US 12,276,535 B2
Method for correcting reading of gas meter in smart gas, internet of things system, and medium thereof
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 Oct. 10, 2023, as Appl. No. 18/484,429.
Application 18/484,429 is a continuation of application No. 18/186,978, filed on Mar. 21, 2023, granted, now 11,867,548.
Claims priority of application No. 202310095073.2 (CN), filed on Feb. 10, 2023.
Prior Publication US 2024/0035868 A1, Feb. 1, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G01F 15/063 (2022.01); G16Y 10/35 (2020.01); G16Y 20/30 (2020.01); G16Y 40/35 (2020.01)
CPC G01F 15/063 (2013.01) [G16Y 10/35 (2020.01); G16Y 20/30 (2020.01); G16Y 40/35 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A method for correcting reading of a gas meter in smart gas, implemented by a smart gas device management platform of an Internet of Things (IoT) system for correcting the reading of the gas meter in the smart gas, the method comprising:
obtaining reading data of the gas meter, wherein the reading data includes a first reading data and a 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;
determining, based on a first reading data sequence corresponding to the first reading data, a historical flow distribution condition;
determining, based on a second reading data sequence corresponding to the second reading data, a current flow distribution condition;
determining, based on the historical flow distribution condition and the current flow distribution condition, 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 corresponding to the gas meter, wherein the working condition parameter includes a standard temperature and a standard pressure and a current temperature and a current pressure; and
determining, based on the second reading data, the standard temperature and the standard pressure, and the current temperature and the current 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.