Elevator Control System Optimizing Cabin Allocation via Traffic Forecasting
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Solution Overview
Problem
Existing elevator systems struggle to accurately predict short-term traffic volumes, leading to inefficiencies in cabin allocation and increased energy consumption due to unnecessary empty runs.
Innovation Solution
The method involves incorporating personal information from individuals present in the elevator system's environment, including presence data and individualized information, to generate more accurate forecasts of traffic volume. This information is weighted based on empirically determined probabilities and used to optimize cabin allocation and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If empirically determined traffic loads are used for cabin allocation, then the system can operate with simple control logic, but the prediction accuracy of short-term traffic volume is insufficient
Solution Approach 1:
The system performs preliminary actions by detecting personal information (presence, movement, destination) of users before they actually make elevator calls. This advance detection allows the control system to predict future traffic volume and composition, enabling proactive cabin allocation decisions that improve prediction accuracy without requiring complex real-time adjustments
2Reliability
If personal information from external systems is incorporated, then the forecast of future traffic situation becomes more accurate, but the system requires more complex data integration and processing
Solution Approach 1:
The elevator control system acts as an intermediary that receives and processes personal information from external systems (access control, visitor management, meeting room booking systems). By centralizing the processing of these diverse data sources within the existing control architecture, the system integrates multiple information streams without requiring each external system to directly communicate with multiple targets, thus improving forecast reliability while managing integration complexity
3Productivity
If destination call control is used, then transport capacity increases, but empty runs occur when cabins are dispatched without accurate knowledge of future traffic
Solution Approach 1:
The system implements feedback by continuously monitoring detected personal information (user presence, movement patterns, destination preferences) and using this information to adjust cabin allocation decisions. This feedback loop enables the system to learn from actual traffic patterns and improve its predictions, ensuring that cabins are dispatched only when there is a high probability of immediate or near-future calls, thereby reducing empty runs while maintaining high transport capacity
Data Source
Figure 1
AI summary
The invention relates to a method for controlling a lift installation (100) that has a plurality of cabins (112 – 120) that can each call at a number of floors (130 – 135), wherein calls placed from outside the cabins are associated with one of the cabins by means of a lift controller (200) on the basis of at least one association criterion, wherein the association takes account of the current traffic situation in the lift installation (100), wherein a forecast of a future traffic situation in the lift installation (100) is produced and taken into account for the association, wherein the forecast is produced by taking account of personal information from persons who are in a predetermined environment of the lift installation or of a section of the installation (100) or enter a predetermined environment of the lift installation or remove themselves from a predetermined environment of the lift installation.