Methods and internet of things systems for obtaining natural gas energy metering component
The embodiments of the present disclosure provide a method for obtaining a natural gas energy metering component, including obtaining a temperature and pressure of a natural gas sample to be detected at a first site based on an object platform; obtaining sample data of a natural gas sample at a second site based on the object platform, the sample data including a natural gas component and a temperature and pressure of the natural gas component; obtaining sample data of a natural gas sample at the first site based on the object platform; determining a natural gas component of the natural gas sample to be detected at the first site based on sample data at the first site and the second site aggregated by a sensor network platform and the temperature and pressure of the natural gas sample to be detected at the first site.
1. A method for obtaining a natural gas energy metering component, which is implemented by a management platform, comprising:
obtaining a temperature and pressure of a natural gas sample to be detected at a first site based on an object platform;
obtaining sample data of a natural gas sample at a second site based on the object platform, the sample data including a natural gas component of the natural gas sample at the second site and a temperature and pressure of the natural gas component;
obtaining sample data of a natural gas sample at the first site based on the object platform, the sample data including a natural gas component of the natural gas sample at the first site and a temperature and pressure of the natural gas component; and
determining a natural gas component of the natural gas sample to be detected at the first site through a prediction model based on the sample data at the first site and the second site aggregated by a sensor network platform and the temperature and pressure of the natural gas sample to be detected at the first site, wherein
the prediction model is a machine learning model, including a plurality of transformation layers and a prediction layer;
an input of the plurality of transformation layers includes the natural gas component of the natural gas sample at the first site, the natural gas component of the natural gas sample at the second site, the temperature and pressure of the natural gas component at the first site, and the temperature and pressure of the natural gas component at the second site; and
an output of the plurality of transformation layers includes a temperature, pressure, and composition data of the natural gas components at the first site and the second site after reduction;
an input of the prediction layer includes the temperature and pressure of the natural gas sample to be detected and the output of the plurality of transformation layers;
an output of the prediction layer includes component data of the natural gas sample to be detected during extraction, and
training of the prediction model includes:
pre-training the transformation layers to obtain pre-trained transformation layers; and
jointly training the pre-trained transformation layers and the prediction layer to obtain the prediction model.
2. The method of claim 1 , wherein the obtaining a temperature and pressure of a natural gas sample to be detected at a first site based on an object platform comprises:
performing a reduction process on the natural gas sample to be detected, and obtaining a plurality of natural gas samples to be detected at a plurality of temperatures and pressures during the reduction based on the object platform.
3. The method of claim 1 , wherein the determining the natural gas component of the natural gas sample to be detected at the first site through a prediction model based on the sample data at the first site and the second site aggregated by a sensor network platform and the temperature and pressure of the natural gas sample to be detected at the first site comprises:
determining, based on a correction coefficient, the sample data at the first site, the sample data at the second site, and the temperature and pressure of the natural gas sample to be detected at the first site, the natural gas component of the natural gas sample to be detected at the first site through the prediction model;
wherein the correction coefficient is determined based on a temperature and/or pressure of the natural gas sample to be detected before the reduction and a temperature and/or pressure during the extraction.
4. An Internet of Things (IoT) system for obtaining a natural gas energy metering component, comprising an object platform, a sensor network platform, a management platform, wherein the management platform is configured to:
obtain a temperature and pressure of a natural gas sample to be detected at a first site based on an object platform;
obtain sample data of a natural gas sample at a second site based on the object platform, the sample data including a natural gas component of the natural gas sample at the second site and a temperature and pressure of the natural gas component;
obtain sample data of a natural gas sample at the first site based on the object platform, the sample data including a natural gas component of the natural gas sample at the first site and a temperature and pressure of the natural gas component; and
determine a natural gas component of the natural gas sample to be detected at the first site through a prediction model based on the sample data at the first site and the second site aggregated by a sensor network platform and the temperature and pressure of the natural gas sample to be detected at the first site, wherein
the prediction model is a machine learning model, including a plurality of transformation layers and a prediction layer;
an input of the plurality of transformation layers includes the natural gas component of the natural gas sample at the first site, the natural gas component of the natural gas sample at the second site, the temperature and pressure of the natural gas component at the first site, and the temperature and pressure of the natural gas component at the second site; and
an output of the plurality of transformation layers includes a temperature, pressure, and composition data of the natural gas components at the first site and the second site after reduction;
an input of the prediction layer includes the temperature and pressure of the natural gas sample to be detected and the output of the plurality of transformation layers;
an output of the prediction layer includes component data of the natural gas sample to be detected during extraction, and
training of the prediction model includes:
pre-training the transformation layers to obtain pre-trained transformation layers; and
jointly training the pre-trained transformation layers and the prediction layer to obtain the prediction model.
5. The IoT system of claim 4 , wherein the object platform is further configured to:
obtain a plurality of natural gas samples to be detected at a plurality of temperatures and pressures in a process of reducing the natural gas sample to be detected.
6. The IoT system of claim 4 , wherein the management platform is further configured to:
determine, based on a correction coefficient, the sample data at the first site, the sample data at the second site, and the temperature and pressure of the natural gas sample to be detected at the first site, the natural gas component of the natural gas sample to be detected at the first site through the prediction model;
wherein the correction coefficient is determined based on a temperature and/or pressure of the natural gas sample to be detected before the reduction and a temperature and/or pressure during the extraction.
7. A computer-readable storage medium storing computer instructions, wherein when reading the computer instructions in the storage medium, a computer implements the method for obtaining the natural gas energy metering component of claim 1 .