US 11,853,398 B2
Methods and systems for detecting detection devices located at energy metering points of natural gas preliminary class
Zehua Shao, Chengdu (CN); Haitang Xiang, Chengdu (CN); Yaqiang Quan, Chengdu (CN); and Bin Liu, 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. 2, 2022, as Appl. No. 18/060,975.
Application 18/060,975 is a continuation of application No. 17/649,343, filed on Jan. 28, 2022, granted, now 11,562,182.
Claims priority of application No. 202110155161.8 (CN), filed on Feb. 4, 2021; and application No. 202210045109.1 (CN), filed on Jan. 14, 2022.
Prior Publication US 2023/0093172 A1, Mar. 23, 2023
Int. Cl. G06F 18/2413 (2023.01); G06F 18/23 (2023.01); G06F 11/30 (2006.01)
CPC G06F 18/2413 (2023.01) [G06F 11/3089 (2013.01); G06F 18/23 (2023.01)] 11 Claims
OG exemplary drawing
 
1. A method for determining an abnormality of a detection device of natural gas, comprising:
obtaining a first detection data set collected by the detection device of the natural gas, wherein the first detection data set includes sub detection data respectively collected by at least two data detection units of the detection device; and
determining whether the detection device is abnormal based on the first detection data set and a first historical detection data set, and sending a determination result to a terminal, wherein the first detection data set and the first historical detection data set include a composition of natural gas, a temperature of natural gas, and a pressure of natural gas, wherein the determining whether the detection device is abnormal based on the first detection data set and the first historical detection data set includes:
for each of the sub detection data in the first detection data set,
obtaining a second historical detection data set through removing historical sub detection data corresponding to the sub detection data in the first historical detection data set;
determining a second cluster center set based on the second historical detection data set;
obtaining a second detection data set through removing the sub detection data from the first detection data set;
determining a third vector corresponding to the second detection data set based on the second detection data set;
determining a third target cluster center based on the third vector and the second cluster center set; and
determining whether the data detection unit corresponding to the sub detection data is an abnormal data detection unit based on a third distance between the third vector and the third target cluster center.