Predictive Warning Control for Timely Hazard Region Alerts
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Solution Overview
Problem
Conventional warning systems face challenges in providing timely warnings before entry into dangerous regions while suppressing unnecessary alert notifications, as they rely solely on entry detection without considering the operational conditions and action predictions of individuals or machines.
Innovation Solution
A warning system comprising an acquisition unit for collecting sensor data, an analysis unit for predicting object actions, and a calculation unit to determine a degree of caution, which issues alerts and controls operations based on calculated caution levels, using image data to track and predict object movements and states without requiring specialized sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional entry detection techniques are used to detect persons in surveillance areas, then entry detection capability is provided, but timely warnings before entry into dangerous regions cannot be given and unnecessary alert notifications occur
Solution Approach 1:
The system performs preliminary action by predicting future positions of moving objects and calculating caution levels before actual dangerous situations occur. The calculation unit computes caution levels based on predicted trajectories, allowing warnings to be issued in advance before objects actually enter dangerous regions, thus resolving the timing issue while maintaining accuracy through conditional alert generation.
Solution Approach 2:
The system applies dynamics by continuously updating caution levels as objects move through the surveillance area. The calculation unit dynamically adjusts caution levels based on real-time positions, predicted future positions, and changing distances to dangerous regions. This dynamic approach allows the system to differentiate between objects approaching danger and those moving away, reducing unnecessary alerts while maintaining timely warnings.
2Area of stationary object
If surveillance coverage is expanded to detect all potential entries, then detection coverage is improved, but unnecessary alert notifications increase
Solution Approach 1:
The system applies local quality by assigning different caution levels to different spatial regions within the surveillance area. Instead of treating all areas uniformly, the calculation unit computes specific caution levels based on local conditions such as distance to dangerous regions, object trajectories, and predicted positions. This localized assessment allows comprehensive surveillance coverage while reducing false alarms by only triggering alerts in regions where actual danger exists.
Solution Approach 2:
The system uses parameter changes by varying alert generation thresholds based on multiple parameters including caution levels, predicted distances to dangerous regions, and object velocities. The calculation unit dynamically adjusts these parameters to distinguish between normal movements and potentially dangerous situations, enabling expanded surveillance coverage without increasing false alarms through intelligent parameter-based filtering.
Data Source
AI summary
A warning system according to an embodiment includes an acquisition unit, an analysis unit, and a calculation unit. The acquisition unit acquires, from first sensor data including information on a target, condition information on the target. The analysis unit analyzes, from second sensor data including information on a region, current state information on an object included in the region and action prediction information on the object. The calculation unit calculates a degree of caution, based on the condition information on the target, the current state information on the object, and the action prediction information on the object.