Smart Gas Household Inspection With IoT Risk Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing gas pipeline inspection methods fail to ensure high completion rates within inspection deadlines and are inefficient, leading to potential safety hazards due to untimely inspections.
Innovation Solution
An IoT system for smart gas household inspection that includes platforms for data acquisition, resource optimization, and machine learning-based hazard prediction to prioritize inspections, ensuring timely and efficient gas pipeline checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional gas pipeline inspection methods are used, then inspection coverage can be achieved, but inspection completion rates within deadlines cannot be ensured and inspection efficiency is low
Solution Approach 1:
The system performs preliminary actions by analyzing gas usage data before inspections to identify high-risk households and predict potential hazards. This preliminary risk assessment enables inspectors to prioritize their visits, ensuring that high-risk areas are inspected first and completion rates are improved within deadlines.
Solution Approach 2:
The patent replaces traditional mechanical inspection methods with an intelligent system that uses gas usage data analysis and machine learning models to automatically identify risk patterns. This substitution of mechanical inspection with data-driven intelligence significantly improves inspection efficiency and enables timely completion of inspections.
2Reliability
If comprehensive gas pipeline inspections are conducted, then safety hazards can be detected, but inspection costs increase
Solution Approach 1:
The system applies local quality by concentrating inspection resources on high-risk households identified through gas usage data analysis. Instead of uniform comprehensive inspections, the system tailors inspection intensity to local risk characteristics, ensuring safety in high-risk areas while reducing unnecessary inspections in low-risk areas, thereby lowering overall inspection costs.
Solution Approach 2:
The patent replaces costly comprehensive mechanical inspections with an intelligent risk prediction system that uses gas usage data and machine learning. This substitution enables the system to identify safety hazards through data analysis before physical inspection, reducing the need for extensive on-site inspections and thereby reducing inspection costs while maintaining safety reliability.
3Productivity
If inspection resources are increased to improve completion rates, then more inspections can be completed, but inspection costs increase
Solution Approach 1:
The system performs preliminary risk assessment using gas usage data to identify which households require inspection. This preliminary action enables efficient allocation of inspection resources to only those households that need inspection, improving completion rates without proportionally increasing inspection resource costs.
Solution Approach 2:
The system applies partial action by conducting inspections only on high-risk households identified through data analysis, rather than performing exhaustive inspections on all households. This selective approach improves inspection completion rates while avoiding the cost increase that would result from comprehensive inspections of all households.
Data Source
AI summary
Method for smart gas household inspection is provided. The method includes acquiring gas usage data of at least one gas user; determining a candidate inspection time period for each gas user; determining an inspection parameter based on the candidate inspection time period and inspection resource information; sending the inspection parameter to a government gas supervision management platform and a smart gas user platform, and generating an inspection command and sending the inspection command to a smart gas inspector object platform; obtaining inspection data of a gas company, and determining an inspection completion rate of the gas company based on the inspection data; and in response to the inspection completion rate not meeting a preset progress condition, sending an inspection progress warning to the smart gas management platform, adjusting a data acquisition frequency and a data storage cleaning cycle of the gas company, and cleaning gas data in a memory.