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CN114740159A - Natural gas energy metering component acquisition method and Internet of things system - Google Patents

Natural gas energy metering component acquisition method and Internet of things system Download PDF

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CN114740159A
CN114740159A CN202210387064.6A CN202210387064A CN114740159A CN 114740159 A CN114740159 A CN 114740159A CN 202210387064 A CN202210387064 A CN 202210387064A CN 114740159 A CN114740159 A CN 114740159A
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natural gas
site
sample
detected
pressure
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CN114740159B (en
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邵泽华
向海堂
李勇
刘彬
权亚强
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Chengdu Qinchuan IoT Technology Co Ltd
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Chengdu Qinchuan IoT Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
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    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/20—Information sensed or collected by the things relating to the thing itself
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
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    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
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Abstract

The embodiment of the specification provides a natural gas energy metering component obtaining method which comprises the steps of obtaining the temperature and the pressure of a natural gas sample to be detected of a first station on the basis of an object platform; acquiring sample data of a natural gas sample of a second station based on the object platform, wherein the sample data comprises natural gas components and corresponding temperature and pressure; acquiring sample data of a natural gas sample of a first site based on an object platform; and determining the natural gas components of the natural gas sample to be detected of the first site based on the sample data of the first site and the second site and the temperature and pressure of the natural gas sample to be detected of the first site, which are gathered by the sensing network platform.

Description

Natural gas energy metering component acquisition method and Internet of things system
Technical Field
The specification relates to the technical field of Internet of things systems and natural gas, in particular to a natural gas energy metering component acquisition method and an Internet of things system.
Background
The natural gas is mainly conveyed through a long-distance pipeline network, and because a pipeline conveying path and a natural gas source are complex, the composition difference of different gas sources is large. When gas is transmitted and distributed through pressure regulation of different stations, the gas pressure and the temperature change, volume components of different gases in the natural gas can also change, and the data of natural gas components of subordinate stations are inaccurate. And the direct component detection of the natural gas at the subordinate site further increases the metering cost.
Therefore, it is desirable to provide a natural gas energy metering component acquisition method, which determines the natural gas components of the natural gas sample to be detected through sample data and temperature pressure analysis of a station, so that the determination of the natural gas components can be more accurate.
Disclosure of Invention
One or more embodiments of the present description provide a natural gas energy metering component acquisition method. The natural gas energy metering component obtaining method comprises the following steps: acquiring the temperature and the pressure of a natural gas sample to be detected at a first site based on an object platform; acquiring sample data of a natural gas sample of a second station based on the object platform, wherein the sample data comprises natural gas components and corresponding temperature and pressure; acquiring sample data of a natural gas sample of a first site based on an object platform; the sample data of the second site and the sample data of the first site are determined in different modes; and determining the natural gas component of the natural gas sample to be detected of the first site based on the sample data of the first site and the second site summarized by the sensing network platform and the temperature and the pressure of the natural gas sample to be detected of the first site.
One or more embodiments of the present description provide a natural gas composition acquisition system. The natural gas composition acquisition system comprises an object platform, a sensing network platform and a management platform, wherein the management platform is configured to execute the following operations: acquiring the temperature and the pressure of a natural gas sample to be detected of a first station based on an object platform; acquiring sample data of a natural gas sample of the second station based on the object platform, wherein the sample data comprises natural gas components and corresponding temperature and pressure; acquiring sample data of a natural gas sample of a first site based on an object platform; the determination mode of the sample data of the second site is different from that of the sample data of the first site; and determining the natural gas components of the natural gas sample to be detected of the first site based on the sample data of the first site and the second site and the temperature and pressure of the natural gas sample to be detected of the first site, which are gathered by the sensing network platform.
One or more embodiments of the present description provide a computer-readable storage medium storing computer instructions that, when read by a computer, cause the computer to perform a natural gas energy metering composition acquisition method.
One or more embodiments of the present description provide a natural gas energy metric constituent acquisition device that includes a processor for processing a natural gas energy metric constituent acquisition method.
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The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
fig. 1 is a diagram of an application scenario of an internet of things system for gas energy metering composition acquisition in accordance with some embodiments of the present description;
FIG. 2 is a schematic diagram of an Internet of things system for natural gas energy metering component acquisition in accordance with certain embodiments herein;
FIG. 3 is an exemplary flow diagram of a natural gas energy metering component acquisition method according to some embodiments herein;
FIG. 4 is a schematic flow diagram illustrating the determination of the composition of a natural gas sample to be tested according to some embodiments of the present disclosure;
FIG. 5 is a schematic flow diagram illustrating the process of obtaining constituents of a natural gas sample to be tested for extraction according to some embodiments of the present disclosure;
FIG. 6 is a schematic diagram of a process for obtaining constituents of a natural gas sample to be tested during extraction according to some embodiments of the present disclosure;
fig. 7 is a schematic flow diagram illustrating the process of obtaining constituents of a natural gas sample to be tested for extraction according to some embodiments of the present disclosure.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, without inventive effort, the present description can also be applied to other similar contexts on the basis of these drawings. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
The embodiment of the application relates to a natural gas energy metering component acquisition method and an internet of things system. The method can be applied to various fields needing to obtain natural gas components, such as the fields of civil fuels, industrial fuels, process production, chemical raw material production and processing, compressed natural gas and the like, and is not limited herein.
Fig. 1 is a diagram of an application scenario of an internet of things system for gas energy metering component acquisition according to some embodiments of the present disclosure.
Server 110, network 120, database 130, site 140, terminal device 150, detection device 160 may be included in application scenario 100. Server 110 may include a processing device 112.
In some embodiments, the natural gas composition acquisition application scenario 100 may determine the natural gas composition of the natural gas sample to be detected at the first site by implementing the methods and/or processes disclosed herein. For example, in a typical application scenario, when the gas pressure and temperature change when the natural gas is transported from the second site to the first site, the temperature and pressure of the natural gas sample to be detected at the first site 140-2 are obtained through the object platform; acquiring sample data of a natural gas sample of the second station 140-1 through the object platform; acquiring sample data of a natural gas sample of the first site 140-2 through the object platform; the natural gas components of the natural gas sample to be detected of the first station 140-2 are determined through the sample data of the first station 140-2 and the second station 140-1 and the temperature and pressure of the natural gas sample to be detected of the first station 140-2 which are gathered by the sensing network platform. The natural gas component can be determined more conveniently and accurately.
