CN114740159B - 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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Publication number
CN114740159B
CN114740159B CN202210387064.6A CN202210387064A CN114740159B CN 114740159 B CN114740159 B CN 114740159B CN 202210387064 A CN202210387064 A CN 202210387064A CN 114740159 B CN114740159 B CN 114740159B
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natural gas
site
sample
detected
temperature
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CN114740159A (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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Priority to US17/664,656 priority patent/US20220286761A1/en
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    • 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
    • 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, e.g. intermittent, or the display, e.g. digital
    • 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]
    • G16Y10/00—Economic sectors
    • G16Y10/35—Utilities, e.g. electricity, gas or water
    • 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
    • G16Y20/10—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
    • 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]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring
    • 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]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/20—Analytics; Diagnosis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/82—Energy audits or management systems therefor

Abstract

The embodiment of the specification provides a natural gas energy metering component acquisition method, which comprises the steps of acquiring the temperature and the pressure of a natural gas sample to be detected of a first site based on an object platform; acquiring sample data of a natural gas sample of a second site based on the object platform, wherein the sample data comprises natural gas components and corresponding temperatures and pressures; acquiring sample data of a natural gas sample of a first site based on an object platform; 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.

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
Natural gas is mainly transported through long pipeline networks, and because of the complexity of pipeline transportation paths and natural gas sources, the composition difference of different gas sources is large. When the pressure is regulated, the gas is delivered and distributed through different stations, the gas pressure and the temperature are changed, and the volume components of different gases in the natural gas are also changed, so that the natural gas component data of the lower-stage stations are inaccurate. And directly carrying out component detection on the natural gas of the lower-level site, the metering cost is further increased.
It is therefore desirable to provide a method for obtaining the components of natural gas energy by determining the components of natural gas in a natural gas sample to be detected by means of sample data and temperature and pressure analysis at a site, which can make the determination of the components of natural gas more accurate.
Disclosure of Invention
One or more embodiments of the present specification provide a natural gas energy metering component acquisition method. The natural gas energy metering component acquisition method comprises the following steps: acquiring the temperature and the pressure of a natural gas sample to be detected of a first site based on an object platform; acquiring sample data of a natural gas sample of a second site based on the object platform, wherein the sample data comprises natural gas components and corresponding temperatures and pressures; acquiring sample data of a natural gas sample of a first site based on an object platform; the method comprises the steps that sample data of a second station and sample data of a first station are different in determining mode; 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 specification provide a natural gas component obtaining system. The natural gas component 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 site based on an object platform; acquiring sample data of a natural gas sample of the second site based on the object platform, wherein the sample data comprises natural gas components and corresponding temperatures and pressures; acquiring sample data of a natural gas sample of a first site based on an object platform; the method comprises the steps that sample data of a second station and sample data of a first station are different in determining mode; 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 specification provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform a natural gas energy metering composition acquisition method.
One or more embodiments of the present specification provide a natural gas energy metering component obtaining apparatus including a processor for processing a natural gas energy metering component obtaining method.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is an application scenario diagram of an Internet of things system for natural gas energy metering component acquisition, shown 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 component capture, shown in accordance with some embodiments of the present description;
FIG. 3 is an exemplary flow chart of a natural gas energy metering component acquisition method according to some embodiments of the present description;
FIG. 4 is a schematic flow chart of determining 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 of components of a natural gas sample to be tested as extracted, according to some embodiments of the present disclosure;
FIG. 6 is a schematic illustration of a flow of components at the time of extraction of a sample of natural gas to be tested, according to some embodiments of the present disclosure;
fig. 7 is a schematic flow diagram of components of a sample of natural gas to be tested as extracted, 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 specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these 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 civil fuel field, industrial fuel field, process production field, chemical raw material production and processing field, compressed natural gas field and the like, and is not limited herein.
Fig. 1 is an application scenario diagram of an internet of things system for natural gas energy metering component acquisition, shown in accordance with some embodiments of the present description.
