Smart Gas Meter Energy Measurement via Cloud Calorific Value Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current natural gas measurement in China relies on volume measurement, which is inefficient and lacks accuracy, especially in household settings due to complex gas supply sources and high costs of chromatography-based energy measurement systems.
Innovation Solution
A smart gas meter system utilizing a cloud-based management platform that interacts with user, service, sensor network, and object platforms to determine gas transmission routes, pressure, and calorific values, enabling accurate energy measurement data generation through cloud computing outside the gas IoT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chromatography-based energy measurement is used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical chromatography system with a computational model-based approach. Instead of using physical chromatography columns and detectors, the system uses a calorific value prediction model that processes gas composition data from sensors to calculate energy content, thereby eliminating complex mechanical measurement equipment while maintaining measurement capability.
Solution Approach 2:
The patent introduces a calorific value prediction model as an intermediary between gas composition measurement and energy calculation. This model acts as a computational mediator that transforms raw sensor data into accurate energy measurements without requiring direct chromatographic analysis, simplifying the overall measurement system.
2Measurement precision
If chromatography-based energy measurement is deployed in key departments, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent replaces expensive, long-lived chromatography equipment with a computational model that can be deployed across multiple locations at low cost. The model uses inexpensive sensor data and performs calculations through software, eliminating the need for costly physical measurement equipment at each deployment location.
Solution Approach 2:
The patent substitutes mechanical chromatography systems with computational modeling, thereby eliminating the high capital expenditure required for purchasing and maintaining chromatography equipment in multiple key departments while achieving comparable measurement accuracy through software-based prediction.
3Device complexity
If volume measurement method is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent changes the measurement parameter from simple volume to energy content by incorporating calorific value calculation. The system measures gas composition parameters and uses these to compute energy content, transforming a simple volume measurement approach into an energy-based measurement that provides higher precision while maintaining relative system simplicity through computational methods.
Solution Approach 2:
The patent introduces a calorific value prediction model as an intermediary that bridges simple volume measurement data and accurate energy measurement. This computational mediator processes basic measurement data and transforms it into precise energy content information without requiring complex physical measurement equipment.
4Productivity
If cloud computing outside gas IoT is used, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent extracts complex computational tasks from the local gas IoT system and places them in an external cloud computing environment. The calorific value prediction model and energy measurement calculations are performed on remote cloud servers, allowing the local system to maintain simplicity while benefiting from high-capacity computational resources available in the cloud.
Data Source
AI summary
The embodiments of the present disclosure provide methods and systems for energy measuring based on natural gas data of a smart gas meter, including establishing the gas IoT. The object platform obtains consumed gas volume data and location data of the smart gas meter as first data and second data, and sending them to the management platform through the sensor network platform. The management platform generates consumed gas preprocessing data based on the first data and the second data, and generates energy measurement data after performing the computation outside the gas IoT on the consumed gas preprocessing data by the cloud platform outside the gas IoT. The management platform sends the energy measurement data to the user platform configured in a regional pipeline network center through the service platform. The user platform performs gas energy measurement of the smart gas meter based on the energy measurement data.