IoT Gas Leak Warning with Scenario-Based Dynamic Thresholds
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Solution Overview
Problem
Existing gas leakage detection systems are limited to kitchen scenarios and face challenges with timely and accurate detection in factory workshops and commercial districts, leading to false alarms and unnecessary panic.
Innovation Solution
An IoT system with a smart gas safety management platform that determines gas determination scenarios through a preset algorithm, sets dynamic thresholds based on gas data dimensions, and issues warnings for gas leakage using concentration, slope, and time thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed threshold method is used for gas leakage detection, then the system is simple to implement, but it causes false alarms and misreporting in different scenarios
Solution Approach 1:
The patent implements dynamic threshold adjustment based on gas determination scenarios. The system dynamically adapts warning thresholds according to the current scenario (kitchen, factory workshop, commercial district) rather than using fixed thresholds. This resolves the contradiction by making the system complex enough to adapt to different scenarios while maintaining reliability through scenario-specific threshold optimization.
Solution Approach 2:
The system changes detection parameters (warning thresholds) based on different gas determination scenarios. By adjusting thresholds according to scenario characteristics and user behaviors, the system achieves high detection accuracy across diverse environments without causing false alarms, thus resolving the reliability issue while maintaining reasonable implementation complexity.
2Adaptability or versatility
If a generic gas detection system is used across all scenarios, then the system has wide applicability, but it cannot detect potential leakage hazards timely in specific scenarios
Solution Approach 1:
The patent segments the gas detection system into scenario-specific modules using gas determination algorithms. Different scenarios (kitchen, factory workshop, commercial district) are handled by dedicated detection logic and threshold settings. This segmentation enables timely detection in each specific scenario while maintaining overall system versatility through the unified platform architecture.
Solution Approach 2:
The system achieves universality through a multi-functional platform that can operate across different scenarios. The gas determination algorithm and dynamic threshold adjustment mechanism enable the same system to adapt to various environments (kitchen, factory, commercial district), providing both wide applicability and scenario-specific timeliness simultaneously.
3Measurement precision
If scenario-specific detection is implemented, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent achieves high detection accuracy by changing detection parameters (thresholds, determination criteria) based on gas determination scenarios rather than redesigning the entire system for each scenario. This parameter-based adaptation maintains detection precision across scenarios while avoiding the complexity of multiple dedicated systems.
4Reliability
If dynamic threshold adjustment is implemented, then false alarms are reduced, but computational requirements increase
Solution Approach 1:
The system adjusts thresholds dynamically based on gas determination scenarios and user behaviors, reducing false alarms while maintaining reasonable computational requirements. The threshold adjustment is driven by scenario classification rather than continuous complex calculations, balancing warning accuracy with energy efficiency.
Data Source
AI summary
The present disclosure provides a method and IoT system for gas safety warning based on a gas determination scenario. The method is implemented by a smart gas safety management platform of the IoT system for gas safety warning. The method comprises determining the gas determination scenario through a preset determination algorithm based on gas data in a plurality of dimensions, wherein the gas determination scenario includes a first determination scenario or a second determination scenario; determining a dynamic threshold set corresponding to the gas determination scenario through a model based on the gas determination scenario and gas scenario data, wherein the dynamic threshold set includes at least one of warning concentration thresholds, warning slope thresholds, and warning time thresholds; and determining a gas leakage situation based on the gas data and the dynamic threshold set, and issuing gas safety warning.