Embodiments are directed to a redundant control system that includes a high availability computing cluster configured to communicate with various devices. The high availability computing cluster includes a first computing system that controls the devices. The first computing system has an assigned network address and hosts a web server via a shared network address. The cluster also includes a second computing system that backs up the first computing system. The first and second computing systems are interchangeable with each other, such that if the first computing systems fails, the second computing system assumes control of the devices and takes over hosting the web server via the shared network address. The cluster also includes a software application configured to run independently on either computing system. The application may be configured to operate the device. The high availability computing cluster also includes a redundant hardware interface that communicates commands to the device.

The present disclosure provides a method for noise control based on a smart gas platform, wherein the method is executed by a smart gas safety management platform of an Internet of Things (IoT) system for noise control based on the smart gas platform, comprising: obtaining noise data of a gas field station through a sound sensor, the sound sensor being arranged at least one monitoring position of the gas field station, and any one monitoring position having a corresponding monitoring period; determining noise change features of the at least one monitoring position based on the noise data; and determining target operating parameters of the gas field station based on the noise change features, the target operating parameters including a target gas flow rate of a gas pipeline in the gas field station.

Automated operation of well site pumping equipment, including generating a mathematical belief model for maintaining an interrelationship between flow rate achievable by a pump unit discharge pressure of the pump unit, and probability of achieving the flow rate at corresponding discharge pressure. Speed of the pump unit is controlled to achieve a target speed based on a flow rate set-point and the mathematical belief model and updating the mathematical belief model at least while the target speed is achieved. Updating the mathematical belief model may include increasing the probability of achieving the flow rate set-point when actual flow rate of the pump unit is not less than the flow rate set-point and decreasing the probability of achieving the flow rate set-point when the actual flow rate of the pump unit is less than the flow rate set-point.

Embodiments are directed to a gas supply system, a gas panel monitoring system and to a gas distribution platform controlled via a high availability computing cluster. In one case, a gas supply system is provided which includes a gas panel defining an enclosure that includes a gas dispensing manifold. Gas flow through the gas dispensing manifold is regulated using solenoids. The gas supply system also includes a redundant control system that is electrically connected to the solenoids. The redundant control system is configured to send actuation signals to the solenoids to allow or prevent gas from flowing through the gas dispensing manifold. The gas supply system further includes a communications module that allows the redundant control system to monitor multiple gas panels. The monitoring includes receiving feedback from gas cabinet components regarding component operational status, and also transmitting actuation signals from the redundant control system to the gas panel.

This present disclosure provides a method for regulating a rated outlet pressure of a gate station compressor for smart gas, which is implemented based on an Internet of Things system for regulating a rated outlet pressure of a gate station compressor for smart gas. The method includes: obtaining user features of a downstream gas usage based on the smart gas object platform, the user features including at least a user type and at least one of downstream flow prediction values of a plurality of future moments, wherein the downstream flow prediction values are obtained by a downstream flow prediction model based on a historical downstream flow sequence; obtaining operation parameters of a compressor, the operation parameters including at least a rated outlet pressure set by the compressor; and determining a rated outlet pressure adjustment amount of the compressor based on the user features and the operation parameters.

The present disclosure provides a method for determining a gas pipeline network opening scheme based on smart gas, which is performed by a smart gas management platform. The smart gas management platform comprises a smart user service management sub-platform, a smart operation management sub-platform and a smart gas data center. The method comprises: obtaining, by the smart gas data center, a region feature of each region within a target range through a smart gas sensing network platform; determining, by the smart operation management sub-platform, a gas demand degree of the each region based on the region feature of the each region; determining, by the smart operation management sub-platform, a region as a first-class region based on the gas demand degree in the region meeting a preset condition, and determining the gas pipeline network opening scheme for the region.

Methods for noise reduction at a smart gas field station, Internet of Things (IoT) systems, and storage media are provided. The method may include obtaining relevant data of a target field station, the relevant data including at least one of operating data of the target field station, noise data of the target field station, and a pressure regulation parameter of an associated field station; predicting, based on the relevant data, noise enhancement data of the target field station for at least one future period; and determining a noise reduction control parameter based on the noise enhancement data and the pressure regulation parameter. The IoT system may include a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas pipeline network equipment sensing network platform, and a smart gas pipeline network equipment object platform.

There is provided techniques for determining an estimate of gas emissions in a landfill. Some techniques provide for obtaining a plurality of measures and determining an estimate of methane emissions by using a gas emissions model to process the plurality of measures, and outputting the estimate of methane emissions. The plurality of measures may include emissions measurements such as methane emissions and/or measures of at least one environmental characteristic such as wind speed and/or direction, turbulence, and/or atmospheric stability. The estimate of gas emissions may be displayed and/or used to determine whether to cause a corrective action to be performed, such as adjusting a flow rate of landfill gas being extracted from the landfill.

Embodiments of the present disclosure provide a method, an Internet of Things system, and a storage medium for smart gas data management. The method includes: obtaining at least one type of raw gas data uploaded by at least one platform in the IoT system, wherein the raw gas data includes at least one of gas transportation data, gas pipeline data, and gas equipment data; evaluating a time reliability and a data reliability of the at least one type of the raw gas data; and generating at least one storage instruction based on the time reliability and the data reliability of the at least one type of the raw gas data to store the raw gas data in a corresponding data storage area.

Automated operation of well site pumping equipment, including generating a mathematical belief model for maintaining an interrelationship between flow rate achievable by a pump unit discharge pressure of the pump unit, and probability of achieving the flow rate at corresponding discharge pressure. Speed of the pump unit is controlled to achieve a target speed based on a flow rate set-point and the mathematical belief model and updating the mathematical belief model at least while the target speed is achieved. Updating the mathematical belief model may include increasing the probability of achieving the flow rate set-point when actual flow rate of the pump unit is not less than the flow rate set-point and decreasing the probability of achieving the flow rate set-point when the actual flow rate of the pump unit is less than the flow rate set-point.