US 11,966,885 B2
Methods and Internet of Things (IoT) systems for predicting maintenance materials of smart gas pipeline networks
Zehua Shao, Chengdu (CN); Yong Li, Chengdu (CN); and Junyan Zhou, 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. 18/186,979.
Claims priority of application No. 202310104350.1 (CN), filed on Feb. 13, 2023.
Prior Publication US 2023/0230050 A1, Jul. 20, 2023
Int. Cl. G06Q 10/20 (2023.01); G06Q 10/04 (2023.01); G16Y 10/35 (2020.01); G16Y 40/40 (2020.01); G16Y 40/50 (2020.01)
CPC G06Q 10/20 (2013.01) [G06Q 10/04 (2013.01); G16Y 10/35 (2020.01); G16Y 40/40 (2020.01); G16Y 40/50 (2020.01)] 5 Claims
OG exemplary drawing
 
1. A method for predicting maintenance materials of a smart gas pipeline network, implemented based on a smart gas safety management platform of an Internet of Things (IoT) system for predicting maintenance materials of a smart gas pipeline network, comprising:
obtaining a pipeline network feature of a gas pipeline network;
predicting fault probabilities of one or more point positions of the gas pipeline network based on the pipeline network feature, the fault probabilities including probabilities of one or more preset fault types of faults occurring at the point positions, wherein
the predicting fault probabilities of one or more point positions of the gas pipeline network based on the pipeline network feature includes:
constructing a pipeline network diagram based on the pipeline network feature, a node of the pipeline network diagram corresponding to the point position of the gas pipeline network, and an edge of the pipeline network diagram corresponding to a gas pipeline of the gas pipeline network; and
predicting, based on the pipeline network diagram, the fault probabilities of one or more point positions of the gas pipeline network through a probability determination model, the probability determination model being a machine learning model; and
determining demand for the maintenance materials based on the fault probabilities of the one or more point positions, wherein
the determining the demand for the maintenance materials includes:
determining sub-demand for the maintenance materials of each point position based on the fault probability of each point position of the one or more point positions, the sub-demand being obtained based on historical maintenance data of the gas pipeline network; and
determining the demand for the maintenance materials based on the sub-demand of each point position of the gas pipeline network, wherein
the IoT system further comprises: a smart gas user platform, a smart gas service platform, a smart gas sensor network platform, and a smart gas object platform;
the smart gas user platform includes a gas user sub-platform and a supervision user sub-platform;
the smart gas service platform includes a smart gas use service sub-platform corresponding to the gas user sub-platform, and a smart supervision service sub-platform corresponding to the supervision user sub-platform;
the smart gas safety management platform includes a smart gas emergency maintenance management sub-platform and a smart gas data center, wherein the smart gas emergency maintenance management sub-platform includes a device safety monitoring management module, a safety alarm management module, a work order dispatch management module, and a materials management module;
the smart gas sensor network platform includes a smart gas device sensor network sub-platform and a smart gas maintenance engineering sensor network sub-platform;
the smart gas object platform includes a smart gas device object sub-platform and a smart gas maintenance engineering object sub-platform; and
the determining demand for the maintenance materials based on the fault probability of each point position includes:
transmitting the demand for the maintenance materials to the smart gas user platform based on the smart gas service platform.