Monitoring and early warning method for gas gate station and internet of things system thereof

An Internet of Things (IoT) system for monitoring and early warning of a gas gate station, comprising a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas sensor network platform, and a smart gas pipeline network device object platform, wherein the smart gas safety management platform is configured to:

• obtain a downstream user feature corresponding to the gas gate station and historical warning data corresponding to the gas gate station;
• determine, based on the downstream user feature and the historical warning data and in combination with operation data of the gas gate station, an associated node related to the gas gate station in a preset time period, wherein the associated node including at least one of a gate station internal node and an external pipeline network node;
• obtain monitoring data of the associated node, wherein the monitoring data at least includes gate station operation data of the gate station internal node and pipeline network monitoring data of the external pipeline network node;
• determine a pipeline network monitoring feature based on the pipeline network monitoring data;
• obtain a plurality of second samples, wherein the second samples include sample current monitoring data, sample historical monitoring data, sample monitoring feature, and sample operation data;
• train an initial prediction model based on the plurality of second samples with second labels to obtain a prediction model, wherein the plurality of second labels include sample ideal operation data, and parameters in the initial prediction model are iteratively updated based on a loss function constructed based on an output of the initial prediction model and the second labels;
• determine ideal operation data of the gas gate station at a future time through the prediction model based on gate station monitoring data of the gas gate station, the gate station operation data, and the pipeline network monitoring feature, wherein the prediction model is a machine learning model;
• and
• issue an early warning notification in response to a difference between the gate station monitoring data and the ideal operation data exceeding a dynamic difference threshold.

A non-transitory computer-readable storage medium storing computer instructions, wherein a computer executes a monitoring and early warning method for a gas gate station when reading the computer instructions, the method comprising:

• obtaining a downstream user feature corresponding to the gas gate station and historical warning data corresponding to the gas gate station;
• determining, based on the downstream user feature and the historical warning data and in combination with operation data of the gas gate station, an associated node related to the gas gate station in a preset time period, wherein the associated node including at least one of a gate station internal node and an external pipeline network node;
• obtaining monitoring data of the associated node, wherein the monitoring data at least includes gate station operation data of the gate station internal node and pipeline network monitoring data of the external pipeline network node;
• determining a pipeline network monitoring feature based on the pipeline network monitoring data;
• obtaining a plurality of second samples, wherein the second samples include sample current monitoring data, sample historical monitoring data, sample monitoring feature, and sample operation data;
• training an initial prediction model based on the plurality of second samples with second labels to obtain a prediction model, wherein the plurality of second labels include sample ideal operation data, and parameters in the initial prediction model are iteratively updated based on a loss function constructed based on an output of the initial prediction model and the second labels;
• determining ideal operation data of the gas gate station at a future time through the prediction model based on gate station monitoring data of the gas gate station, the gate station operation data, and the pipeline network monitoring feature, wherein the prediction model is a machine learning model;
• and
• issuing an early warning notification in response to a difference between the gate station monitoring data and the ideal operation data exceeding a dynamic difference threshold.