| CPC G06Q 50/06 (2013.01) | 5 Claims |
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1. A method for controlling a gas supply cost based on smart gas, wherein the method is implemented by a smart gas management platform based on an Internet of Things (IoT) system for controlling a gas supply cost based on smart gas, comprising:
predicting a gas supply quantity for a future preset time period based on a planned gas supply quantity of a gas supplier;
predicting a gas demand quantity for the future preset time period based on a gas usage quantity of a historical user;
determining a gas gap for the future preset time period based on the gas supply quantity and the gas demand quantity; and
determining a gas compensation scheme based on the gas gap, operational requirements of an end of a pipeline, and operational parameters of the end of the pipeline; wherein
the predicting a gas supply quantity for a future preset time period based on a planned gas supply quantity of a gas supplier includes:
determining, based on the planned gas supply quantity of the gas supplier, a predicted gas supply quantity for the future preset time period using a preset manner;
predicting a gas supply deviation rate of the gas supplier by a deviation rate prediction model based on a gas pipeline design map, weather information for the future preset time period, and the gas demand quantity for the future preset time period, the deviation rate prediction model being a machine learning model; and
determining the gas supply quantity for the future preset time period based on the predicted gas supply quantity and the gas supply deviation rate, wherein
the deviation rate prediction model includes a pipeline feature extraction layer and a deviation rate prediction layer, the pipeline feature extraction layer used to process the gas pipeline design map to determine a pipeline feature map and the deviation rate prediction layer used to process the pipeline feature map, the weather information for the future preset time period, and the gas demand quantity for the future preset time period to determine the gas supply deviation rate of the gas supplier;
the deviation rate prediction model is obtained by jointly training of the pipeline feature extraction layer and the deviation rate prediction layer based on samples and labels, the samples include historical sample gas pipeline design maps for at least one historical sample area, weather information for the historical sample time period, and gas demand quantity for the historical sample time period, and labels include historical sample gas deviation rate corresponding to the historical sample area of the historical sample time period;
wherein the jointly training includes:
inputting the historical sample gas pipeline design maps into an initial pipeline feature extraction layer and obtaining an output of the initial pipeline feature extraction layer, wherein the output of the initial pipeline feature extraction layer includes a pipeline feature map that reflects features of gas pipelines, the pipeline feature map includes nodes and edges, the nodes represent gas pipelines, node attributes of the nodes reflect relevant features of corresponding gas pipelines, the node attributes include reliability of the pipelines and standard flow rate interval of the pipelines, the edges represent that the gas pipelines are adjacent and connected, edge attributes of the edges reflect relevant features of corresponding pathways, and the edge attributes include a degree of bending at a node connection;
inputting the weather information for the historical time period, the gas demand quantity for the historical time period, and the output of the initial pipeline feature extraction layer into an initial deviation rate prediction layer and obtaining an output of the initial deviation rate prediction layer, wherein the output of the initial deviation rate prediction layer includes a gas supply deviation rate of the gas supplier, and the gas supply deviation rate of the gas supplier is a degree of deviation between an amount of gas actually supplied by the gas supplier to an entire preset area and the predicted gas supply quantity in the future preset time period;
constructing a loss function based on the output of the initial deviation rate prediction layer and the labels;
updating parameters of the initial pipeline feature extraction layer and the initial deviation rate prediction layer iteratively based on the loss function until meeting a preset condition; and
obtaining the deviation rate prediction model; and
the predicting a gas demand quantity for the future preset time period based on a gas usage quantity of a historical user includes:
determining a first historical usage quantity based on the gas usage quantity of the historical user; wherein the first historical usage quantity is a gas usage quantity of the historical user at a target historical time period, and the target historical time period is a historical time period corresponding to the future preset time period;
fitting the first historical usage quantity to obtain a first straight line;
determining, based on the first straight line, a second historical usage quantity;
wherein the second historical usage quantity is a gas usage quantity in the first historical usage quantity for which a distance from the first straight line satisfies a preset distance condition;
fitting the second historical usage quantity to obtain a second straight line; and
predicting the gas demand quantity for the future preset time period based on the second straight line;
wherein the IoT system further comprises a smart gas user platform, a smart gas service platform, a smart gas sensing network platform, and a smart gas object platform; the method further comprises:
sending a gas operation and management information query instruction to the smart gas service platform through the smart gas user platform, and receiving gas operation and management information uploaded by the smart gas service platform, wherein the smart gas user platform is configured as a terminal device;
obtaining gas operation and management information from a smart gas data center of the smart gas management platform through the smart gas service platform, and sending the gas operation and management information to the smart gas user platform, wherein the smart gas management platform is configured to perform an information interaction with the smart gas service platform and the smart gas sensing network platform through the smart gas data center of the smart gas management platform, respectively;
receiving the gas operation and management information query instruction issued by the smart gas service platform through the smart gas data center of the smart gas management platform, uploading the gas operation and management information to the smart gas service platform; issuing an instruction for obtaining gas equipment-related data to the smart gas sensing network platform, and receiving gas equipment-related data uploaded by the smart gas sensing network platform;
receiving the instruction for obtaining gas equipment-related data issued by the smart gas data center of the smart gas management platform through the smart gas sensing network platform, uploading the gas equipment-related data to the smart gas data center of the smart gas management platform; receiving the gas equipment-related data uploaded by the smart gas object platform, and issuing the instruction for obtaining gas equipment-related data to the smart gas object platform; and
receiving the instruction for obtaining gas equipment-related data issued by the smart gas sensing network platform through the smart gas object platform and uploading the gas equipment-related data to the smart gas sensing network platform, wherein the smart gas object platform is configured as a variety of gas and monitoring devices.
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