A method for determining an allocation decision for at least one elevator includes using an existing calls in an elevator system as a first input in a machine learning module, processing the first input with the machine learning module to provide a first output comprising a first allocation decision, using the first output as a second input in an iterative module, processing the second input with the iterative module to provide a second ouput comprising a second allocation decision, and providing the second allocation decision to an elevator control module and to an allocation decision storage for further machine learning module training.

An elevator system (2) comprises a hoistway (4) extending between a plurality of landings (8a, 8b, 8c); an elevator car (60) configured for moving along the hoistway (4) between the plurality of landings (8a, 8b, 8c); a load/weight sensor (44) configured for detecting the load of the elevator car (60); a speed detector (34) configured for detecting the speed of the elevator car (60); and an elevator safety system. The elevator safety system comprises a safety gear (20) configured for stopping, upon activation, any movement of the elevator car (60); and an electronic safety controller (30) configured for activating the safety gear (20) when the detected speed of the elevator car (60) exceeds a set speed limit. The electronic safety controller (30) is configured for setting the speed limit as a function of the load detected by the load/weight sensor (44).

An elevator system (2) comprises a hoistway (4) extending between a plurality of landings (8a, 8b, 8c); an elevator car (60) configured for moving along the hoistway (4) between the plurality of landings (8a, 8b, 8c); a load/weight sensor (44) configured for detecting the load of the elevator car (60); a speed detector (34) configured for detecting the speed of the elevator car (60); and an elevator safety system. The elevator safety system comprises a safety gear (20) configured for stopping, upon activation, any movement of the elevator car (60); and an electronic safety controller (30) configured for activating the safety gear (20) when the detected speed of the elevator car (60) exceeds a set speed limit. The electronic safety controller (30) is configured for setting the speed limit as a function of the load detected by the load/weight sensor (44).

An elevator system having elevator cars in a building, the system having: a first elevator car of the elevator cars configured to execute a self-diagnostic routine, wherein the first elevator car is configured to: instruct a subset of the elevator cars to enter an idle mode and analyze data shared by the first elevator car; process the operational data among the subset of the elevator cars; process the operational data among the subset of the elevator cars; collect operational data; share the operational data among the subset of the elevator cars; receive from the subset of the elevator cars an analysis of the operational data that is indicative of an operational state of the first elevator car; determine that a fault condition exists when the operational state is outside a threshold; and automatically execute a predetermined response upon when the first elevator car determines that the fault condition exists.

The present disclosure provides a system and a method for controlling motion of a bank of elevators. The method includes accepting current requests for service by the bank of elevators, accepting a partial trajectory of a motion of a person moving in an environment serviced by the bank of elevators, and obtaining a probability of a future elevator request. The method further includes processing the partial trajectory with a neural network trained to estimate a weighted combination of probability density functions that indicates an arrival time distribution of the person, and generating a set of possible future requests jointly representing the probability of the future elevator request and the arrival time distribution. The method further includes optimizing a schedule of the bank of elevators to serve the current requests and the set of possible future requests, and controlling the bank of elevators according to the schedule.

A method for controlling an elevator where an elevator is allocated for the use of a passenger in a first optimization phase in such a way that a first cost function is minimized, a second optimization phase is performed, in which the route of the allocated elevator is optimized in such a way that a second cost function is minimized.

According to an aspect, there is provided a method and an apparatus for determining an allocation decision for at least one elevator. In the solution existing calls are used in an elevator system as a first input in a machine learning module. The first input is processed with the machine learning module to provide a first output comprising a first allocation decision. The first output is then used as a second input in an iterative module. The second input is processed with the iterative module to provide a second output comprising a second allocation decision. The second allocation decision is provided to an elevator control module and to an allocation decision storage for further machine learning module training.

An elevator system includes a group control device (12) and a destination call registration device (9). The group control device (12) performs group control of a plurality of elevator devices. Each of the elevator devices includes a plurality of cars which ascend and descend in the same shaft. The destination call registration device (9) is installed in an elevator hall. Moreover, the group control device (12) includes a first determination unit (15) and an assigned car determination unit (18). The first determination unit (15) tentatively assigns a call newly registered from the destination call registration device (9) to a first car, and excludes the first car from candidate cars of a first group in a case where a second car arranged above or below the first car stops at the hall before the first car. The assigned car determination unit (18) selects a car to which the new registration call is assigned from the candidate cars of the first group.

A method for training neural networks to assign calls to elevator cars simulates an environment in which first and second cars move between building floors in reaction to calls indicating desired floors, each simulation including steps: determining a current state of the environment including a current position of each car, a list of current calls and a new call; inputting first and second input data encoding at least a part of the current state into respective first and second neural networks each configured to convert the input data into output values indicating a probability and/or tendency for the cars to be assigned to the new call; determining a selected car using the output values; assigning the new call to the selected car, and determining reward values quantifying a usefulness of the assignment; training the neural networks using past simulation reward values to increase the usefulness of future assignments.

An elevator has a plurality of cars movable along different vertical axes between building floors and a sensor system providing sensor data indicative of the elevator current state. An elevator control method includes: receiving the sensor data including current position of each car relative to the floors, list of assigned current calls and a new call for assignment each indicating a destination floor; generating a list of eligible cars using the sensor data and at least one rule with which each car should comply when fulfilling the new call; inputting the sensor data as input data into an artificial neural network trained to convert the input data into one output value for each car indicating a probability and/or tendency for assignment of the car to the new call; determining one of the eligible cars as a selected car using the output values; assigning the new call to the selected car.