IoT Debris Flow Risk Mapping With UAV Emergency Supervision
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Solution Overview
Problem
Current debris flow monitoring systems lack accuracy and timeliness due to insufficient multi-dimensional data integration, making it difficult to implement effective preventive measures against sudden and destructive debris flows.
Innovation Solution
A system and method utilizing a large IoT model for emergency supervision, involving a debris flow emergency supervision system with platforms for user interaction, management, sensing, and perception control, including UAVs for data collection and flocculant spraying, to enhance monitoring and prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-dimensional monitoring and integrated analysis are implemented, then monitoring accuracy and timeliness are improved, but system complexity increases
Solution Approach 1:
The system divides the target region into multiple sub-regions for independent monitoring and risk assessment. Each sub-region can be monitored and analyzed separately, which simplifies the overall system architecture while maintaining comprehensive coverage. The segmentation allows parallel processing of monitoring data from different locations, improving timeliness without proportionally increasing system complexity.
Solution Approach 2:
The emergency supervision management platform performs multiple functions including data collection, risk assessment, UAV control, and warning issuance within a single integrated system. This multi-functionality reduces the need for separate specialized systems, thereby managing complexity while achieving multi-dimensional monitoring and integrated analysis.
2Measurement precision
If comprehensive multi-dimensional monitoring is implemented, then monitoring accuracy is improved, but the quantity of data processing increases
Solution Approach 1:
By dividing the target region into sub-regions, the system processes data in manageable segments rather than handling the entire dataset at once. This segmentation strategy reduces the immediate data processing load while maintaining comprehensive monitoring coverage across all sub-regions.
Solution Approach 2:
The system determines risk values and monitors parameters specifically tailored to each sub-region's local characteristics and conditions. This localized approach allows the system to process and analyze only the relevant data for each area, reducing unnecessary data processing while maintaining high monitoring accuracy through locally-specific analysis.
3Area of stationary object
If UAV operations are used for data collection, then monitoring coverage is improved, but operational complexity increases
Solution Approach 1:
The system automatically generates flight paths, collection point locations, and collection volumes based on risk assessments and predefined parameters. The UAV operations are automated through program control rather than manual intervention, reducing operational complexity while expanding monitoring coverage through automated data collection across multiple sub-regions.
Data Source
AI summary
Provided are a system and method for emergency supervision of a debris flow based on a large model of IoT. The method includes: dividing a target region into a plurality of sub-regions; at every preset interval, determining enhanced multimodal data of each of the sub-regions based on original multimodal data of each of the sub-regions and a positional relationship between the sub-regions; determining an independent risk value of each of the sub-regions; determining a first risk value of each of the sub-regions based on the independent risk value and the positional relationship; and generating a collection instruction based on the first risk value of each of the sub-regions, a downstream residential density, and the enhanced multimodal data of the sub-regions, and sending the collection instruction to an emergency supervision internal perception control platform to control a UAV to collect data based on the collection instruction.