The application scenario 100 may include a server 110, a network 120, a database 130, a site 140, a terminal device 150, and a detection device 160. The 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, the gas pressure and temperature change as the natural gas is transported from the second site to the first site, and the temperature and pressure of the natural gas sample to be detected at the first site 140-2 are obtained by the subject platform; acquiring sample data of a natural gas sample of the second site 140-1 through the object platform; acquiring sample data of a natural gas sample of the first site 140-2 through the object platform; and determining the natural gas components of the natural gas sample to be detected of the first station 140-2 through the sample data of the first station 140-2 and the second station 140-1 summarized by the sensor network platform and the temperature and the pressure of the natural gas sample to be detected of the first station 140-2. The natural gas component determination can be more convenient and accurate.
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, or may be directly connected to the server 110 or be located inside the server 110.
Server 110 may be used to manage resources and process data and/or information from at least one component of the present system or external data sources (e.g., a cloud data center). In some embodiments, the natural gas composition of the natural gas sample to be tested for at the first site 140-2 may be determined after processing by the server 110. The server 110 may retrieve data from the database 130 or save data to the 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, server 110 may be regional or remote. In some embodiments, server 110 may be implemented on a cloud platform or provided in a virtual manner.
In some embodiments, server 110 may include a processing device 112. Operations in this specification may be performed by processing device 112 executing program instructions. The processing device 112 may process data and/or information obtained from other devices or system components. The processor may execute program instructions to perform one or more of the functions described in this disclosure based on such data, information, and/or processing results. In some embodiments, the processing device 112 may comprise one or more sub-processing devices (e.g., a single-core processing device or a multi-core, multi-core processing device). By way of example only, the processing device 112 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or the like, or any combination thereof.
The network 120 may connect components of the natural gas composition acquisition application scenario 100 and/or connect systems with external resource components. In some embodiments, sample data of the first site 140-2 and the second site 140-1, and data of temperature and pressure of the natural gas sample to be detected of the first site 140-2, etc., may be communicated over the network 120. Network 120 enables communication between components and other parts of the system to facilitate the exchange of data and/or information.
In some embodiments, network 120 may be any one or more of a wired network or a wireless network. For example, network 120 may include a cable network, a fiber optic network, or the like, or any combination thereof. The network connection between the parts can be in one of the above-mentioned ways or in a plurality of 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, database 130 may be used to provide sample data of natural gas samples at the outer second site 140-1, sample data of natural gas samples at the first site 140-2, etc. for the natural gas composition acquisition application scenario 100. 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 transmission pipeline engineering, and the station 140 mainly receives natural gas, pressurizes the natural gas of the pipeline, separately transmits the natural gas, distributes gas, stores gas, and regulates peak. Depending on where in the gas line, the station 140 may include a gas line head station, a gas line tail station, an intermediate station, and the like. In terms of acting in gas pipelines, the stations 140 may include natural gas valve stations, natural gas filling stations, natural gas storage stations, natural gas field stations, and the like.
In some embodiments, the site 140 may be provided with a detection device 160 of natural gas data, which may be used to detect performance parameters of the natural gas, such as pressure, temperature, flow, composition, etc. In some embodiments, server 110 and/or terminal device 150 may obtain sample data based on natural gas samples obtained by detection device 160 at second site 140-1 and/or first site 140-2 via network 120 or database 130.
The sites 140 may include a first site 140-2 and a second site 140-1. Wherein the second station 140-1 may be an upper natural gas station of the first station 140-2 for outputting natural gas to a lower station thereof. The first station 140-2 may be a lower 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 lower natural gas station at the same time, and one station may correspond to a plurality of upper natural gas stations and a plurality of lower 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 terminal device 150 may include a worker at first site 140-2, a worker at second site 140-1, a third party inspector, a customer, etc., and may also include other relevant personnel. In some embodiments, the user of terminal device 150 may be one or more users. In some embodiments, terminal device 150 may be one or any combination of mobile device 150-1, tablet computer 150-2, laptop computer 150-3, and the like, among other input and/or output enabled devices. In some embodiments, the processing device 112 may be included in the 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 in the site.
