Mine Management System for Collision Risk Assessment
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Solution Overview
Problem
The existing mine management systems are ineffective in preventing unwanted alarms and collisions between unmanned and manned vehicles, leading to reduced safety and productivity due to the desensitization of operators to false alarms.
Innovation Solution
A mine management system that includes an unmanned vehicle traveling data generation unit, current situation data acquisition units for both vehicles, existence range and position estimation units, and a collision risk determination unit to predict potential collisions and issue alarms only when necessary, thereby preventing unwanted alarms and ensuring safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alarm is issued to avoid collision between unmanned vehicle and manned vehicle, then safety is improved, but unwanted alarms cause operator desensitization and reduce alarm effectiveness
Solution Approach 1:
The system changes the parameters of alarm issuance by introducing dynamic risk level assessment based on multiple factors including relative position, speed, trajectory, and environmental conditions. Instead of fixed threshold alarms, the system continuously evaluates collision risk parameters and issues alarms only when the assessed risk exceeds predetermined thresholds, thereby maintaining alarm effectiveness while ensuring safety.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring the operational state of both unmanned and manned vehicles, assessing collision risk in real-time, and adjusting alarm issuance decisions based on this feedback. The risk assessment feedback loop includes detecting vehicle positions, calculating relative trajectories, evaluating environmental factors, and dynamically adjusting alarm thresholds to prevent both collisions and operator desensitization.
2Reliability
If collision avoidance techniques are implemented, then safety is improved, but operations may need to be stopped and productivity lowers
Solution Approach 1:
The system applies dynamics by implementing dynamic risk assessment and adaptive collision avoidance strategies. Instead of static stop-go approaches, the system continuously evaluates collision risk based on real-time vehicle positions, speeds, and trajectories. When risk is low, operations proceed uninterrupted; when risk increases, the system dynamically adjusts by issuing graduated alarms or selective stop commands, thereby maintaining safety while minimizing productivity loss.
Solution Approach 2:
The system employs partial action by implementing selective and graduated collision avoidance measures. Rather than stopping all operations whenever a potential risk is detected, the system applies partial interventions such as issuing warnings only to specific vehicles, requesting speed adjustments for particular units, or implementing localized route modifications. This approach maintains overall operational flow while addressing specific collision risks.
3Measurement precision
If estimation of manned vehicle position and unmanned vehicle range is performed, then collision risk determination is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the complex collision risk determination task into distinct functional modules: a data acquisition unit for collecting vehicle position and speed information, an existence range estimation unit for calculating potential position ranges, a manned vehicle position estimation unit for predicting manned vehicle trajectories, and a risk determination unit for synthesizing this information. This modular segmentation improves measurement precision while managing system complexity through organized functional decomposition.
Solution Approach 2:
The system implements preliminary action by pre-calculating and storing estimation parameters such as vehicle speed ranges, acceleration profiles, and trajectory models before actual collision risk assessment. The existence range estimation unit pre-determines possible position ranges based on historical operational data, and the manned vehicle position estimation unit pre-establishes trajectory prediction algorithms. This preliminary preparation enables faster and more accurate real-time risk determination without excessive computational complexity during critical moments.
Data Source
AI summary
A mine management system includes generating unmanned vehicle traveling data including a target traveling route of an unmanned vehicle, acquiring unmanned vehicle current situation data at first time point, acquiring manned vehicle current situation data at the first time point, estimating a range in which the unmanned vehicle may be present at second time point based on the unmanned vehicle traveling data and the unmanned vehicle current situation data, estimating a position where a manned vehicle may be present at the second time point based on the manned vehicle current situation data, and deriving a risk level indicating a possibility of collision between the manned vehicle and the unmanned vehicle corresponding to the second time point at the first time point per position where the manned vehicle may be present based on estimation results of estimating the unmanned vehicle existence range and the manned vehicle existence position.