Methods for regulating flow meters of industrial and commercial units of smart gas, internet of things (IOT) systems and media thereof

An Internet of Things (IoT) system for regulating a flow meter of an industrial and commercial unit of a smart gas, comprising:

• a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform which interact in sequence, wherein the smart gas user platform is configured to upload user feedback data to the smart gas management platform via the smart gas service platform based on a gas user sub-platform, and the smart gas service platform is configured to obtain continuous gas supply parameters from the smart gas management platform and send the continuous gas supply parameters to the smart gas user platform, the smart gas object platform comprises a gas pipeline network equipment object sub-platform, the smart gas object platform includes a valve control equipment, wherein the smart gas management platform is configured to:
• obtain operation data, a time feature, and the user feedback data, the operation data including gas usage data and gas outage feedback data of the industrial and commercial unit;
• determine, based on the operation data, the time feature, and the user feedback data, a gas flow trend of a low balance user;
• and
• determine, based on the gas flow trend, the time feature, and the gas outage feedback data, the continuous gas supply parameters of the low balance user, the continuous gas supply parameters including continuous gas supply quota and a continuous gas supply time, wherein the continuous gas supply quota includes gas supply quotas for a plurality of time periods;
• predicting, based on the gas flow trend and the time feature, a planned gas outage time;
• determining, based on the planned gas outage time, the time feature, and the gas outage feedback data, the continuous gas supply parameters;
• determining, based on a candidate continuous gas supply time, the planned gas outage time, the time feature, and the gas outage feedback data, a gas outage influence value, the gas outage influence value refers to a value for assessing how a gas outage affects an user;
• determining, based on the candidate continuous gas supply time, the planned gas outage time, the time feature, the gas outage feedback data, and the gas flow trend, the gas flow trend refers to a gas flow value reflecting a future gas usage trend, the gas outage influence value through a first model, wherein the first model is a neural network model, wherein the first model is obtained by a training process using a plurality of training samples with labels, wherein the training samples include sample historical continuous gas supply times, sample historical planned gas outage times, sample historical time features, sample historical gas flow trends, and sample historical gas outage feedback data of a sample historical moment, wherein the historical gas outage feedback data refers to the gas outage feedback data prior to the historical moment, wherein the labels are labeled based on the gas outage influence value corresponding to the historical continuous gas supply times, wherein the gas outage influence value is determined based on subsequent user feedback, wherein the subsequent user feedback includes a user gas application time and a user attitude feedback score after an actual gas outage;
• determining, based on the gas outage influence value, the continuous gas supply time;
• and
• controlling the smart gas object platform, based on the continuous gas supply parameters, to utilize the valve control equipment to continue supplying gas to the low balance user based on the continuous gas supply quota and the continuous gas supply time;
• after a paid gas quota of the low balance user is used up, continuing to supply the gas to the low balance user for a period of time, and allocating different gas supply quotas for the low balance user for different time periods in a time-gas supply distribution,
• wherein the determining based on the gas flow trend of the low balance user based on the operation data, the time feature, and the user feedback data includes:
• determining, based on the operation data, the time feature, and the user feedback data, a customer flow feature of a future time period;
• and
• determining, based on the customer flow feature of the future time period and the time feature, the gas flow trend;
• and
• wherein the determining, based on the customer flow feature of the future time period and the time feature, the gas flow trend includes:
• determining, based on the customer flow feature of the future time period and the time feature, the gas flow trend through a second model, the second model being a machine learning model;
• and
• wherein the determining, based on the operation data, the time feature, and the user feedback data, the customer flow feature of the future time period includes:
• determining, based on the operation data and the time feature, a first candidate customer flow feature;
• determining, based on the user feedback data, a second candidate customer flow feature;
• and
• determining, based on the first candidate customer flow feature and the second candidate customer flow feature, the customer flow feature of the future time period;
• and
• wherein the determining, based on the first candidate customer flow feature and the second candidate customer flow feature, the customer flow feature of the future time period includes:
• determining the customer flow feature of the future time period by performing a weighted summation on the first candidate customer flow feature and the second candidate customer flow feature.