The sensing device 160 may include a temperature sensing device 160-1, a pressure sensing device 160-2, a composition testing device (not shown), a volume testing device (not shown), etc. The component testing device may include, for example, a chromatograph or the like. The temperature detecting 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. The pressure detecting device 160-2 refers to a device that measures the natural gas pressure in a site, and may be implemented based on a pressure sensor, for example.
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. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. 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 part or all of the object platforms. The user platform is a master of the operation system of the Internet of things and can be used for acquiring user requirements, the user requirements are a foundation and a premise formed by the operation system of the Internet of things, and the connection among the platforms of the Internet of things is used for meeting the requirements of the user. The service platform is located between the user platform and the management platform, is a bridge for the connection of 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 the connection and the 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 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 for executing the perception information generation and the 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 object platform acquires the perception information and transmits the perception 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 sensor network platform, so that the control of the corresponding object is realized.
Fig. 2 is an exemplary schematic diagram of a natural gas component liquids extraction system, according to some embodiments of the present disclosure. As shown in fig. 2, the natural gas component acquisition system may be implemented based on an internet of things system, and the internet of things system 200 for natural gas energy metering component acquisition includes an object platform 210, a sensor network platform 220, and a management platform 230. In some embodiments, the natural gas component obtaining 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 natural gas energy component capture may be applied to natural gas transportation management. When applied to natural gas transportation management, the subject 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 a temperature and a pressure of a natural gas sample to be detected at the 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 at the second site, or the like. The object platform 210 may upload the collected data related to the 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 used as information input 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 subject platform 210 may include the temperature and pressure of the natural gas sample to be detected at the first site, sample data of the natural gas sample at the second site, sample data of the natural gas sample at the first site. In some embodiments, the subject platform 210 is configured to obtain a plurality of natural gas samples to be tested at a plurality of temperatures and pressures during the reduction process of the natural gas samples to be tested.
In some embodiments, the sensor network platform 220 may connect the management platform 230 and the object platform 210 to implement the functions of sensing information sensing communication and controlling information sensing communication. 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 of 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, the sample data of the natural gas sample at the first site based on the subject platform 210. In some embodiments, the sample data of the natural gas sample at the second site includes a natural gas composition and corresponding temperature and pressure. In some embodiments, the sample data of the second station and the sample data of the first station are determined differently.
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 the natural gas composition of the natural gas sample to be detected based on the correction factor, 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 by means of a predictive model. In some embodiments, the correction factor is determined based on the temperature and/or pressure of the natural gas sample to be tested prior to 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 a heating value of the natural gas sample to be detected based on the natural gas composition of the natural gas sample to be detected; the downstream gas meter is subjected to energy metering based on the heating 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 heating value of the natural gas sample to be detected is generated by the off-network cloud platform based on the composition data of the natural gas sample to be detected.
For more details regarding management platform 230, see FIGS. 3-7 and their description.
It should be noted that the above description of the system and its components is for descriptive convenience only and is not intended to limit the present disclosure to the scope of the illustrated embodiments. It will be understood by those skilled in the art that, given the principles of the system, it is possible to combine the individual components arbitrarily or to connect the constituent subsystems with other components without departing from such principles. For example, the sensor network platform and the management platform may be integrated in one component. For another example, each component may share a single storage device, or each component may have a respective storage device. Such variations are within the scope of the present description.
FIG. 3 is an exemplary flow chart of a natural gas energy metering component acquisition method, flow 300 being performed by a management platform, according to some embodiments of the present description.
At step 310, the temperature and pressure of the natural gas sample to be tested at the first site is obtained based on the subject platform.