The server 110 and the terminal device 150 may be connected through the network 120, and the database 130 may be connected to the server 110 through the network 120, directly connected to the server 110, or inside the server 110.
The server 110 may be used to manage resources and process data and/or information from at least one component of the present system or an external data source (e.g., a cloud data center). In some embodiments, the natural gas component of the natural gas sample to be detected at the first site 140-2 may be determined after processing by the server 110. Server 110 may retrieve data on database 130 or save data to database 130 at the time of processing. In some embodiments, the server 110 may be a single server or a group of servers. In some embodiments, the server 110 may be regional or remote. In some embodiments, the server 110 may be implemented on a cloud platform, or provided in a virtual manner.
In some embodiments, the server 110 may include a processing device 112. Operations in this description may be performed by processing device 112 executing program instructions. Processing device 112 may process data and/or information obtained from other devices or system components. The processor may execute program instructions based on the data, information, and/or processing results to perform one or more of the functions described herein. In some embodiments, the processing device 112 may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core processing device). For example only, the processing device 112 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), the like, or any combination thereof.
The network 120 may connect the components of the natural gas component acquisition application scenario 100 and/or connect the system with external resource components. In some embodiments, the sample data of the first site 140-2 and the second site 140-1, and the temperature and pressure data of the natural gas sample to be detected at the first site 140-2 may be communicated over the network 120. Network 120 enables communication between the various components and with other components outside the system to facilitate the exchange of data and/or information.
In some embodiments, the network 120 may be any one or more of a wired network or a wireless network. For example, the network 120 may include a cable network, a fiber network, and the like, or any combination thereof. The network connection between the parts can be in one way or in multiple ways. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies. In some embodiments, network 120 may include one or more network access points.
Database 130 may be used to store data and/or instructions. In some embodiments, the database 130 may be used to provide the natural gas composition acquisition application scenario 100 with sample data of a natural gas sample of the second site 140-1, sample data of a natural gas sample of the first site 140-2, and so on. Database 130 may be implemented in a single central server, multiple servers connected by communication links, or multiple personal devices. In some embodiments, database 130 may be included in server 110, terminal device 150, and possibly other system components.
The station 140 refers to various natural gas stations in natural gas pipeline engineering, and the station 140 mainly receives natural gas, pressurizes pipeline natural gas, separately delivers natural gas, distributes gas, stores gas, adjusts peak, and the like. Stations 140 may include gas delivery head stations, gas delivery end stations, intermediate stations, etc., depending on the location in the gas delivery pipeline. Sites 140 may include natural gas valve stations, natural gas filling stations, natural gas storage and distribution stations, natural gas terminals, and the like, as may function in a gas pipeline.
In some embodiments, the site 140 may be provided with a detection device 160 of natural gas data that may be used to detect performance parameters of the natural gas, such as pressure, temperature, flow, composition, and the like. In some embodiments, the server 110 and/or the terminal device 150 may obtain sample data of the natural gas sample of the second site 140-1 and/or the first site 140-2 based on the detection device 160 via the network 120 or the database 130.
The sites 140 may include a first site 140-2 and a second site 140-1. Wherein the second site 140-1 may be a superior natural gas site of the first site 140-2 for exporting natural gas to its subordinate sites. The first station 140-2 may be a subordinate natural gas station of the second station 140-1 for receiving natural gas of the second station 140-1, one station may be an upper natural gas station and a subordinate natural gas station at the same time, and one station may correspond to a plurality of upper natural gas stations and a plurality of subordinate natural gas stations at the same time.
Terminal device 150 refers to one or more terminal devices or software. In some application scenarios, the user using the terminal device 150 may include a worker at the first site 140-2, a worker at the second site 140-1, a third party inspector, a customer, etc., and may also include other related personnel. In some embodiments, the user of terminal device 150 may be one or more users. In some embodiments, the terminal device 150 may be one or any combination of other devices having input and/or output capabilities, such as a mobile device 150-1, a tablet computer 150-2, a laptop computer 150-3, and the like. In some embodiments, processing device 112 may be included in terminal device 150, as well as other possible system components.
The detection device 160 refers to a device that measures the corresponding status data of the natural gas at the site.
The detection device 160 may include a temperature detection device 160-1, a pressure detection device 160-2, a composition testing device (not shown), a volume testing device (not shown), and the like. The composition testing device may include, for example, a chromatograph or the like. The temperature detection device 160-1 refers to a device that measures the temperature of the natural gas in the site, and may be implemented based on a temperature sensor, for example. Pressure sensing device 160-2 refers to a device that measures the natural gas pressure in the site, and may be implemented, for example, based on a pressure sensor.
It should be noted that the natural gas composition acquisition application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of the present application. It will be apparent to those skilled in the art that various modifications and variations can be made in light of the description herein. For example, the natural gas composition acquisition application scenario 100 may also include an information source. However, such changes and modifications do not depart from the scope of the present application.
The Internet of things system is an information processing system comprising a user platform, a service platform, a management platform, a sensing network platform and an object platform, wherein part or all of the platforms are arranged. The user platform is a leading person of the Internet of things operation system and can be used for obtaining user requirements, the user requirements are the basis and the premise formed by the Internet of things operation system, and the connection among the platforms of the Internet of things is used for meeting the requirements of users. The service platform is positioned between the user platform and the management platform, is a bridge for the connection between the user platform and the management platform, and provides input and output services for users. The management platform can realize overall planning and coordination of connection and cooperation among all functional platforms (such as a sensing network platform and an object platform), gathers information of the operation system of the Internet of things, and can provide sensing management and control management functions for the operation system of the Internet of things. The sensing network platform can realize the connection of the management platform and the object platform and has the functions of sensing information sensing communication and controlling information sensing communication. The object platform is a functional platform that performs perceptual information generation and control information.
