US 11,898,704 B2
Methods and Internet of Things systems for smart gas pipeline life prediction based on safety
Zehua Shao, Chengdu (CN); Haitang Xiang, Chengdu (CN); Lei Zhang, Chengdu (CN); Yong Li, Chengdu (CN); and Yongzeng Liang, Chengdu (CN)
Assigned to CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Chengdu (CN)
Filed by CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Sichuan (CN)
Filed on Dec. 6, 2022, as Appl. No. 18/062,555.
Claims priority of application No. 202211256470.5 (CN), filed on Oct. 14, 2022.
Prior Publication US 2023/0094640 A1, Mar. 30, 2023
Int. Cl. F17D 5/00 (2006.01); G16Y 10/35 (2020.01); G16Y 40/50 (2020.01); F17D 5/02 (2006.01); G05D 1/02 (2020.01); F16L 55/32 (2006.01); F17D 5/06 (2006.01); G06N 3/088 (2023.01); G06N 20/00 (2019.01); G06N 5/04 (2023.01); G06N 3/08 (2023.01); G06N 20/20 (2019.01); G06N 5/025 (2023.01); G06N 3/042 (2023.01); G06N 3/049 (2023.01); G06N 3/02 (2006.01); G06N 7/01 (2023.01); G06N 3/047 (2023.01); G06N 3/045 (2023.01); G06N 5/022 (2023.01); G06N 5/046 (2023.01); G06N 3/044 (2023.01); G05B 19/042 (2006.01); G05B 19/4155 (2006.01); G05B 23/02 (2006.01); G05B 13/04 (2006.01); G06F 11/34 (2006.01); F16L 101/30 (2006.01); G06F 113/08 (2020.01)
CPC F17D 5/005 (2013.01) [F16L 55/32 (2013.01); F17D 5/02 (2013.01); F17D 5/06 (2013.01); G05B 13/048 (2013.01); G05B 19/042 (2013.01); G05B 19/4155 (2013.01); G05B 23/024 (2013.01); G05B 23/0283 (2013.01); G05D 1/0221 (2013.01); G06F 11/3447 (2013.01); G06F 11/3452 (2013.01); G06N 3/02 (2013.01); G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/049 (2013.01); G06N 3/08 (2013.01); G06N 3/088 (2013.01); G06N 5/022 (2013.01); G06N 5/025 (2013.01); G06N 5/04 (2013.01); G06N 5/046 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G16Y 10/35 (2020.01); G16Y 40/50 (2020.01); F16L 2101/30 (2013.01); F16L 2201/30 (2013.01); G06F 2113/08 (2020.01); G06F 2201/86 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A method for smart gas pipeline life prediction, wherein the method is implemented by a smart gas pipeline network device management sub-platform, the method comprising:
obtaining operation information of a target gas pipeline section within a first time period through a smart gas data center;
determining, based on the operation information, a first performance parameter of the target gas pipeline section of at least one moment within the first time period, wherein the first performance parameter at least includes transportation performance of the target gas pipeline section within the first time period;
determining, based on the first performance parameter of at least one moment, a first performance parameter sequence of the target gas pipeline section within the first time period, wherein the first performance parameter sequence is a sequence obtained by arranging the first performance parameter of at least one moment in a chronological order; and
determining, based on the first performance parameter sequence, a remaining life of the target gas pipeline section, including
predicting, based on the first performance parameter sequence, a second performance parameter sequence of the target gas pipeline section within a second time period, wherein the second performance parameter sequence includes performance parameter of at least one future moment; and
determining, based on the first performance parameter sequence or the second performance parameter sequence, the remaining life of the target gas pipeline section;
wherein the predicting, based on the first performance parameter sequence, a second performance parameter sequence of the target gas pipeline section within a second time period includes:
determining, based on the first performance parameter sequence, the second performance parameter sequence of the target gas pipeline section within the second time period through a performance parameter prediction model, wherein the performance parameter prediction model is a time series prediction model, an input of the performance parameter prediction model includes the first performance parameter sequence and an environmental feature sequence, the environmental feature sequence includes a plurality of environmental features arranged in chronological order, the performance parameter prediction model includes a first prediction layer and a second prediction layer, an input of the first prediction layer includes the first performance parameter sequence, an output of the first prediction layer includes second performance parameter sequence before correction, an input of the second prediction layer includes the second performance parameter sequence before correction, a confidence level, the first performance parameter sequence, and the environmental feature sequence, an output of the second prediction layer includes the second performance parameter sequence, wherein the confidence level is related to a number of performance parameters in the second performance parameter sequence before correction and a complexity of a gas pipeline map.