A non-transitory computer-readable storage medium storing computer instructions that, when executed by a processor, direct the processor to perform operations comprising:

• regulating a flow meter of an industrial and commercial unit of a smart gas, wherein an Internet of Things (IoT) system further comprises a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform, wherein the smart gas user platform is configured to upload user feedback data to the smart gas management platform via the smart gas service platform based on a gas user sub-platform, and the smart gas service platform is configured to obtain continuous gas supply parameters from the smart gas management platform and send the continuous gas supply parameters to the smart gas user platform, the smart gas object platform comprises a gas pipeline network equipment object sub-platform, the smart gas object platform includes a valve control equipment;
• obtaining operation data, a time feature, and the user feedback data, the operation data including gas usage data and gas outage feedback data of the industrial and commercial unit;
• determining, based on the operation data, the time feature, and the user feedback data, a gas flow trend of a low balance user;
• and
• determining, based on the gas flow trend, the time feature, and the gas outage feedback data, the continuous gas supply parameters of the low balance user, the continuous gas supply parameters including continuous gas supply quota and a continuous gas supply time, wherein the continuous gas supply quota includes gas supply quotas for a plurality of time periods;
• predicting, based on the gas flow trend and the time feature, a planned gas outage time;
• determining, based on the planned gas outage time, the time feature, and the gas outage feedback data, the continuous gas supply parameters;
• determining, based on a candidate continuous gas supply time, the planned gas outage time, the time feature, and the gas outage feedback data, a gas outage influence value, the gas outage influence value refers to a value for assessing how a gas outage affects an user;
• determining, based on the candidate continuous gas supply time, the planned gas outage time, the time feature, the gas outage feedback data, and the gas flow trend, the gas flow trend refers to a gas flow value reflecting a future gas usage trend, the gas outage influence value through a first model, wherein the first model is a neural network model, wherein the first model is obtained by a training process using a plurality of training samples with labels, wherein the training samples include sample historical continuous gas supply times, sample historical planned gas outage times, sample historical time features, sample historical gas flow trends, and sample historical gas outage feedback data of a sample historical moment, wherein the historical gas outage feedback data refers to the gas outage feedback data prior to the historical moment, wherein the labels are labeled based on the gas outage influence value corresponding to the historical continuous gas supply times, wherein the gas outage influence value is determined based on subsequent user feedback, wherein the subsequent user feedback includes a user gas application time and a user attitude feedback score after an actual gas outage;
• determining, based on the gas outage influence value, the continuous gas supply time;
• and
• controlling the smart gas object platform, based on the continuous gas supply parameters, to utilize the valve control equipment to continue supplying gas to the low balance user based on the continuous gas supply quota and the continuous gas supply time;
• after a paid gas quota of the low balance user is used up, continuing to supply the gas to the low balance user for a period of time, and allocating different gas supply quotas for the low balance user for different time periods in a time-gas supply distribution,
• wherein the determining based on the gas flow trend of the low balance user based on the operation data, the time feature, and the user feedback data includes:
• determining, based on the operation data, the time feature, and the user feedback data, a customer flow feature of a future time period;
• and
• determining, based on the customer flow feature of the future time period and the time feature, the gas flow trend;
• and
• wherein the determining, based on the customer flow feature of the future time period and the time feature, the gas flow trend includes:
• determining, based on the customer flow feature of the future time period and the time feature, the gas flow trend through a second model, the second model being a machine learning model;
• and
• wherein the determining, based on the operation data, the time feature, and the user feedback data, the customer flow feature of the future time period includes:
• determining, based on the operation data and the time feature, a first candidate customer flow feature;
• determining, based on the user feedback data, a second candidate customer flow feature;
• and
• determining, based on the first candidate customer flow feature and the second candidate customer flow feature, the customer flow feature of the future time period;
• and
• wherein the determining, based on the first candidate customer flow feature and the second candidate customer flow feature, the customer flow feature of the future time period includes:
• determining the customer flow feature of the future time period by performing a weighted summation on the first candidate customer flow feature and the second candidate customer flow feature.