Smart Gas Meter Accuracy Risk Prediction via IoT Sensor Data
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Solution Overview
Problem
Current solutions for ensuring the accuracy of ultrasonic gas meters in gas pipeline networks rely heavily on regular on-site inspections, which are labor-intensive and fail to promptly identify accuracy issues.
Innovation Solution
A method and system using Internet of Things (IoT) technology to predict the accuracy risk of smart gas meters by acquiring gas attribute data from distributed sensors, performing pre-diagnosis on metering devices, and utilizing a pipeline network diagnosis model to construct an accuracy diagnosis map and determine the second accuracy risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regular on-site inspections and maintenance are performed on ultrasonic meters, then metering accuracy can be maintained, but a large amount of work is required and problems cannot be promptly identified
Solution Approach 1:
The system performs preliminary diagnosis by continuously monitoring gas attribute data (density, composition, mass, fluid pulsation, flow rate profile) and comparing it against theoretical values to predict accuracy risks before actual metering errors occur. This advance detection enables proactive maintenance scheduling rather than reactive responses.
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where metering data from ultrasonic meters is continuously collected, analyzed against theoretical calculations, and used to generate accuracy risk predictions. This feedback loop enables real-time monitoring and immediate identification of accuracy deviations without waiting for periodic inspections.
2Measurement precision
If regular on-site inspections are conducted to ensure metering accuracy, then accuracy issues can be detected, but the process is labor-intensive and inefficient
Solution Approach 1:
The system enables self-service monitoring where the metering infrastructure automatically collects, transmits, and analyzes its own performance data. The ultrasonic meters and distributed sensors autonomously generate accuracy risk assessments without requiring manual intervention, eliminating labor-intensive inspection processes while maintaining continuous verification of metering precision.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with an automated information-based system. Instead of physical on-site examinations by personnel, the system uses distributed sensors, data transmission networks, and automated analysis algorithms to continuously verify metering accuracy, dramatically improving inspection productivity.
3Loss of time
If more frequent inspections are performed to promptly identify accuracy issues, then response time improves, but work requirements and costs increase
Solution Approach 1:
The system applies partial monitoring actions by focusing computational resources on analyzing only the most critical gas attribute parameters (density, composition, mass, fluid pulsation, flow rate profile) that have the greatest impact on metering accuracy. This selective approach achieves prompt problem identification without requiring exhaustive inspection of all possible parameters, optimizing the ratio of response time to resource consumption.
Data Source
AI summary
Embodiments of the present disclosure provide a method and a system for predicting an accuracy risk of a smart gas meter based on Internet of Things (IoT). The method includes: acquiring gas attribute data of gas passing through a metering device based on a distributed sensor deployed in a gas pipeline network; determining a first accuracy risk of the metering device through performing pre-diagnosis on the metering device; in response to the first accuracy risk of at least one metering device satisfying a preset risk condition, constructing an accuracy diagnosis map based on metering devices in a gas area; determining, based on the accuracy diagnosis map, a second accuracy risk of the at least one metering device in the gas area through a pipeline network diagnosis model; and displaying detection indication information to a smart gas device management platform based on the second accuracy risk and a risk threshold.