The processing of information in the internet of things system can be divided into a processing flow of sensing information and a processing flow of control information, and the control information can be information generated based on the sensing information. The sensing information is processed by acquiring the sensing information by the object platform and transmitting the sensing information to the management platform through the sensing network platform. The control information is issued to the object platform by the management platform through the sensing network platform, and then the control of the corresponding object is realized.
FIG. 2 is an exemplary schematic diagram of a natural gas composition acquisition system according to some embodiments herein. As shown in fig. 2, the natural gas composition acquisition system may be implemented based on an internet of things system, and the internet of things system 200 for acquiring natural gas energy metering compositions includes an object platform 210, a sensor network platform 220, and a management platform 230. In some embodiments, the natural gas composition acquisition system 200 may be part of the processing plant 110 or implemented by the processing plant 110.
In some embodiments, the internet of things system 200 for gas energy metering component acquisition may be applied to gas delivery management. When applied to natural gas transportation management, the object platform 210 may be configured to collect data related to natural gas transportation, including first site data and second site data, for example, the first site data may be temperature and pressure of a natural gas sample to be detected at a first site, sample data of the natural gas sample at the first site, and the like; as another example, the second site data may be sample data of a natural gas sample of the second site, or the like. The object platform 210 may upload the collected data related to natural gas transportation to the sensor network platform 220, the sensor network platform 220 may perform summary processing on the collected data, and the management platform 230 determines the component data of the natural gas sample to be detected based on the data summarized by the sensor network platform 220.
In some embodiments, the object platform 210 may obtain information. The acquired information can be input as the information of the whole internet of things. The object platform 210 may be in communication with the sensor network platform 220, the object platform 210 being configured to collect terminals and obtain data. In some embodiments, the data acquired by the object platform 210 may include the temperature and pressure of the natural gas sample to be detected at the first site, the sample data of the natural gas sample at the second site, and the sample data of the natural gas sample at the first site. In some embodiments, the object platform 210 is configured to obtain a plurality of natural gas samples to be detected at a plurality of temperatures and pressures during the reduction process of the natural gas samples to be detected.
In some embodiments, the sensor network platform 220 may connect the management platform 230 and the object platform 210 to implement functions of sensing information and communicating control information. In some embodiments, the sensory network platform 220 aggregates the sample data of the first and second sites and the temperature and pressure of the natural gas sample to be detected at the first site.
In some embodiments, management platform 230 may refer to a platform that manages natural gas.
In some embodiments, the management platform 230 may be configured to determine the natural gas composition of the natural gas sample to be detected at the first site by obtaining the temperature and pressure of the natural gas sample to be detected at the first site, the sample data of the natural gas sample at the second site, and the sample data of the natural gas sample at the first site based on the object platform 210. In some embodiments, the sample data for the natural gas sample at the second site includes the natural gas composition and corresponding temperature and pressure. In some embodiments, the sample data for the second site is determined in a different manner than the sample data for the first site.
In some embodiments, the management platform 230 may be further configured to determine the natural gas composition of the natural gas sample to be detected based on the sample data of the first site, the sample data of the second site, and the temperature and pressure of the natural gas sample to be detected of the first site through the predictive model.
In some embodiments, the management platform 230 may be further configured to determine, through the predictive model, the natural gas composition of the natural gas sample to be detected based on the correction coefficient, the sample data of the first site, the sample data of the second site, and the temperature and pressure of the natural gas sample to be detected of the first site. In some embodiments, the correction factor is determined based on the temperature and/or pressure of the natural gas sample to be tested before reduction and the temperature and/or pressure at the time of extraction.
In some embodiments, the management platform 230 may be further configured to determine the calorific value of the natural gas sample to be detected based on the natural gas composition of the natural gas sample to be detected; and energy metering is carried out on the downstream gas meter based on the calorific value of the natural gas sample to be detected, and the energy metering result is sent to the user platform 250 through the service platform 240. In some embodiments, the calorific value of the natural gas sample to be detected is generated by the off-grid cloud platform based on the composition data of the natural gas sample to be detected.
For more details regarding management platform 230, reference may be made to fig. 3-7 and the description thereof.
It should be noted that the above description of the system and its components is merely for convenience of description and should not be construed as limiting the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of components or sub-systems may be combined with other components without departing from such teachings. For example, the sensor network platform and the management platform may be integrated into one component. For another example, the components may share one storage device, and each component may have a storage device. Such variations are within the scope of the present disclosure.
Fig. 3 is an exemplary flow diagram of a natural gas energy metering composition acquisition method, flow 300 being performed by a management platform, according to some embodiments of the present description.
Step 310, acquiring the temperature and the pressure of the natural gas sample to be detected at the first station based on the object platform.
The first station may be a station for pressure-regulating natural gas transportation and distribution, and it is understood that the first station may serve as a subordinate station to receive natural gas transported by a superordinate station and to pressure-regulating natural gas transportation and distribution to other stations or areas.
The natural gas sample to be detected can be natural gas which needs to be subjected to component detection.
Further, the natural gas sample to be detected at the first site may be natural gas in the first site, which is required to be subjected to detection of natural gas components, and may be all or part of natural gas in the first site.
In some embodiments, the temperature and the pressure of the natural gas sample to be detected can be obtained through corresponding sensors (for example, a temperature sensor, a pressure sensor, and the like, and the specific model of the sensor is not limited in some embodiments of the present specification) provided in the first station, and are recorded in the storage device of the first station in real time.
In some embodiments, the temperature value and the pressure value of the natural gas to be detected stored in the first site may be obtained by the object platform. For example, the target platform may be used as a corresponding sensor to detect a temperature value and a pressure value of the natural gas to be detected. For another example, the target platform respectively extracts the temperature value and the pressure value of the natural gas to be detected stored in the first site at the time point according to the specified time interval.
For more description of the temperature and pressure of the natural gas sample to be tested at the first site obtained based on the subject platform, see fig. 5.
Step 320, obtaining sample data of the natural gas sample of the second station based on the object platform, wherein the sample data comprises natural gas components and corresponding temperature and pressure.
Sample data refers to data that can be a reference sample. The sample data may include parameters associated with the sample. In some embodiments, the sample data for a natural gas sample may include a natural gas component of the natural gas sample and corresponding temperature and pressure values for the component.
