20240035868. METHOD FOR CORRECTING READING OF GAS METER IN SMART GAS, INTERNET OF THINGS SYSTEM, AND MEDIUM THEREOF simplified abstract (CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.)
METHOD FOR CORRECTING READING OF GAS METER IN SMART GAS, INTERNET OF THINGS SYSTEM, AND MEDIUM THEREOF
Organization Name
CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.
Inventor(s)
Yongzeng Liang of Chengdu (CN)
METHOD FOR CORRECTING READING OF GAS METER IN SMART GAS, INTERNET OF THINGS SYSTEM, AND MEDIUM THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240035868 titled 'METHOD FOR CORRECTING READING OF GAS METER IN SMART GAS, INTERNET OF THINGS SYSTEM, AND MEDIUM THEREOF
Simplified Explanation
The present disclosure describes a method, system, and medium for correcting the reading of a gas meter in a smart gas system. The method involves obtaining reading data of the gas meter, including a first reading data and a second reading data. It then determines the historical flow distribution condition and the current flow distribution condition. A first confidence level of the reading data is determined. If the first confidence level is smaller than a confidence level threshold, a working condition parameter is obtained. Finally, a correction value of the second reading data is determined through a reading data correction model.
- The method is implemented by a smart gas device management platform in an IoT system.
- Reading data of the gas meter is obtained, including both the initial reading and the updated reading.
- The historical and current flow distribution conditions are determined to assess the accuracy of the reading data.
- A confidence level is calculated to evaluate the reliability of the reading data.
- If the confidence level is below a certain threshold, a working condition parameter is obtained to further refine the correction process.
- A correction value is determined using a reading data correction model, which helps adjust the second reading data.
Potential applications of this technology:
- Gas meter reading accuracy improvement: The method allows for more accurate readings of gas meters in smart gas systems, reducing errors and improving billing accuracy.
- Energy management optimization: By obtaining accurate gas meter readings, energy management systems can better analyze and optimize energy consumption patterns.
- IoT-enabled gas monitoring: The method can be applied in IoT systems to monitor gas usage and detect anomalies or inefficiencies in gas distribution networks.
Problems solved by this technology:
- Inaccurate gas meter readings: The method addresses the issue of incorrect gas meter readings, which can lead to billing errors and inefficient energy management.
- Lack of confidence in reading data: By determining confidence levels and applying correction models, the method increases the reliability of gas meter readings.
- Limited visibility into gas flow distribution: The method provides insights into historical and current flow distribution conditions, enabling better analysis and optimization of gas usage.
Benefits of this technology:
- Improved billing accuracy: By correcting gas meter readings, the method ensures that customers are billed accurately for their gas consumption.
- Enhanced energy management: Accurate gas meter readings enable more precise analysis and optimization of energy usage, leading to cost savings and improved sustainability.
- Increased reliability of gas data: The method enhances the reliability of gas meter readings, providing more trustworthy data for decision-making and system monitoring.
Original Abstract Submitted
the present disclosure provides method, internet of things (iot) system, and medium for correcting reading of a gas meter in smart gas. the method may be implemented by a smart gas device management platform of an iot system for correcting reading of the gas meter in smart gas. the method may include: obtaining reading data of the gas meter, wherein the reading data includes a first reading data and a second reading data; determining a historical flow distribution condition; determining a current flow distribution condition; determining a first confidence level of the reading data; in response to a determination that the first confidence level is smaller than a confidence level threshold, obtaining a working condition parameter; and determining a correction value of the second reading data through a reading data correction model.