The first station may be a station for pressure regulating delivery of natural gas, it being understood that the first station may receive natural gas delivered by an upper station as a lower station and pressure regulating delivery of natural gas to other stations or areas.
The natural gas sample to be tested may be natural gas for which component testing is desired.
Further, the natural gas sample to be detected at the first site may be natural gas in the first site for which detection of a natural gas component is desired, which may be all or part of the natural gas in the first site.
In some embodiments, the temperature and pressure of the natural gas sample to be detected may be obtained by corresponding sensors (e.g., temperature sensors, pressure sensors, etc., without limitation to the specific model of the sensor in some embodiments of the present disclosure) provided in the first site, and recorded in real-time in a storage device of the first site.
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 through the object platform. For example, the object platform may detect the temperature value and the pressure value of the natural gas to be detected as corresponding sensors. For another example, the object 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 designated time interval.
See fig. 5 for a more explanation of the temperature and pressure of the natural gas sample to be tested at the first site obtained based on the subject platform.
At step 320, sample data is obtained for the natural gas sample at the second site based on the subject platform, the sample data including the natural gas composition and corresponding temperature and pressure.
Sample data refers to data that can be used as a reference sample. The sample data may include parameters related to the sample. In some embodiments, the sample data of the 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 taking real-time measurements of the natural gas sample. For example, temperature and pressure values of natural gas are obtained by installed temperature and pressure sensors, and a natural gas sample is analyzed by a gas chromatograph to obtain composition data of the natural gas sample.
The second station may be an upper station for pressure regulating the delivery of natural gas to a lower station. It will be appreciated that the second station (the superior station) may pressure-regulate the delivery of natural gas to the first station (the inferior station) to allow the first station to obtain and store natural gas.
The sample data of the natural gas sample at the second site may be the composition data and corresponding temperature and pressure values of the known natural gas sample at the second site. Sample data of the natural gas sample of the second site may be acquired 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 historical acquired natural gas data at the second site, such as temperature, pressure, and corresponding composition data of a portion of the natural gas at the second site acquired during historical production, and the data is taken as sample data of the natural gas sample at the second site.
In step 330, sample data of the natural gas sample at the first site is obtained based on the subject platform, the sample data including the natural gas composition and corresponding temperature and pressure.
The sample data of the natural gas sample at the first site may be the known composition of the natural gas sample at the first site and the corresponding temperature and pressure values. In some embodiments, the sample data of the natural gas sample at the first site may be historical obtained natural gas data at the first site, such as temperature, pressure, and corresponding composition data of a portion of the natural gas at the first site obtained during historical production, and the data is taken as sample data of the natural gas sample at the first site. The sample data of the natural gas sample at the first site may be obtained by the object platform corresponding to the first site, and the specific obtaining description refers to the description of obtaining the sample data of the natural gas sample at 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 chromatograph experimental analysis method, and the sample data of the second site may be obtained through gas chromatograph analysis.
In some embodiments, sample data (i.e., natural gas composition, as well as its temperature and pressure) is obtained from a natural gas sample by performing a gas chromatography experiment on the natural gas sample at a lower stage, such as a first stage. It can be appreciated that by performing a gas chromatography experiment on a natural gas sample, the operation is more convenient and the cost (including equipment) required for the experiment is lower than that of a gas chromatograph (a component analysis method adopted later). Typically, in view of economic costs, the gas chromatograph is provided only in a natural gas upper stage (e.g., second stage), and the component analysis can be performed by a gas chromatography experiment in a lower stage (e.g., first stage).
In some embodiments, the natural gas sample in the second station may be detected by a gas chromatograph provided in an upper level station such as the second station, thereby obtaining sample data (i.e., natural gas composition and its temperature and pressure) of the natural gas sample.
The classification and setting of the upper-level site and the lower-level site may be set in advance based on a predetermined evaluation rule, such as comprehensive evaluation and identification based on the gas supply area covered by the site, the site building scale, the gas supply amount, and the like.