In some embodiments, the natural gas sample data may be obtained by making real-time measurements of the natural gas sample. For example, the temperature and pressure values of the natural gas are acquired by the installed temperature and pressure sensors, and the natural gas sample is analyzed by the gas chromatograph analyzer to acquire composition data of the natural gas sample.
The second site may be a superordinate site that pressure-regulates the transmission and distribution of natural gas to subordinate sites. It is understood that the second site (superordinate site) may pressure-regulate the distribution of natural gas to the first site (subordinate site) so that the first site may obtain and store natural gas.
The sample data of the natural gas sample at the second site may be the component data and corresponding temperature and pressure values of the known natural gas sample at the second site. And acquiring sample data of the natural gas sample of the second site through the object platform corresponding to the second site. In some embodiments, the sample data of the natural gas sample at the second site may be the historically acquired natural gas data of the second site, such as the temperature, pressure and corresponding composition data of the part of the natural gas at the second site acquired in the historical production, and the data is used as the sample data of the natural gas sample at the second site.
Step 330, sample data of the natural gas sample of the first site is obtained based on the object platform, wherein the sample data comprises natural gas components and corresponding temperature and pressure.
The sample data of the natural gas sample at the first site may be the composition and corresponding temperature and pressure values of the known natural gas sample at the first site. In some embodiments, the sample data of the natural gas sample of the first site may be the historically acquired natural gas data of the first site, such as the temperature, pressure and corresponding composition data of the part of the natural gas of the first site acquired in the historical production, and the data is taken as the sample data of the natural gas sample of the first site. The sample data of the natural gas sample of the first site may be obtained through the object platform corresponding to the first site, and for specific obtaining description, refer to the description of obtaining the sample data of the natural gas sample of the second site in step 320.
In some embodiments, the sample data of the second site and the sample data of the first site are determined in different manners, for example, the sample data of the first site may be obtained based on a gas chromatography experimental analysis method, and the sample data of the second site may be obtained by a gas chromatography analysis.
In some embodiments, sample data (i.e., the natural gas composition and its temperature and pressure) from which the natural gas sample was obtained is obtained by performing a gas chromatography experiment on a natural gas sample at a subordinate site, such as the first site. It can be understood that, compared with a gas chromatograph (component analysis method adopted subsequently) method, the method for performing the gas chromatography experiment on the natural gas sample has the advantages of more convenient operation and lower cost (including equipment) required by the experiment. Generally, in consideration of economic cost, the gas chromatograph is provided only in a natural gas upper stage site (e.g., a second site), and a component analysis can be performed by a gas chromatography experiment in a lower stage site (e.g., a first site).
In some embodiments, the natural gas sample in the second station may be detected by a gas chromatograph disposed in an upper station such as the second station, so as to obtain sample data of the natural gas sample (i.e., the natural gas composition and its temperature and pressure).
The classification and setting of the upper and lower sites may be set in advance based on predetermined evaluation rules, such as comprehensive evaluation and determination based on the gas supply area covered by the sites, the building size of the sites, the amount of gas supplied, and the like.
In some embodiments, sample data of the natural gas sample stored in the first site may be obtained by the object platform. For example, the target platform may act as a corresponding sensor for detecting the natural gas composition and the temperature and pressure of the natural gas sample in the subordinate site. For another example, the target platform may extract at a specified time the natural gas components and their temperatures and pressures obtained by the superior site via the gas chromatograph.
Step 340, determining the natural gas component of the natural gas sample to be detected of the first site based on the sample data of the first site and the second site summarized by the sensing network platform and the temperature and the pressure of the natural gas sample to be detected of the first site.
The natural gas component can be a variety of combustible and non-combustible gas contents contained in natural gas, for example, combustible low molecular saturated hydrocarbon gases: methane, ethane, propane, butane, etc.; non-flammable non-hydrocarbon gases: carbon dioxide, carbon monoxide, nitrogen, hydrogen, and the like. Even when the same volume of natural gas with different components is combusted, the generated heat quantity can be different, so that accurate energy metering can be obtained by determining the components of the natural gas, and further, the specified metering of the natural gas can be transmitted to other natural gas sites or areas.
In some embodiments, the natural gas component of the natural gas sample to be detected at the first site may be the hydrocarbon gas and non-hydrocarbon gas content of the natural gas sample to be subjected to component detection at the subordinate site. The natural gas composition can be used for accurately transmitting the specified metered natural gas to other natural gas sites or areas by the subordinate sites.
In some embodiments, the management platform may determine the natural gas component of the natural gas sample to be detected at the first site based on the sample data of the first site and the second site and the temperature and the pressure of the natural gas sample to be detected at the first site in various ways. For example, the management platform may determine the natural gas component of the natural gas sample to be detected at the first site based on a table lookup interpolation method.
In some embodiments, the method of table lookup interpolation includes tabulating the sample data of the acquired natural gas sample, which may be tabulated sample data (natural gas composition and temperature and pressure thereof) of the first and second stations.
It is understood that a table is built based on a pair of temperature and pressure values corresponding to a set of natural gas components, wherein a pair of temperature and pressure values in the built table corresponds to a set of natural gas components. For example, 20 ℃, 2000pa corresponds to natural gas component a: 85% of methane, 10% of ethane, 3.5% of other multi-alkane, and 1.5% of other combustible gases such as hydrogen sulfide and hydrogen; 22 ℃ and 2000pa correspond to a group of natural gas components B: 85.1% of methane, 10.3% of ethane, 3.1% of other multi-alkanes and 1.5% of other combustible gases such as hydrogen sulfide and hydrogen.
And performing table lookup interpolation based on the temperature and the pressure of the natural gas sample to be detected to obtain the natural gas component corresponding to the temperature and the pressure in the table, namely the natural gas component of the natural gas sample to be detected. For example, if the temperature and the pressure of the natural gas sample to be detected are 20 ℃ and 2000pa, respectively, the natural gas component a can be used as the predicted component of the natural gas sample to be detected. For further description of determining the natural gas composition of the natural gas sample to be detected, reference is made to the contents of fig. 4-7.
