Abstract:
A group-supervising control system for an elevator includes a cage position prediction unit for predicting cage positions after a predetermined time period based on the present positions, a service available time period distribution calculation unit for calculating the time periods until the service is available (predicted arrival times of a cage capable of responding to a hall call earliest) based on the predicted cage positions, and an assignment correction value calculation unit for calculating assignment correction values for correcting assignment estimation values based on the distributions of the time periods until the service is available. Unevenness in the time periods until the service is available with regard to the respective floors is decreased.
Abstract:
A group controller for controlling elevator cars in a building having a plurality of floors includes a traffic and traffic rate estimator for providing fuzzy estimates of traffic and traffic rate; a closed loop fuzzy logic controller for providing a control parameter in response to the fuzzy estimates of traffic and traffic rate and in response to an elevator control system output variable; and an elevator dispatcher for controlling the operation of the elevator cars during single source traffic conditions in response to the control parameter.
Abstract:
A group controller for controlling elevator cars in a building having a plurality of floors includes an elevator dispatcher for controlling the operation of the elevator cars during single source traffic conditions, the elevator dispatcher having a constraint for limiting car assignments in response to the constraint; and an adaptive contraint generator for modifying a value of the constraint in response to an elevator control system output variable. In one embodiment, the group controller includes a traffic and traffic rate estimator for providing fuzzy estimates of traffic and traffic rate; a fuzzy logic controller for providing a control parameter in response to the fuzzy estimates of traffic and traffic rate, the control parameter having a constraint for limiting a value of the control parameter; an adaptive constraint generator for modifying a value of the constraint in response to an elevator control system output variable; and an elevator dispatcher for controlling the operation of the elevator cars during single source traffic conditions in response to the control parameter.
Abstract:
The feature distinguishing part distinguishes feature modes from the traffic volume data detected by the traffic volume detecting part or from the traffic volume data estimated from the detected traffic volume data by the traffic volume estimating part, and the control parameter setting part sets the optimum control parameter according to the distinction results, further the drive controlling part controls the drive of cars on the control parameters. The distinction function constructing part constructs and modifies the distinction function of feature modes by learning prepared plural feature modes or the distinction results of past feature modes, furthermore the control result detecting part detects the control results or the drive results of cars, and corrects the control parameters. The control results or the drive results are exhibited on the user interface, and the control parameters are set and corrected from the outside by referring the results.
Abstract:
A computer controlled elevator system (FIG. 1 ) using prediction methodology to enhance the system's elevator service, having "learning" capabilities to adapt the system to changing building operational characteristics, including signal processing means for computing the "best" prediction model to be used for prediction, the best factoring coefficients for combining real time and historic predictors associated with the best prediction model, the best data and prediction time interval lengths to be used, and the optimal number of look-ahead intervals or steps (for real time predictions) or look-back days (for historic predictions) to the extent applicable to the prediction model, etc. Using the algorithm(s) of the invention the best prediction methodology and associated parameters are selected by running on site simulations based on exemplary values and comparing the prediction results to recorded data indicative of the actual events that have occurred in the system over a past appropriate period of time. That which provides the most accurate predictions, i.e., those with a minimum error as determined by appropriate mathematical models (e.g., sum of the square of the prediction error or sum of absolute error), are thereafter used in the prediction methodology of the system until further evaluations indicate that further changes should be made.
Abstract:
An elevator controlling apparatus of the present invention comprises a plurality of cage call devices for generating information with respect to cage calls from each of a plurality of cages in; a plurality of cage controlling devices which are provided in correspondence with a plurality of elevator cages, which generate information with respect to hall calls and cage traffic information and which control the operation of the elevator cages; a learning device which calculates the total traffic value in each unit time zone on the basis of the cage traffic information so that when the total traffic in a unit time zone is similar to that of an adjacent unit time zone, these time zones are set as the a divided time zone, and when a divided time zone is over a predetermined time, the next divided time zone is set; and an operation controlling device for controlling the plurality of cage controlling devices on the basis of the total traffic for the each unit time zone, the divided time zones, the information with respect to cage calls and the information with respect to hall calls.
