Patent ID: 11965765
Assignee: CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 0:
1. A method for predicting gas transmission loss, implemented by a gas equipment management platform of an Internet of Things (IoT) system for predicting gas transmission loss, comprising:
obtaining gas flow data, gas pressure data, and ambient temperature data of a plurality of time points respectively based on gas metering devices, pressure detection devices, and temperature monitoring devices at a plurality of positions of a gas pipeline network;
predicting a gas metering error based on the ambient temperature data;
determining whether gas loss is abnormal loss based on the gas flow data, the gas pressure data, and the gas metering error, wherein the determining whether the gas loss is abnormal loss includes:
determining a gas pressure change feature based on the gas pressure data;
determining a gas flow difference based on the gas flow data of the plurality of positions in combination with the gas pressure change feature and the gas metering error; and
determining whether the gas flow difference satisfies a preset condition, and in response to a determination that the gas flow difference does not satisfy the preset condition, determining that the gas loss is the abnormal loss; wherein the preset condition includes that the gas flow difference satisfies a gas flow difference range; and the gas flow difference range is related to at least one of a type and quality of gas or the gas pressure change feature, wherein the determining whether the gas flow difference satisfies a preset condition includes:
constructing a graph structure by taking the gas metering devices in the gas pipeline network as nodes and gas pipelines as edges based on the gas flow data, the gas pressure change feature, the gas metering error, the gas flow difference, and the gas flow difference range;
determining a gas flow difference prediction value corresponding to each edge of the graph structure based on processing the graph structure by a flow model, the flow model being a graph neural networks (GNN) model:
updating the gas flow difference corresponding to each edge in the graph structure; and
determine whether the gas flow difference prediction value corresponding to each edge in the graph structure satisfies the gas flow difference range;

in response to a determination that the gas loss is the abnormal loss, sending a warning notice.