., relative to a design maximum capacity limit or previously determined reduce capacity limit) for the elevator car <NUM> as a function of the capacity parameter. For example, the capacity parameter may be a measured weight when people are not entering the elevator and the capacity limit may be a predetermined reduction multiple of the capacity parameter. In one embodiment, as shown in block <NUM>, the method may include controlling the elevator car <NUM>, by the controller <NUM>, to disregard calls for service when the elevator car <NUM> is at or above the reduced capacity limit. In some embodiments, the system may continue to run to hall calls as long as at least one person boards the elevator at a last hall call.

As shown in <FIG>, in one embodiment, block <NUM> may be further defined by block 510A1, which identifies that the method may include communicating, between the controller <NUM> and the first sensor <NUM>, that may be onboard the elevator car <NUM>, directly or via a cloud service <NUM>. As shown in block 510A2, the method may include processing the first sensor data, in whole or in-part, at one or more of the first sensor <NUM>, the cloud service <NUM> and the controller <NUM>. Processed portions may be stitched together at the controller <NUM> for form compiled data. As shown in block 510A3, the method may include determining, from the first sensor data, one or more of passenger count, volume of available space and volume of occupied space.

With reference to <FIG>, as indicated, the second sensor <NUM> may be onboard the elevator car <NUM> or located at the landing <NUM>. In one embodiment, block <NUM> may be further defined by block 520A1, which identifies that the method may include communicating between the second sensor <NUM> and the controller <NUM> directly or via the cloud service <NUM>. As shown in block 520A2, the method may include processing the second sensor data in whole or part at one or more of the second sensor <NUM>, the cloud service <NUM> and the controller <NUM>. Processed portions may be stitched together at the controller <NUM> for form compiled data. In one embodiment the second sensor <NUM> may be a motion sensor or depth sensor located onboard the elevator car <NUM>, or on the landing <NUM>.

As shown in <FIG>, in one embodiment, block <NUM> may be further defined by block 530A1, which identifies that the method may include determining, by the controller <NUM>, the reduced capacity limit by applying a predetermined multiplier to the capacity parameter. In one non-limiting example, the method may include determining, by the controller <NUM>, that the reduced capacity limit may be less than or equal to ninety percent (or other reduction multiple) of a previously programed or determined capacity limit. <FIG> shows another embodiment for defining or expanding upon block <NUM> based on the discussion related to <FIG>, above. As shown in block 530B1, the method includes the controller <NUM> accumulating data related to passengers entering the elevator <NUM> in response to hall calls while the elevator car <NUM> is near its design capacity limit or previously determined reduced capacity limit (or other selected capacity limit). As shown in block 530B2, the method includes the controller <NUM> setting a reduced capacity limit that correlates to a <NUM>% (or other percentage) boarding probability that a passenger will enter the car <NUM>. The method includes the controller <NUM> setting a tolerance range around the reduced capacity limit. The tolerance range may be +/-<NUM> % (or other percentage) of the boarding probability. As shown in block 530B3, the method may include the controller determining, at each hall call to which it responds, whether passengers enter the elevator car <NUM>. If they do (yes at 530B3) then as shown in block 530B4 the method may include the controller <NUM> increasing the reduced capacity limit by half the tolerance range to an upper capacity tolerance. Otherwise (no at 530B3) as shown in block 530B5 the method may include the controller <NUM> decreasing the reduced capacity limit by half the tolerance range to a lower capacity tolerance. At block 530B6 the method includes determining whether the boarding probability is within acceptable limits over time, such as hours or days (as non-limiting examples). If so (yes at 530B6) then the controller <NUM> may return to block <NUM>. Otherwise (no at 530B6) the controller <NUM>, at block 530B7, may modify one or both of the tolerance range (making it larger or smaller) and the reduced capacity limit (increasing or decreasing the limit).

In the above embodiments, the system <NUM> may utilize an actual available capacity in the elevator car <NUM> to determine a reduced capacity limit based on a number of passengers, cargo including luggage, time of day, season, geographic location, elevator use, etc. This reduced capacity limit should avoid a condition in which passengers avoid entering the elevator at a landing because it is perceived to be overcrowded. Thus, the system <NUM> may utilize various types of information to learn the reduced capacity limit, which, even for a same-sized elevator car <NUM> located elsewhere, varies culturally, geographically and by usage of the building. For example, passengers in one geographic location, such as a crowded city or densely populated university, may accept a more densely packed elevator car, while those in more rural areas or hospitals may expect a less packed elevator car. In addition to measuring the volume or passenger count inside the cab, in order to learn a reduced capacity limit, the system <NUM> may utilize sensors to detect when passengers in the hallway (or landing) decide not to board the elevator car <NUM>. When passengers do not enter, a practical upper limit to the capacity may be determined, which may be less than a predetermined or preprogrammed capacity limit.