In some embodiments, a table is built through sample data of a known natural gas sample, and a non-detection method (such as a corresponding relation in table lookup interpolation) is used, so that the natural gas component corresponding to the natural gas sample to be detected at a certain temperature and pressure can be quickly and accurately determined, complex operation of testing or detection is avoided, data testing based on a limited number of times in the early stage or testing data obtained by using history can be realized, the component of the natural gas can be obtained after the pressure and temperature data are known, and the method is favorable for saving testing cost, testing labor and testing time.
In some embodiments, after determining the natural gas component of the natural gas sample to be detected, the following operation steps may be further performed:
and 350, determining the heating value of the natural gas sample to be detected based on the natural gas components of the natural gas sample to be detected.
The calorific value of a natural gas sample may be the heat generated when a unit volume or mass of natural gas is combusted. In some embodiments, the calorific value of the natural gas may be determined by the natural gas composition. For example, the calorific value of natural gas can be calculated from the natural gas components. As can be appreciated, accurate natural gas heating value can be ensured by obtaining accurate natural gas components.
In some embodiments, the calorific value of the natural gas sample to be detected may be generated by the off-grid cloud platform based on the component data of the natural gas sample to be detected. The off-network cloud platform is a cloud platform independent of the internet of things system, and can be a third-party cloud platform.
In some embodiments, the component data of the natural gas sample to be detected can be sent to an off-grid cloud platform outside the system, and the calorific value of the natural gas sample to be detected, which is obtained by performing related calculation and returned by the off-grid cloud platform, can be obtained.
In some embodiments, the calorific value of the natural gas sample to be detected may be generated by a terminal (e.g., an external processor) having a data calculation function based on the component data of the natural gas sample to be detected. It can be understood that the component data of the natural gas sample to be detected can be sent to the external processor, and the calorific value calculated and returned by the external processor is obtained.
And step 360, carrying out energy metering on the downstream gas meter based on the heat productivity of the natural gas sample to be detected, and sending an energy metering result to the user platform through the service platform.
The downstream gas meter may be a device for recording and displaying the energy measurement of the natural gas output by the natural gas station (for example, a natural gas subordinate station), and it is understood that the energy measurement of the natural gas output by the natural gas subordinate station to the user residential area is recorded in the downstream gas meter in real time. Further, the user can know the consumed natural gas energy measurement in the month through the downstream gas meter.
Energy metering may refer to the metering of the heating value of natural gas, and as will be appreciated, energy metering of natural gas is determined by the amount of heat generated by combustion within a specified volume of natural gas. Further, the heat generation of natural gas may depend on the content of combustible gases in the natural gas, e.g. methane, etc.
In some embodiments, energy metering can be performed on a downstream gas meter based on the calorific value of the natural gas sample to be detected, and the energy metering result is sent to the user platform through the service platform. It can be understood that the actual use of natural gas by the user is calculated in an energy metering mode, and the pricing is further carried out. For example, the heating value is multiplied by a loss factor (the loss factor can be set according to actual conditions or empirical values), and the obtained calculation result is used as the actual natural gas consumption of the user.
In some embodiments, the energy metering results may be sent to the user platform by the service platform so that the user may observe the gas energy metering in the user platform for a preset time. For example, when the service platform calculates the natural gas energy metering result each time, the natural gas energy metering result is sent to the user platform, and the user platform can display the natural gas energy metering result obtained by the customer in real time, so that the user can plan subsequent gas consumption, or report an abnormal condition through the user platform in time when the abnormal gas consumption statistics occurs.
FIG. 4 is an exemplary diagram illustrating the determination of the composition of a natural gas sample to be tested by a predictive model according to some embodiments of the disclosure.
In some embodiments, the natural gas component of the natural gas sample to be detected may be determined by the predictive model based on the sample data of the first site, the sample data of the second site, and the temperature and pressure of the natural gas sample to be detected of the first site.
Further, the predictive model may be a machine learning model, e.g., a neural network, a deep neural network, or the like.
The input of the prediction model may be sample data (e.g., known natural gas sample composition and its corresponding temperature and pressure) of the first site, the second site, and the temperature and pressure of the natural gas sample to be detected at the first site; the output may be the natural gas composition of the natural gas sample to be tested. It can be understood that the natural gas components of the natural gas sample to be detected output from the prediction model can be obtained by inputting the natural gas sample components of the first site and the second site and the corresponding temperature and pressure thereof, and the temperature and pressure of the natural gas sample to be detected of the first site into the prediction model.
In some embodiments, the predictive model may be trained based on sets of labeled training samples. Specifically, a training sample with a label is input into the prediction model, and parameters of the prediction model are updated through training.
In some embodiments, the set of training samples may include: the method comprises the steps of obtaining sample data of known natural gas samples from a first site and a second site, then randomly blocking component data in part of the sample data, and using samples of the blocked component data as samples to be detected during training. In some embodiments, the training samples may be obtained from the first site and the second site, for example, sample data of multiple sets of natural gas samples obtained from the first site and the second site through the object platform, including corresponding component data of the natural gas samples at multiple sets of temperatures and pressures.
In some embodiments, the label at the time of model training may be the occluded component value described above.
In some embodiments, the tag may be obtained by measuring a natural gas component of the natural gas sample, for example, by a gas chromatography experiment or the like.
In some embodiments, the predictive model may be trained by various methods to update model parameters based on the above samples. For example, the training may be based on a gradient descent method.
In some embodiments, the results are trained when the predictive model under training satisfies a preset condition. Wherein, the prediction condition may be that the result of the loss function converges or is less than a preset threshold, etc.
According to the above description, the prediction model trained by a large number of training samples can quickly obtain the more accurate natural gas components of the natural gas sample to be detected output from the prediction model based on the input known sample data of the first site and the second site and the temperature and pressure of the natural gas sample to be detected of the first site. The accuracy and the efficiency of the natural gas components of the natural gas sample can be improved, and the accuracy of the natural gas energy metering and the pricing thereof is further improved.
Fig. 5 is an exemplary graph illustrating the acquisition of temperature and pressure of a natural gas sample to be tested at a first site according to some embodiments herein.
As shown in fig. 5, in some embodiments, the method of obtaining the temperature and pressure of the natural gas sample to be detected at the first site may comprise: and carrying out reduction treatment 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 in the reduction process.
