Rail Dispatch Fault Warning Using Unified Multi-Source Data
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Solution Overview
Problem
In fully automatic driverless rail transit systems, dispatch personnel in control centers face increased labor intensity and challenges in quickly detecting and handling faults, as they need to take over the responsibilities of drivers, leading to inefficiencies and safety concerns.
Innovation Solution
A train operation intelligent dispatching system that collects multi-source data, processes it into a unified format, and uses intelligent fault analysis models to provide fault advance warnings and preplan alarms, enabling rapid fault troubleshooting and operation restoration through a centralized dispatching plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fully automatic driverless mode is implemented, then operational efficiency is improved, but dispatch personnel labor intensity increases significantly
Solution Approach 1:
The system enables self-service through automated fault detection and analysis. The intelligent fault analysis module automatically monitors multi-source data, detects faults, and generates analysis results without requiring manual inspection by dispatch personnel, allowing the system to serve itself in fault detection tasks.
Solution Approach 2:
The patent replaces manual mechanical inspection and analysis processes with an intelligent automated system. The intelligent fault analysis module uses computational algorithms to process multi-source data and detect faults, substituting the mechanical effort of manual monitoring and analysis with automated electronic processing.
2Reliability
If dispatch personnel manually monitor all systems, then fault detection capability is maintained, but fault handling speed decreases
Solution Approach 1:
The system performs preliminary action by continuously pre-monitoring multi-source data and pre-analyzing system states. The intelligent fault analysis module is continuously processing data and ready to immediately generate fault analysis results when anomalies are detected, eliminating the need for manual inspection at the moment of fault occurrence.
Solution Approach 2:
Manual fault detection and analysis processes are replaced with automated intelligent analysis systems that continuously monitor and immediately respond to faults, significantly reducing the time between fault occurrence and detection.
3Measurement precision
If multi-source data is collected and processed, then fault analysis accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex fault analysis task into distinct functional modules: data collection module, data processing module, intelligent fault analysis module, and result output module. Each module handles a specific aspect of the analysis process independently, making the overall complex system more manageable and maintainable.
Solution Approach 2:
The intelligent fault analysis module serves multiple functions: it collects data from various sources, processes the data, performs fault detection, analyzes fault characteristics, and generates comprehensive analysis results. This multi-functional design consolidates what would otherwise require multiple separate systems into a single integrated module.
Data Source
AI summary
A train operation intelligent dispatching system and method are provided, where the system includes: a data acquisition and control module, configured to collect multi-source data affecting train operation; a data processing module, configured to perform data aggregation on the multi-source data, and process the multi-source data to form multi-source data in a unified data format; a multi-disciplinary data resource pool, configured to perform real-time storage and updating of the multi-source data in the unified data format, and store historical multi-source data; an intelligent fault analysis module, having intelligent fault analysis models therein, configured to perform fault analysis based on the multi-source data to obtain fault analysis results of corresponding disciplinary systems, and output fault advance warnings of corresponding disciplinary systems according to the fault analysis results; and a process preplan module, configured to provide preplan handling processes corresponding one-to-one with external multi-disciplinary systems.