Embodiments of the present disclosure provide a method for smart gas data storage, an Internet of Things system, and a storage medium. The method includes: obtaining at least one type of raw gas data, calculating collection time characteristics of the raw gas data based on a collection time stamp when the raw gas data is collected; evaluating a time reliability of the raw gas data based on the collection time characteristics; determining at least one set of gas sampling data of the raw gas data based on gas importance of the raw gas data; evaluating a data reliability of the raw gas data based on the at least one set of gas sampling data; and generating at least one storage instruction based on the time reliability and the data reliability to store the raw gas data in a corresponding data storage area.

There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.

The present disclosure provides a method and an Internet of Things system for managing a gas pressure based on smart gas. The method includes: determining a gas in-home pressure regulation scheme, and the gas in-home pressure regulation scheme including a pressure regulation parameter of a gas device; generating, based on the gas in-home pressure regulation scheme, a pressure regulation instruction; regulating, based on the pressure regulation instruction, a gas in-home pressure of at least one floor; and transmitting the gas in-home pressure regulation scheme to a smart gas user terminal.

A method for process control with process measurements validation capability involves obtaining a first actual sensor reading of a process variable from a field sensor to be monitored, and obtaining a first virtual sensor reading of the process variable from a virtual sensor. The virtual sensor is camera-based. The method further involves calculating a first deviation between the first actual sensor reading and the first virtual sensor reading, making a first determination that the first deviation exceeds a prespecified threshold, based on the first determination, making a second determination that the first actual sensor reading does not correspond to first related sensors readings, and based on the second determination, making the first virtual sensor reading a first trusted sensor reading for controlling an aspect of a process associated with the process variable.

Embodiments of the present disclosure provide a method and an Internet of Things system for centralized heating gas supervision based on a supervision platform. The method includes obtaining an indoor environmental feature of each sub-region in a target region and gas consumption data of a heat supply source in the target region by a gas company sensor network plat form, the indoor environmental feature and the gas consumption data being collected by a smart gas device object platform; determining whether heating data in the target region meets a preset condition based on the indoor environmental feature of each sub-region and the gas consumption data; generating an early warning message in response to determining that the heating data does not meet the preset condition, and sending the early warning message to a gas user platform by a gas user service platform.

Commanding GTs to base load level based upon measured ambient condition for each GT; commanding each GT to adjust a power output to match scaled power output value equal to a fraction of a difference between the respective power output and a nominal power output value, and measuring actual emissions value for each GT during the adjusting of the respective power output; and adjusting an operating condition of each GT in the set of GTs based upon a difference between the respective measured actual emissions value, a nominal emissions value at the ambient condition and an emissions scale factor, wherein the nominal emissions value at the ambient condition and the emissions scale factor are stored in a pre-existing emissions model for the GT.

The embodiments of the present disclosure provide methods for controlling natural gas efficiency enhancement of smart gas, smart gas Internet of Things (IoT) systems and mediums. The method may comprise obtaining at least one user need through a smart gas user platform based on a smart gas service platform, the user need including a gas usage target need; determining at least one optimization objective based on the at least one user need; determining a target proportioning feature of a synergist according to the at least one optimization objective, the target proportioning feature including a proportioning vector and an addition proportion of the synergist; and configuring a target synergist that satisfies the target proportioning feature to enhance efficiency of natural gas by sending the target proportioning feature to a smart gas object platform through a smart gas sensor network platform.

Commanding GTs to base load level based upon measured ambient condition for each GT; commanding each GT to adjust a power output to match scaled power output value equal to a fraction of a difference between the respective power output and a nominal power output value, and measuring actual emissions value for each GT during the adjusting of the respective power output; and adjusting an operating condition of each GT in the set of GTs based upon a difference between the respective measured actual emissions value, a nominal emissions value at the ambient condition and an emissions scale factor, wherein the nominal emissions value at the ambient condition and the emissions scale factor are stored in a pre-existing emissions model for the GT.

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.

The embodiments of the present disclosure provide a method for optimizing performance indicators of smart gas, a smart gas Internet of Things (IoT) system, and a medium. The method may comprise obtaining at least one user need through the smart gas user platform based on the smart gas service platform; determining at least one optimization objective based on the at least one user need; determining a constraint that a target proportioning feature satisfies according to the at least one optimization objective; generating at least one candidate proportioning feature according to the constraint; performing at least one round of iterative processing on the at least one candidate proportioning feature, and determining the candidate proportioning feature and an evaluation value after each round of iteration until the evaluation value satisfies the constraint; and determining the candidate proportioning feature with the evaluation value satisfying the constraint as the target proportioning feature.