The reduction treatment may be a process of changing the temperature and pressure of the natural gas sample to be detected to the temperature and pressure at the time of extraction. The temperature and pressure at the time of extraction refer to the temperature and pressure at the time of obtaining natural gas from an upper site. Because the storage conditions, the transportation conditions and the like of each station are different. The change of natural gas transportation and storage conditions can cause the change of temperature and pressure, and the change of temperature and pressure can bring certain errors to the component measurement. By reducing the temperature and the pressure of the natural gas sample to be detected to the temperature and the pressure during extraction, the errors of component measurement caused by the change of the temperature and the pressure can be reduced, and the accuracy of the obtained natural gas component of the natural gas sample to be detected is improved.
In some embodiments, the temperature and pressure at which the natural gas sample is extracted may be recorded and saved at a site where the natural gas sample is stored, for example, the temperature and pressure data at which the natural gas sample to be detected is extracted may be obtained based on data recorded at the first site or the second site.
In some embodiments, the reduction process may include operations such as pressurization, depressurization, temperature rise, temperature reduction, and the like, for example, the current temperature and pressure of the natural gas sample to be detected obtained from the first site are respectively 20 ℃ and 2300pa, and the temperature and pressure of the natural gas sample to be detected when being extracted (i.e., the target of reduction) are respectively 23 ℃ and 2000pa based on the data stored in the first site. Therefore, the natural gas sample to be detected needs to be subjected to temperature rise and pressure reduction treatment.
The plurality of temperatures and pressures in the reduction process may be determined according to preset data acquisition conditions, for example, if the preset data acquisition conditions are that sample data is acquired once every time the temperature changes by 1 degree celsius or every time the pressure changes by 100Pa in the reduction process, the data samples at the plurality of temperatures and pressures may be acquired in the reduction process based on the preset data acquisition conditions. For example only, when the preset data collection condition is that data is collected every time the temperature changes by 0.5 degrees celsius, the plurality of temperatures in the reduction process in the above example may be 20.5 ℃, 21 ℃, 21.5 ℃, 22 ℃ and 22.5 ℃, and the corresponding pressure may be the corresponding pressure of the natural gas sample to be detected when the temperature of the natural gas sample to be detected changes to the above temperature point.
In some embodiments, a plurality of temperatures and pressures of the natural gas sample to be detected may be obtained during the reduction process, and the natural gas sample to be detected at the plurality of temperatures and pressures may be used as the plurality of natural gas samples to be detected. Taking the above example as an example: the temperature and pressure when extracting the natural gas sample 20 ℃, 2300pa, reduce to 23 ℃, 2000pa, its in-process, can obtain the multiunit temperature and pressure in the reduction process: 20.5 ℃, 2250 pa; 21 ℃ 2200 pa; 21.5 ℃ and 2150 pa; 22 ℃, 2100pa and 22.5 ℃, 2050 pa. The natural gas samples at the plurality of temperatures and pressures can be used as a plurality of natural gas samples to be detected in the acquired reduction process.
In some embodiments, it can be understood that the reduction process can make the temperature and pressure of the sample closer to the temperature and pressure at the time of extraction, reduce errors caused by temperature and pressure changes of the storage environment of the sample, and further improve the accuracy of the predicted composition data.
In some embodiments, multiple tests may be performed during the reduction process to predict the composition of the natural gas sample to be tested at the corresponding temperature and pressure.
The multiple tests can be respectively carried out on the natural gas samples under different temperatures and pressures in the reduction treatment process to obtain the components of the natural gas sample to be detected under the corresponding temperature and pressure. For example, multiple tests may be performed by gas chromatography experiments. By way of example only, the natural gas samples at the above-mentioned sets of temperatures and pressures (e.g., 20.5 ℃, 2250 pa; 21 ℃, 2200 pa; 21.5 ℃, 2150 pa; 22 ℃, 2100pa and 22.5 ℃, 2050pa) may be subjected to the compositional test, respectively, to obtain a plurality of sample data.
The predicted components can be natural gas components at different temperatures and pressures, and the obtained natural gas components can be used as corresponding components at corresponding temperatures and pressures. For example, a component test may be performed on a natural gas sample to be tested at a temperature and a pressure of 20.5 ℃ and 2250pa to obtain component data a, and in this way, component data B and the like of the natural gas sample to be tested at 21 ℃ and 2200pa are obtained, respectively.
In some embodiments, the natural gas components corresponding to the natural gas to be detected at multiple temperatures and pressures can be obtained through multiple tests, so that each group of natural gas samples to be detected (including corresponding temperatures, pressures and natural gas components therein) can be used as a known sample, and errors caused by insufficient number of samples can be reduced.
In some embodiments, the component data of the natural gas sample to be detected during extraction can be predicted based on the machine learning model processing the temperature and the pressure of the natural gas sample to be detected during extraction, a plurality of temperatures and pressures of the natural gas sample to be detected and the corresponding component data.
The component data during extraction can be corresponding component data of the natural gas sample to be detected under temperature and pressure during extraction, more accurate energy metering data can be obtained based on the component data during extraction, and more accurate pricing data can be obtained.
In some embodiments, the machine learning model may be a neural network or a deep neural network.
In some embodiments, the input of the machine learning model may be a temperature and a pressure when the natural gas sample to be detected is extracted, and a plurality of temperatures, pressures and corresponding component data of the natural gas sample to be detected in the restoring process, where the plurality of temperatures, pressures and corresponding component data of the natural gas sample to be detected in the restoring process may be represented by sample data 1, sample data 2, and …, respectively, and sample data 1 may include the following data, taking the foregoing example as an example: 20.5 ℃, 2250pa, component data A; sample data 2 may include the following data: 21 ℃, 2200pa, component data B.
The output of the machine learning model may be the component data at the time of extraction of the sample to be detected. It can be understood that the temperature and the pressure of the natural gas sample to be detected during extraction, the plurality of temperatures, the plurality of pressures of the natural gas sample to be detected during reduction and the corresponding component data thereof are input into the machine learning model, and the natural gas component of the natural gas sample to be detected during extraction can be output from the machine learning model.
In some embodiments, the machine learning model may be trained based on sets of labeled training samples. Specifically, the training samples with labels are input into the machine learning model, and parameters of the machine learning model are updated through training.
