US 11,867,548 B2
Methods, Internet of Things systems, and mediums for correcting smart gas flow
Zehua Shao, Chengdu (CN); Yong Li, Chengdu (CN); Yongzeng Liang, Chengdu (CN); and Xiaojun Wei, Chengdu (CN)
Assigned to CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Chengdu (CN)
Filed by CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Sichuan (CN)
Filed on Mar. 21, 2023, as Appl. No. 18/186,978.
Claims priority of application No. 202310095073.2 (CN), filed on Feb. 10, 2023.
Prior Publication US 2023/0228608 A1, Jul. 20, 2023
Int. Cl. G01F 15/063 (2022.01); G16Y 40/35 (2020.01); G16Y 20/30 (2020.01); G16Y 10/35 (2020.01)
CPC G01F 15/063 (2013.01) [G16Y 10/35 (2020.01); G16Y 20/30 (2020.01); G16Y 40/35 (2020.01)] 7 Claims
OG exemplary drawing
 
1. A method for correcting a smart gas flow, implemented by a smart gas device management platform of an Internet of Things (I) system for correcting a smart gas flow, comprising:
obtaining reading data of a gas meter;
determining a first confidence level of the reading data based on the reading data; wherein the reading data includes first reading data and second reading data, the first reading data being historical reading data of the gas meter, the second reading data being current reading data corresponding to a current time point, and the determining a first confidence level of the reading data based on the reading data comprising:
predicting, based on the first reading data, a distribution interval of third reading data and a distribution probability corresponding to the third reading data; the third reading data being a theoretical value of the current reading data, and the current reading data being a gas consumption from time when the gas meter starts metering to the current time point; and
taking a distribution probability corresponding to the second reading data in the distribution interval of the third reading data as the first confidence level;
in response to a determination that the first confidence level is smaller than a confidence level threshold, obtaining a working condition parameter; the confidence level threshold being determined through an experience value; and
determining, based on the working condition parameter, a gas meter correction manner; the working condition parameter including a standard temperature and pressure and a current temperature and pressure, and the determining, based on the working condition parameter, a gas meter correction manner comprising:
determining, based on the second reading data, the standard temperature and pressure, and the current temperature and pressure, a correction value of the second reading data through a reading data correction model, wherein the reading data correction model is a machine learning model.