IoT Natural Gas Metering System for Abnormal Device Detection
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Solution Overview
Problem
The natural gas metering process faces challenges in accurately detecting abnormal devices, which can affect the precision and stability of gas measurement, especially with the increasing complexity of customer groups and diversified gas application scenarios.
Innovation Solution
A method and system utilizing the Internet of Things (IoT) to determine abnormal devices in natural gas metering processes. This involves obtaining natural gas detection parameters from detection devices, processing them to determine first and second energy data, comparing these data sets to identify potential abnormalities, and calculating the probability of device abnormality based on related information and energy data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gas measurement methods are used, then the system is simple, but the measurement accuracy and stability deteriorate due to inability to detect abnormal devices
Solution Approach 1:
The system segments the gas measurement process into multiple independent detection nodes distributed across the network. Each detection device independently measures gas parameters and transmits data to the management platform, allowing parallel processing and improved measurement accuracy without requiring a completely complex centralized system.
Solution Approach 2:
The management platform receives detection data from multiple devices, processes it through data processing modules, and provides feedback by identifying abnormal devices. This feedback mechanism enables continuous improvement of measurement accuracy by detecting and flagging abnormal devices while maintaining the overall system structure.
2Reliability
If multiple detection devices are deployed to improve measurement reliability, then the measurement stability improves, but the difficulty of detecting and measuring abnormal devices increases
Solution Approach 1:
The system implements a feedback mechanism where the management platform receives data from multiple detection devices, processes it through data processing modules, and identifies abnormal devices by comparing data consistency. This feedback loop automatically detects abnormal devices without requiring manual inspection, resolving the contradiction between improved reliability and increased detection difficulty.
Solution Approach 2:
The management platform acts as an intermediary between multiple detection devices and the user platform. It collects, processes, and analyzes data from all devices, using data processing modules to identify abnormal devices through comparative analysis, thereby simplifying the detection process despite having multiple devices.
3Productivity
If real-time monitoring is implemented to improve response speed, then the productivity improves, but the energy consumption increases
Solution Approach 1:
The detection devices periodically transmit detection data to the management platform at set intervals rather than continuously. This periodic action enables real-time monitoring capability and improved productivity while reducing energy consumption compared to continuous transmission, as devices can enter low-power states between transmission cycles.
Data Source
AI summary
The present disclosure discloses a method for determining an abnormal device in a process of measuring energy of natural gas based on Internet of Things (IOT). The method may include obtaining a natural gas detection parameter detected by at least one detection device via a sense network platform in response to a query request; determining first energy data and second energy data by processing the natural gas detection parameter; determining whether the abnormal device exists by comparing the first energy data and the second energy data; in response to determining that the abnormal device exists, for each detection device, determining a probability that the detection device is abnormal based on related information of the detection device, the first energy data, and the second energy data; and determining the abnormal device based on the probability that the detection device is abnormal.