In some embodiments, the set of training samples may include a plurality of temperatures and pressures and their corresponding components of the historical natural gas sample to be detected during the historical recovery process.
In some embodiments, the tag may be a natural gas component of the history of the natural gas sample to be tested as it was extracted.
In some embodiments, the tag may be obtained by detecting a component of the natural gas sample to be detected during extraction, for example, by a gas chromatography experiment.
In some embodiments, the machine learning model may be trained by various methods to update model parameters based on the above samples. For example, the training may be based on a gradient descent method.
In some embodiments, the training is ended when the machine learning model under training satisfies a preset condition. The preset condition may be that the loss function result converges or is smaller than a preset threshold, etc.
In some embodiments, predicting the component data of the temperature and pressure at which the natural gas sample to be tested is extracted via a machine learning model is more accurate and efficient than analyzing the component data at the time of extraction via an experimental approach (e.g., a gas chromatography experiment).
Fig. 6 is a diagram illustrating an example of detecting component data obtained by extracting a natural gas sample to be tested according to some embodiments of the present disclosure.
In some embodiments, the sample data of the natural gas sample at the first site, the sample data of the natural gas sample at the second site, and the temperature and pressure of the natural gas sample to be detected may be processed by the prediction model to predict the component data of the natural gas sample to be detected at the time of extraction. As shown in fig. 6, the prediction model may include a plurality of transform layers and prediction layers.
In some embodiments, the predictive model may be a deep neural network. In some embodiments, the prediction model may include a plurality of transform layers and a prediction layer.
In some embodiments, the prediction model may also be a combination of multiple models, and for example only, the prediction model may be a combination of multiple transform layer models and a prediction layer model.
In some embodiments, the input of the prediction model may be the temperature and pressure of the natural gas sample to be detected, and the sample data (including the natural gas composition and its corresponding temperature and pressure) of the first and second sites, and the output may be the composition data of the natural gas sample to be detected at the time of extraction.
In some embodiments, the input to the plurality of transform layers may be sample data of known natural gas samples (e.g., natural gas compositions of the first site, the second site, and their corresponding temperatures and pressures), and the output may be temperature, pressure, and their composition data (e.g., restored temperature, pressure, and composition data) of the known natural gas samples at the time of extraction predicted based on the sample data of the input known natural gas samples. The inputs to the prediction layer may be the temperature and pressure of the natural gas sample to be tested, and the output of the conversion layer (i.e., the temperature, pressure and their composition data of the known natural gas sample at the time of extraction). The output of the prediction layer may be the composition data of the natural gas sample to be detected at the time of extraction.
In some embodiments, the training of the prediction model may be pre-training the transform layer, and then jointly training the transform layer and the prediction layer.
The pre-training of the transformation layer may be to pre-set parameters of the prediction layer, and then train the transformation layer according to a training sample with a label, where the training sample may be sample data of a plurality of known natural gas samples of the first site and the second site during extraction, and may shield component data of part of the sample data during extraction, and use a known sample of the shielded component data as a sample to be evaluated. The label of its training sample may be the value of the occluded component data. In some embodiments, the parameters of the transformation layer may be trained to update by various methods based on the above samples. For example, the gradient descent method is trained. In some embodiments, the training ends when the transform layer in the training satisfies a preset condition. The preset condition may be that the loss function result converges or is smaller than a preset threshold, etc.
In some embodiments, the pre-trained at least one transform layer may be joint-trained end-to-end with the prediction layer. Specifically, in one round of iterative training, the training samples include: a plurality of known sample data and sample data to be evaluated, wherein the sample data (including the natural gas composition and the temperature and pressure thereof) of the known natural gas samples of the first station and the second station can be used as the known sample data, by masking the component data of the natural gas sample for a portion of the sample data at a known extraction time, namely, the natural gas sample of the sample data when the occluded component data is known to be extracted is taken as the sample data to be evaluated, the known sample data is respectively and correspondingly input into a plurality of transformation layers, the sample data to be evaluated is input into a prediction layer, the loss function a of at least one transformation layer is determined (the transformation layers can be corresponding to a plurality of loss functions such as a1, a2, a3 and the like), and a loss function b of the prediction layer, wherein the loss function a and the loss function b are processed to construct a combined loss function.
In some embodiments, when the loss function a and the loss function b are processed, weights may also be given to the two loss functions, for example, an average weight of the two loss functions. The weights may be predetermined to reflect the different degrees of importance of the two to the predictive model. In some embodiments, the joint Loss function LosscCan be expressed as: lossc＝LossA+LossB。
Furthermore, parameters of the prediction model are updated based on the joint loss function, and because the transformation layer is pre-trained, the parameters of the model can be updated in a mode of mainly updating the parameters of the prediction layer during joint training.
In some embodiments, when the prediction model is a plurality of transformation layers and prediction layers, each model layer of the prediction model is more targeted, and the prediction accuracy of each layer and the accuracy of the overall output of the prediction model are improved. Further, the model training is more concise by adopting an end-to-end joint training mode.
Fig. 7 is an exemplary diagram illustrating the determination of the composition of a natural gas sample to be tested according to some embodiments of the present description.
In some embodiments, determining, by the predictive model, the natural gas component of the natural gas sample to be detected based on the sample data of the first site, the sample data of the second site, and the temperature and pressure of the natural gas sample to be detected of the first site comprises: and determining the natural gas components of the natural gas sample to be detected based on the correction coefficient, the sample data of the first site, the sample data of the second site and the temperature and pressure of the natural gas sample to be detected of the first site through the prediction model. Wherein the correction coefficient is determined based on the temperature and/or pressure of the natural gas sample to be detected before reduction and the temperature and/or pressure during extraction.
The correction coefficient may be a reference coefficient that is used by the prediction model as a degree of correction of the natural gas component of the natural gas sample to be detected. In some embodiments, the correction factor may be related to the temperature or pressure difference between the natural gas sample to be tested before reduction and the extraction.
In some embodiments, the correction factor may be a difference between the temperature or pressure of the natural gas sample to be detected before reduction and the temperature or pressure at the time of extraction.
