| CPC G06Q 50/06 (2013.01) [F17D 5/005 (2013.01); G06Q 10/06375 (2013.01); G06Q 30/018 (2013.01)] | 9 Claims |
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1. A method for gas pipeline network supervision based on smart gas, wherein the method is implemented based on a smart gas pipeline network safety management platform of an Internet of Things (IoT) system for gas pipeline network supervision based on smart gas, the IoT system further includes a smart gas user platform, a smart gas service platform, a smart gas sensor network platform, and a smart gas object platform, wherein the smart gas pipeline network safety management platform includes a smart gas pipeline network inspection management sub-platform and a smart gas data center, the smart gas sensor network platform includes a smart gas pipeline network inspection project sensor network sub-platform, the smart gas object platform includes one or more smart gas pipeline network inspection project object sub-platforms, wherein
the smart gas user platform includes a terminal device;
the smart gas object platform includes a gas device and an inspection project device, the gas device includes a pipeline network device, the pipeline network device includes pipelines and gate stations, and the inspection project device includes inspection vehicles and alarm devices;
and the method comprises:
obtaining an area to be inspected through the smart gas data center;
determining one or more downstream users based on the area to be inspected, and obtaining gas consumption data of each of the one or more downstream users through the smart gas sensor network platform;
determining, based on the gas consumption data of the one or more downstream users, peak-valley features of gas consumption at a future time;
determining a plurality of optional time points based on the peak-valley features of gas consumption;
predicting future gas consumption features based on one or more change values of accessibility, the plurality of optional time points, and the gas consumption data of the one or more downstream users through a prediction model, wherein:
the future gas consumption features are gas consumption features after inspection based on each of the plurality of optional time points; and
the accessibility is used to represent whether a gas source node is capable of normally supplying gas to a user node;
the prediction model is a machine learning model, wherein the prediction model is obtained by a training process based on a plurality of training samples with labels, the training process including:
inputting the plurality of the training samples into an initial prediction model;
constructing a loss function based on the labels and results of the initial prediction model;
updating parameters of the initial prediction model iteratively based on the loss function; and
obtaining a trained prediction model when a preset training condition of the initial prediction model is satisfied, wherein the preset training condition includes that the loss function converges or a count of iterations reaches a threshold; and
the training samples including historical gas consumption data of the each of the one or more downstream users before a plurality of historical inspection time points; and
the labels being historical gas consumption features after inspection based on the plurality of historical inspection time points;
determining a target inspection time point based on the future gas consumption features;
automatically arranging an unexecuted inspection plan according to the target inspection time;
sending a prompt and an alarm according to a preset threshold to the smart gas user platform for controlling the terminal device to feed information back to a user;
generating inspection reminder instructions based on the target inspection time point through an inspection time warning module of the smart gas pipeline network inspection management sub-platform, wherein the inspection reminder instructions are sent to a corresponding smart gas pipeline network inspection project object sub-platform by the smart gas data center through the smart gas pipeline network inspection project sensor network sub-platform of the smart gas sensor network platform; and
controlling the pipelines, the gate stations, and the inspection vehicles, by a computer, deployed in different areas of pipeline network to execute the inspection reminder instructions.
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