Patent ID: 11874261
Assignee: CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A method for managing dust pollution in a smart city, which is executed by a management platform, the method comprising:
obtaining, through a sensing network platform, one or more environmental data of an area to be detected and one or more street data associated with the environmental data, the environmental data at least including dust data indicating dust information in the air;
determining, based on the environmental data, whether there is dust pollution in the area to be detected;
in response to determining that there is dust pollution in the area to be detected, determining, based on the environmental data and the street data, a position of at least one dust pollution source and treating the dust pollution; wherein the determining, based on the environmental data and the street data, a position of at least one dust pollution source comprises:
dividing the area to be detected into a plurality of sub-areas;
determining, based on the street data, road network sub-maps of the plurality of sub-areas by the sub-platforms, respectively; each road network sub-map at least including streets of the corresponding sub-area and relationships between the streets; and
determining, based on the road network sub-maps and the environmental data, the position of at least one dust pollution source, wherein the determining, based on the road network sub-maps and the environmental data, the position of at least one dust pollution source comprises:
determining, based on the environmental data, one or more target sub-areas with dust pollution from the plurality of sub-areas;
determining, based on the road network sub-maps of the target sub-areas, a target road network map, wherein the target road network map includes streets of the target sub-areas and the relationships between the streets;
extracting a node vector representation of each node and an edge vector representation of each edge from the target road network map; wherein the node represents a street, and the node vector representation at least includes a name, a position and a direction of a street; the edge represents the relationship of the street, the edge vector representation at least includes the relationship between the streets;
extracting the dust data of each node based on the environmental data;
adding the dust data to the node vector representation of each node to obtain a target node vector representation; and
determining, according to the target node vector representation and the edge vector representation, the position of the at least one dust pollution source based on a graph neural network;

obtaining, through the sensing network platform, wind direction data at the position of the at least one dust pollution source;
determining, based on the position of the at least one dust pollution source, one or more target streets, wherein the one or more target streets connect to the at least one dust pollution source; and
determining, based on inputting the wind direction data, the dust data, the position of the at least one dust pollution source, distance between the at least one dust pollution source and each of the one or more target streets, and weather data to a pollution degree prediction model, degrees of dust pollution of the one or more target streets in a future period.