In some embodiments, taking the above example as an example, the temperature and pressure of the natural gas sample to be detected before reduction is 20 ℃ and 2300pa, and the temperature and pressure during extraction is 23 ℃ and 2000 pa. It is understood that the correction factor may be 23 deg.c-20 deg.c-3 or 2000pa-2300 pa-300, and it is understood that the correction factor may be 3 or-300.
In some embodiments, the temperature and the pressure during extraction are respectively subtracted from the temperature and the pressure before the natural gas sample to be detected is reduced, and the difference between the temperature and the pressure is weighted to obtain the correction coefficient.
In some embodiments, taking the above example as an example, the temperature and pressure of the natural gas sample to be detected before reduction is 20 ℃ and 2300pa, and the temperature and pressure during extraction is 23 ℃ and 2000 pa. It will be appreciated that the correction factor determined based on temperature changes is-3 and the correction factor determined based on pressure changes is 300; the two correction coefficients may be weighted and summed to obtain the final correction coefficient. For example, if it is determined based on historical experience that temperature changes have less influence on the composition of the natural gas than pressure changes, then the correction factor determined based on temperature may be assigned a relatively smaller weight, such as 0.2, and correspondingly, the correction factor determined based on pressure may be assigned a greater weight, such as 0.8, and the resulting correction factor is 239.4(-3 x 0.2+300 x 0.8 — 239.4).
In some embodiments, the input of the prediction model may be the correction coefficient, the sample data of the natural gas sample at the first site, the sample data of the natural gas sample at the second site, and the temperature and pressure of the natural gas sample to be detected at the first site; the output may be the natural gas component of the natural gas sample to be tested.
In some embodiments, the set of training samples may include: the method comprises the steps of obtaining sample data of known natural gas samples from a first site and a second site, then randomly blocking component data in part of the sample data, using the sample of the blocked component data as a sample to be detected during training, and using a correction coefficient of the natural gas sample to be detected, wherein a label can be a blocked component value.
It is understood that the prediction model is similar to the prediction model in fig. 4, and please refer to the related description in fig. 4 for a detailed description of the training method of the prediction model.
In some embodiments, the correction coefficient of the natural gas sample to be detected is added into the prediction model, so that a prediction basis can be further provided for the prediction model, and the prediction precision can be improved.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered as illustrative only and not limiting, of the present invention. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features are required than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single disclosed embodiment.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims (10)

1. A natural gas energy metering composition acquisition method, the method being performed by a management platform, the method comprising:
acquiring the temperature and the pressure of a natural gas sample to be detected of a first station based on an object platform;
acquiring sample data of a natural gas sample of a second station based on the object platform, wherein the sample data comprises natural gas components and corresponding temperature and pressure;
acquiring sample data of a natural gas sample of a first site based on the object platform; the sample data comprises natural gas components and corresponding temperature and pressure;
and determining the natural gas components of the natural gas sample to be detected of the first site based on the sample data of the first site and the second site and the temperature and pressure of the natural gas sample to be detected of the first site, which are summarized by the sensing network platform.
2. The method of claim 1, wherein the determining the natural gas composition of the natural gas sample to be detected at the first site based on the sample data at the first and second sites and the temperature and pressure of the natural gas sample to be detected at the first site comprises:
determining the natural gas components of the natural gas sample to be detected based on the sample data of the first site, the sample data of the second site and the temperature and pressure of the natural gas sample to be detected of the first site through a prediction model; the prediction model is a machine learning model.
3. The method of claim 2, wherein the obtaining the temperature and the pressure of the natural gas sample to be detected at the first site based on the object platform comprises: and carrying out reduction treatment on the natural gas sample to be detected, and acquiring a plurality of natural gas samples to be detected at a plurality of temperatures and pressures in the reduction process based on the object platform.
4. The method of claim 2, wherein the determining, by the predictive model, the natural gas composition of the natural gas sample to be detected based on the sample data of the first site, the sample data of the second site, and the temperature and pressure of the natural gas sample to be detected of the first site comprises:
determining the natural gas component of the natural gas sample to be detected based on a correction coefficient, the sample data of the first site, the sample data of the second site and the temperature and pressure of the natural gas sample to be detected of the first site through a prediction model;
the correction coefficient is determined based on the temperature and/or pressure of the natural gas sample to be detected before reduction and the temperature and/or pressure during extraction.
5. An internet of things system for gas energy metering composition acquisition, the system comprising an object platform, a sensor network platform, a management platform configured to:
acquiring the temperature and the pressure of a natural gas sample to be detected of a first station based on an object platform;
acquiring sample data of a natural gas sample of a second station based on the object platform, wherein the sample data comprises natural gas components and corresponding temperature and pressure;
acquiring sample data of a natural gas sample of a first site based on the object platform; the sample data comprises natural gas components and corresponding temperature and pressure;
and determining the natural gas components of the natural gas sample to be detected of the first site based on the sample data of the first site and the second site and the temperature and pressure of the natural gas sample to be detected of the first site, which are summarized by the sensing network platform.
6. The system of claim 5, the management platform configured to further perform the following:
determining the natural gas components of the natural gas sample to be detected based on the sample data of the first site, the sample data of the second site and the temperature and pressure of the natural gas sample to be detected of the first site through a prediction model; the prediction model is a machine learning model.
7. The system of claim 6, the object platform configured to further perform the following: and acquiring a plurality of natural gas samples to be detected under a plurality of temperatures and pressures in the process of reducing the natural gas samples to be detected.
8. The system of claim 6, the management platform configured to further perform the following:
determining the natural gas components of the natural gas sample to be detected based on a correction coefficient, the sample data of the first site, the sample data of the second site and the temperature and pressure of the natural gas sample to be detected of the first site through a prediction model;
the correction coefficient is determined based on the temperature and/or pressure of the natural gas sample to be detected before reduction and the temperature and/or pressure during extraction.
9. A computer-readable storage medium storing computer instructions which, when read by a computer, cause the computer to perform the method for obtaining energy content of natural gas as claimed in any one of claims 1 to 4.
10. A natural gas energy metering composition acquisition apparatus comprising a processor for performing the natural gas energy metering composition acquisition method as claimed in any one of claims 1 to 4.
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