US 11,989,008 B2
Methods and Internet of Things systems for regulating rated outlet pressures of gate station compressors for smart gas
Zehua Shao, Chengdu (CN); Yaqiang Quan, Chengdu (CN); Guanghua Huang, Chengdu (CN); Haitang Xiang, Chengdu (CN); and Yuefei Wu, Chengdu (CN)
Assigned to CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Chengdu (CN)
Filed by CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Sichuan (CN)
Filed on Aug. 1, 2023, as Appl. No. 18/362,997.
Application 18/362,997 is a continuation of application No. 18/154,854, filed on Jan. 16, 2023, granted, now 11,762,373.
Claims priority of application No. 202211496057.6 (CN), filed on Nov. 28, 2022.
Prior Publication US 2023/0376010 A1, Nov. 23, 2023
Int. Cl. G05B 19/416 (2006.01)
CPC G05B 19/416 (2013.01) [G05B 2219/41108 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for regulating a rated outlet pressure of a gate station compressor for smart gas, wherein the method is implemented based on an Internet of Things system for regulating a rated outlet pressure of a gate station compressor for smart gas, the Internet of Things system includes a smart gas device management platform, a smart gas sensor network platform, and a smart gas object platform interacting in sequence, and the method is executed by the smart gas device management platform, comprising:
obtaining user features of a downstream gas usage based on the smart gas object platform, the user features including at least a user type and at least one of downstream flow prediction values of a plurality of future moments, wherein the downstream flow prediction values of the plurality of future moments are obtained by a downstream flow prediction model based on a historical downstream flow sequence, the downstream flow prediction model being a machine learning model; the downstream flow prediction model is obtained by training a plurality of first training samples with labels; and the first training samples include a sample historical downstream flow sequence, and the labels are downstream flow prediction values of a plurality of future moments corresponding to moments of the first training samples;
obtaining operation parameters of a compressor based on the smart gas object platform, the operation parameters including at least a rated outlet pressure set by the compressor; and
determining a rated outlet pressure adjustment amount of the compressor based on the user features and the operation parameters.