Elevator Trapped-Event Detection with Neural Network Analysis
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Solution Overview
Problem
Existing elevator systems face challenges in accurately identifying real trapped events and optimizing rescue resource allocation, as many distress events are not caused by system failures and can be resolved without human intervention.
Innovation Solution
A method using a neural network model to analyze operational state data and generate a feature vector, determining the probability of a transport object being trapped in an elevator car, incorporating safety event data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional distress event detection methods are used, then all distress events are alerted to rescue personnel, but rescue resources are wasted on non-trapped events
Solution Approach 1:
The patent replaces traditional mechanical rule-based alert systems with an intelligent analysis system using neural networks and decision trees. The system automatically analyzes operational data, identifies true trapped events, and generates alerts only for genuine cases, eliminating waste of rescue resources on false alarms while maintaining high reliability through multi-parameter correlation analysis
Solution Approach 2:
The patent introduces an intermediary intelligent analysis system between the distress event detection and rescue personnel dispatch. This intermediary layer processes operational data, determines whether events are true trapped incidents or false alarms, and selectively triggers alerts, thereby preventing unnecessary rescue resource deployment while ensuring genuine cases are promptly responded to
2Measurement precision
If comprehensive operational data analysis is performed, then accuracy of trapped event determination is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis system into distinct functional modules: data acquisition module, neural network analysis module, decision tree module, and alert generation module. Each module handles specific aspects of the analysis, making the overall system more manageable and maintainable while achieving high measurement precision through coordinated operation of these specialized components
Solution Approach 2:
The patent introduces an intermediary intelligent analysis system that processes comprehensive operational data through structured methodologies. The system uses standardized data collection protocols and systematic analysis frameworks to manage complexity while maintaining high determination accuracy through multi-parameter correlation analysis and expert rule integration
Data Source
AI summary
A method and device for determining a safety risk of an elevator system, and a non-transitory computer-readable storage medium storing a computer program for implementing the method. A method for determining a safety risk of an elevator system includes determining the safety risk by generating a feature vector based at least on operational state data of the elevator system, and subsequently determining a probability of a transport object being trapped in a car of the elevator system using a neural network model. The feature vector and the probability are an input variable and an output variable of the neural network model, respectively, and the feature vector includes components corresponding to combinations selected from a plurality of state values of a first category of features. Further, the state values of the first category of features are determined based on the operational state data.