A reduced capacity (e.g., a practical capacity) limit may also be determined by detecting and accounting for occupant types that are given a wider berth, e.g., a robot, an impaired person or a person with supportive machinery. For example, certain cohorts of passenger may be more or less comfortable about riding in elevators with robots as cleaning implements or otherwise. Capacity limits may be based on a time of day, e.g., people may be more willing to squeeze into an elevator during rush hour in an office building. As a further practical example, during a winter rush hour, a capacity limit based on load may be different than a summer rush hour due to the size of bulky clothing. Thus, over-crowding may occur with less loaded weight. In such a situation, the system may learn to reduce the overloading-weight when, for example, the car reaches a floor call and nobody gets on even though the elevator loading is below a predetermined capacity limit. The system may then adjust the capacity limit to, for example, a fractional percentage (such as ninety percent) of the reduced capacity limit, going forward under the same conditions, including time of day, season, geographic location, and type of elevator utilization, such as an office building. The elevator car <NUM> may thereafter purposely not respond to a floor call when the same conditions are met and the capacity is at or above the reduced capacity limit. Rather in such situations the system may transmit for the elevator car a "FULL" or "NON-STOP" signal to the controller <NUM>, even though the actual weight in the elevator is less than the design capacity limit for loading.

Utilizing this information, the system <NUM> may learn an effective full load limit that may be specific for a given building and may be adapted for variations in the amount of space taken by each call. The above embodiments may improve dispatching performance by maximizing utilization without assigning passengers to an elevator car <NUM> that may be deemed by the passengers to be too full.

Sensor data identified herein may be obtained and processed separately, or simultaneously and stitched together, or a combination thereof, and may be processed in a raw or compiled form. The sensor data may be processed on the sensor (e.g. via edge computing), by controllers identified or implicated herein, on a cloud service, or by a combination of one or more of these computing systems. The sensor may communicate the data via wired or wireless transmission lines, applying one or more protocols as indicated below.

Wireless connections may apply protocols that include local area network (LAN, or WLAN for wireless LAN) protocols. LAN protocols include WiFi technology, based on the Section <NUM> standards from the Institute of Electrical and Electronics Engineers (IEEE). Other applicable protocols include Low Power WAN (LPWAN), which is a wireless wide area network (WAN) designed to allow long-range communications at a low bit rates, to enable end devices to operate for extended periods of time (years) using battery power. Long Range WAN (LoRaWAN) is one type of LPWAN maintained by the LoRa Alliance and is a media access control (MAC) layer protocol for transferring management and application messages between a network server and application server, respectively. LAN and WAN protocols may be generally considered TCP/IP protocols (transmission control protocol/Internet protocol), used to govern the connection of computer systems to the Internet. Wireless connections may also apply protocols that include private area network (PAN) protocols. 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 radio waves. PAN protocols also include Zigbee, a technology based on Section <NUM>. <NUM> protocols from the IEEE, representing a suite of high-level communication protocols used to create personal area networks with small, low-power digital radios for low-power low-bandwidth needs. Such protocols also include Z-Wave, which is a wireless communications protocol supported by the Z-Wave Alliance that uses a mesh network, applying low-energy radio waves to communicate between devices such as appliances, allowing for wireless control of the same.

Wireless connections may also include radio-frequency identification (RFID) technology, used for communicating with an integrated chip (IC), e.g., on an RFID smartcard. In addition, Sub-<NUM> RF equipment operates in the ISM (industrial, scientific and medical) spectrum bands below Sub <NUM> - typically in the <NUM> - <NUM>, <NUM> and the <NUM> frequency range. This spectrum band below <NUM> is particularly useful for RF IOT (internet of things) applications. The Internet of things (IoT) describes the network of physical objects-"things"-that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet. Other LPWAN-IOT technologies include narrowband internet of things (NB-IOT) and Category M1 internet of things (Cat M1-IOT). Wireless communications for the disclosed systems may include cellular, e.g. <NUM>/<NUM>/<NUM> (etc.). Other wireless platforms based on RFID technologies include Near-Field-Communication (NFC), which is a set of communication protocols for low-speed communications, e.g., to exchange date between electronic devices over a short distance. NFC standards are defined by the ISO/IEC (defined below), the NFC Forum and the GSMA (Global System for Mobile Communications) group. The above is not intended on limiting the scope of applicable wireless technologies.

