Patent ID: 11972374
Assignee: CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.
Field: IT methods for management (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 6:
7. An Internet of things system for smart gas maintenance management order dispatch warning, comprising a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensing network platform, and a smart gas object platform, wherein the smart gas management platform includes an assignment module, a warning module, a warning monitoring module, a traffic warning value determination module, and a fault warning value determination module;
the smart gas management platform is used to:
in response to the assignment module assigning a gas work order to an order receiver, determine a warning characteristic of the gas work order; wherein the warning characteristic comprises at least a traffic warning value and a fault warning value, the determining the warning characteristic of the gas work order comprising:
determining the traffic warning value based on a fault location characteristic of the gas work order through the traffic warning value determination module; and
determining the fault warning value based on a gas fault characteristic of the gas work order through the fault warning value determination module, wherein the determining the fault warning value based on a gas fault characteristic of the gas work order through the fault warning value determination module includes:
determining a fault transfer distribution by processing an estimated location distribution graph by a fault transfer distribution model, wherein the estimated location distribution graph is constructed based on the gas fault characteristic and an estimated location distribution, wherein
the estimated location distribution includes at least one estimated location and an estimated probability of the at least one estimated location, the at least one estimated location refers to a location where a gas fault occurs at a current or future time, the estimated probability refers to a probability of the gas fault occurring at the estimated location, the estimated location includes a first type of estimated location and a second type of estimated location, the first type of estimated location is a location where the gas fault has occurred currently, the second type of estimated location is a surrounding or associated location of the location where the gas fault has occurred, and an estimated probability of the second type of estimated location is determined based on a distance or correlation between the second type of estimated location and the first type of estimated location;
the fault transfer distribution includes a gas fault corresponding to a gas fault characteristic, a severity of that gas fault, a future moment at which the gas fault is transferred, and a transfer probability of the gas fault being transferred at the future moment;
the estimated location distribution graph includes nodes and edges, the nodes refer to the estimated location, a property of the nodes includes a node type, a gas fault characteristic, and gas monitoring data, the gas fault characteristic includes a fault type of the gas fault currently occurring at the nodes, the edges reflect a connection relationship between different nodes, and the connection relationship includes a physical connection relationship and a logical connection relationship; and
the fault transfer distribution model is a graph neural network (GNN) model, an input to the fault transfer distribution model includes the estimated location distribution graph, and an output of the fault transfer distribution model includes the fault transfer distribution, wherein each node of the estimated location distribution graph output a fault transfer distribution for the node at least one future moment; and the fault transfer distribution model is obtained by a training process including:
obtaining a plurality of training samples and labels;
obtaining the fault transfer distribution model through training, based on the plurality of training samples and the labels;
determining the fault warning value based on the fault transfer distribution; and
in response to the warning monitoring module monitoring that the warning characteristic meets a preset condition, providing a warning to the order receiver by the warning module.