Smart Gas Safety Platform for Harmful Component Warning
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Solution Overview
Problem
There is a need for an effective method to monitor and warn about harmful components in natural gas to ensure household gas safety and pipeline cleanliness, as natural gas contains harmful substances like hydrogen sulfide and carbon monoxide, and long-term pipeline usage can lead to the entrainment of other harmful components.
Innovation Solution
A smart gas safety management platform using an Internet of Things system that obtains gas composition and user information, determines the generation rate of harmful components through a machine learning-based prediction model, and generates warnings when the generation rate exceeds a threshold, integrating composition and use information to provide timely safety alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional gas monitoring methods are used, then the system is simple, but the ability to predict and warn about harmful components is insufficient
Solution Approach 1:
The system performs preliminary action by using a machine learning model to predict the generation rate of harmful components before they reach dangerous levels. The model is trained on historical data and continuously predicts future harmful component generation based on gas composition and usage patterns, enabling early warning and preventive measures rather than reactive monitoring.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw sensor data (gas composition and usage information) and safety warnings. This model processes and analyzes the data to predict harmful component generation rates, bridging the gap between simple monitoring and reliable safety prediction.
2Measurement precision
If machine learning prediction model is implemented, then the prediction accuracy of harmful components is improved, but the computational complexity increases
Solution Approach 1:
The machine learning model implements self-service by automatically training itself on historical data and continuously improving its prediction accuracy without requiring manual intervention. The system autonomously processes gas composition and usage information, generates predictions, and updates its internal parameters to enhance measurement precision while managing its own complexity.
Solution Approach 2:
The system applies partial action by focusing the machine learning model's computational resources on predicting only the harmful component generation rate based on relevant inputs (gas composition and usage information), rather than analyzing all possible gas parameters. This selective approach improves prediction accuracy for the specific harmful component while controlling overall computational complexity.
3Loss of time
If real-time monitoring and prediction is performed, then the response time for safety warnings is reduced, but the energy consumption increases
Solution Approach 1:
The system implements periodic action by performing real-time prediction and monitoring at scheduled intervals rather than continuously. The smart gas safety management platform processes gas composition and usage information periodically to generate harmful component generation rate predictions, providing timely safety warnings while reducing energy consumption compared to continuous real-time processing.
Data Source
AI summary
The present disclosure provides a method, an Internet of Things system and medium for early warning smart gas harmful components. The method comprises: obtaining composition information of a gas and use information of a user; determining a first generation rate of the harmful components based on the composition information; determining a second generation rate of the harmful components through a generation rate prediction model based on the composition information and the use information, wherein the generation rate prediction model is a machine learning model and obtained by training, wherein a training sample includes historical use information of the user and historical composition information, and a label includes a second generation rate corresponding to the historical use information of the user and the historical composition information; determining a generation rate of the harmful components based on the first generation rate and the second generation rate; and generating warning information in response to the generation rate of the harmful components being greater than a generation rate threshold.