Wired connections may include connections (cables/interfaces) under RS (recommended standard)-<NUM>, also known as the TIA/EIA-<NUM>, which is a technical standard supported by the Telecommunications Industry Association (TIA) and which originated by the Electronic Industries Alliance (EIA) that specifies electrical characteristics of a digital signaling circuit. Wired connections may also include (cables/interfaces) under the RS-<NUM> standard for serial communication transmission of data, which formally defines signals connecting between a DTE (data terminal equipment) such as a computer terminal, and a DCE (data circuit-terminating equipment or data communication equipment), such as a modem. Wired connections may also include connections (cables/interfaces) under the Modbus serial communications protocol, managed by the Modbus Organization. Modbus is a master/slave protocol designed for use with its programmable logic controllers (PLCs) and which is a commonly available means of connecting industrial electronic devices. Wireless connections may also include connectors (cables/interfaces) under the PROFibus (Process Field Bus) standard managed by PROFIBUS & PROFINET International (PI). PROFibus which is a standard for fieldbus communication in automation technology, openly published as part of IEC (International Electrotechnical Commission) <NUM>. Wired communications may also be over a Controller Area Network (CAN) bus. A CAN is a vehicle bus standard that allow microcontrollers and devices to communicate with each other in applications without a host computer. CAN is a message-based protocol released by the International Organization for Standards (ISO). The above is not intended on limiting the scope of applicable wired technologies.

When data is transmitted over a network between end processors as identified herein, the data may be transmitted in raw form or may be processed in whole or part at any one of the end processors or an intermediate processor, e.g., at a cloud service (e.g. where at least a portion of the transmission path is wireless) or other processor. The data may be parsed at any one of the processors, partially or completely processed or compiled, and may then be stitched together or maintained as separate packets of information. Each processor or controller identified herein may be, but is not limited to, a single-processor or multi-processor system of any of a wide array of possible architectures, including field programmable gate array (FPGA), central processing unit (CPU), application specific integrated circuits (ASIC), digital signal processor (DSP) or graphics processing unit (GPU) hardware arranged homogenously or heterogeneously. The memory identified herein may be but is not limited to a random access memory (RAM), read only memory (ROM), or other electronic, optical, magnetic or any other computer readable medium.

The controller may further include, in addition to a processor and nonvolatile memory, one or more input and/or output (I/O) device interface(s) that are communicatively coupled via an onboard (local) interface to communicate among other devices. The onboard interface may include, for example but not limited to, an onboard system bus, including a control bus (for inter-device communications), an address bus (for physical addressing) and a data bus (for transferring data). That is, the system bus may enable the electronic communications between the processor, memory and I/O connections. The I/O connections may also include wired connections and/or wireless connections identified herein. The onboard interface may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers to enable electronic communications. The memory may execute programs, access data, or lookup charts, or a combination of each, in furtherance of its processing, all of which may be stored in advance or received during execution of its processes by other computing devices, e.g., via a cloud service or other network connection identified herein with other processors.

Embodiments can be in the form of processor-implemented processes and devices for practicing those processes, such as processor. Embodiments can also be in the form of computer code based modules, e.g., computer program code (e.g., computer program product) containing instructions embodied in tangible media (e.g., non-transitory computer readable medium), such as floppy diskettes, CD ROMs, hard drives, on processor registers as firmware, or any other non-transitory computer readable medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes a device for practicing the embodiments. Embodiments can also be in 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, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an device for practicing the exemplary embodiments.

Claim 1:
An elevator system (<NUM>) comprising:
a controller (<NUM>);
an elevator car (<NUM>) operationally connected to the controller (<NUM>);
a first sensor (<NUM>) configured to provide first sensor data to the controller (<NUM>), wherein the controller (<NUM>) is configured to identify, from the first sensor data, a capacity parameter of the elevator car (<NUM>), wherein the capacity parameter includes one or more of: loaded weight; volume of available space; or volume of occupied space;
a second sensor (<NUM>) configured to provide second sensor data to the controller (<NUM>), wherein the controller (<NUM>) is configured to determine, from the second sensor data, that passengers remain outside the elevator car (<NUM>) when the elevator car (<NUM>) is stopped at a landing and its elevator doors are open; and
wherein from the first sensor data and the second sensor data, the controller (<NUM>) is configured to determine a reduced capacity limit for the elevator car (<NUM>) as a function of the capacity parameter;
characterized in that when determining the reduced capacity limit, the controller (<NUM>) is configured for:
accumulating data related to passengers entering the elevator in response to hall calls while the elevator car (<NUM>) is near its design capacity limit or previously determined reduced capacity limit;
setting the reduced capacity limit to correlate with a predetermined boarding probability and setting a tolerance range around the reduced capacity limit;
determining whether the boarding probability is within acceptable limits over time; and
upon determining that the boarding probability is outside of acceptable limits over time, modifying one or both of the tolerance range and the reduced capacity limit.