Abstract:
A computer based elevator system (FIG. 1) including data "filtering" means evaluating at least part of the system's over-all operational, historic data base, determining when significant traffic density was present in the system and then selecting out such data, saving it in a special data base. Boarding and de-boarding count data is separately processed on a floor-by-floor, time-interval-by-time-interval, sequential basis and evaluated with respect to two base lines (FIGS. 2A and 4)--a first, "end" base line ("E") based on a preset, lower percent of the total floor's population ("F.P."; e.g. E=1% F.P.), and a second, "start" base line ("S") baased on a preset, higher percent of that floor's total population (e.g. S=3% F.P.); and two time frames--a first, minimum time frame ("T.S.") based on the time (e.g. 18 minutes) the values must stay above "S" for significant traffic density to be considered present, and a second, maximum time frame ("T.E.") based on the maximum allowed time the values (which previously met the first percent and time requirements) may go and continuously stay below "E", which, when this time maximum (e.g. 6 minutes) is exceeded, is considered the end of the significant traffic density period for those time intervals. All data that meets those criteria is "filtered" through from the incoming data, producing the blocks of filtered data of FIGS. 3 and 5, representing only that data which had been recorded during significant traffic density conditions.
Abstract:
An elevator system containing a group of elevator cars (1-4) and a group controller (32) having signal processing means (CPU) for controlling the dispatching of the cars from a main floor or lobby (L) in relation to different group parameters. During up-peak conditions, each car is dispatched from the main floor to an individual plurality of contiguous floors, defining a "sector" (SN). Sectors are contiguous, and the number of sectors may be less than the number of cars, and a floor can be assigned to more than one sector. Floors that constitute a sector assigned exclusively to a car are displayed on an indicator (SI) at the lobby. Cars are selected for assignment by grouping floors into sectors and appropriately selecting sectors, so that each elevator car handles more or less an equal predicted traffic volume during varying traffic conditions, resulting in the queue length and waiting time at the lobby being decreased, and the handling capacity of the elevator system increased. Estimation of future traffic flow levels for the various floors for, for example, each five (5) minute interval, are made using traffic levels measured during the past few time intervals on the given day as real time predictors, using a linear exponential smoothing model, and traffic levels measured during similar time intervals on previous days as historic traffic predictors, using a single exonential smoothing model. The combined estimated traffic is then used to group floors into sectors ideally having at least nearly equal traffic volume for each time interval.
Abstract:
Elevator system with multiple cars (1-4) and a group controller (32) having signal processing means (CPU) controlling car dispatching from the lobby (L). During peak conditions (up-peak, down-peak and noontime), each car is dispatched and assigned to hall call floors having a large predicted number of passengers waiting on priority basis, resulting in queue length and waiting time at the lobby and upper floors being decreased, and system handling capacity increased. Estimations of future traffic flow levels for the floors for five minute intervals are made using traffic levels measured during the past few time intervals on that day as real time predictors, using a linear exponential smoothing model, and traffic levels measured during similar time intervals on previous similar days as historic traffic predictors, using a single exponential smoothing model. Combined prediction is used to assign hall calls to cars on priority basis for those floors having predicted high level of passenger traffic to limit maximum waiting time and car load. Noontime priority scheme is based on multiple queue sizes and percentages of maximum waiting time limits. Different waiting time limits can be used for lobby and above lobby up and down hall calls with automatic adjustment. During up-peak the lobby is given high priority. The lobby queue is predicted using passenger arrival rates and expected car arrival times. Down-peak operation uses multiple queue levels and percentages of waiting time limits, with estimated queues based on passenger arrival using car-to-hall-call travel time.
Abstract:
An elevator system contains a group of elevator cars. A group controller contains signal processing means for controlling the dispatching of the cars from a main floor or lobby in relation to different group parameters. During up-peak conditions, each car is dispatched from the main floor to an individual plurality of contiguous floors, defining a "sector". Sectors are contiguous. The number of sectors may be less than the number of cars. Floors that constitute a sector assigned exclusively to a car are displayed on an indicator at the lobby. Sectors and cars are selected for assignment in a cyclical or round-robin sequence. If the next car selected is not available for assignment, another car is selected. If no car calls are made to the floors in the sector that is assigned to a car, the next sector is selected. The floors in the sector assigned to a car are displaced to direct passengers to the car. If car calls to the floors are not made, the car doors are closed and a new sector is assigned to the car according to the sequence.