In some embodiments, sample data for a natural gas sample stored in the first site may be obtained by the subject platform. For example, the subject platform may detect the natural gas composition and temperature and pressure of the natural gas sample in the lower level site as corresponding sensors. For another example, the subject platform may extract the natural gas components and their temperature and pressure obtained by the upper level site via gas chromatograph at a specified time.
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 sensor network platform and the temperature and the pressure of the natural gas sample to be detected of the first site.
The natural gas component may be a variety of combustible and non-combustible gas contents contained in natural gas, for example, a combustible low molecular saturated hydrocarbon gas: methane, ethane, propane, butane, etc.; non-flammable non-hydrocarbon gas: carbon dioxide, carbon monoxide, nitrogen, hydrogen, etc. Even when the same volume of natural gas of different components is combusted, the generated heat may be different, so that accurate energy metering can be obtained by determining the natural gas composition, and further, the natural gas with specified metering can be transmitted to other natural gas sites or areas.
In some embodiments, the natural gas composition of the natural gas sample to be tested at the first site may be the hydrocarbon gas and non-hydrocarbon gas content of the natural gas sample in the lower site for which composition testing is desired. The natural gas composition can be used to enable the lower-level stations to accurately transmit the specified metering natural gas to other natural gas stations or areas.
In some embodiments, the management platform may implement determining the natural gas composition of the first site to be tested natural gas sample based on the sample data of the first site and the second site and the temperature and pressure of the first site to be tested natural gas sample in a number of ways. For example, the management platform may determine the natural gas composition of the natural gas sample to be detected at the first site based on a look-up table interpolation method.
In some embodiments, the method of table look-up interpolation includes tabulating the acquired sample data of the natural gas sample, which may be the first and second sites (natural gas composition and its temperature and pressure).
It will be appreciated that the mapping is performed based on a pair of temperature and pressure values corresponding to a corresponding natural gas composition, wherein the pair of temperature and pressure values in the constructed table corresponds to a set of natural gas compositions. For example, 20 ℃, 2000pa corresponds to natural gas component a: 85% of methane, 10% of ethane and 3.5% of other multi-alkane, and 1.5% of other combustible gases such as hydrogen sulfide, hydrogen and the like; 22 ℃ and 2000pa for 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, hydrogen and the like.
And performing table lookup interpolation based on the temperature and the pressure of the natural gas sample to be detected, and obtaining the natural gas component corresponding to the temperature and the pressure value in the table, namely the natural gas component of the natural gas sample to be detected. For example, if the temperature and pressure of the natural gas sample to be detected are 20 ℃ and 2000pa, respectively, then the natural gas component a may be used as the predicted component of the natural gas sample to be detected. For more description of determining the natural gas composition of a natural gas sample to be tested, see fig. 4-7.
In some embodiments, the table is built by sample data of known natural gas samples, and the natural gas components corresponding to the natural gas samples to be detected at a certain temperature and under a certain pressure can be quickly and accurately determined by a non-detection method (such as a corresponding relation in table lookup interpolation), so that complex operation of test or detection is avoided, data test based on a limited number of times in the prior stage or test data obtained by utilizing histories can be realized, and the components of the natural gas can be obtained after the pressure and temperature data are obtained later, thereby being beneficial to saving test cost, test manpower and test time.
In some embodiments, after the natural gas composition of the natural gas sample to be detected is determined, the following operational steps may be further performed:
Step 350, determining the heating value of the natural gas sample to be detected based on the natural gas component of the natural gas sample to be detected.
The heating value of the natural gas sample may be the heat generated when a unit volume or mass of natural gas is burned. In some embodiments, the heating value of natural gas may be determined by the natural gas composition. For example, the heating value of natural gas can be calculated from the natural gas composition. It will be appreciated that accurate natural gas heating values can be ensured by obtaining accurate natural gas compositions.
In some embodiments, the heating value of the natural gas sample to be detected may be generated by the off-grid cloud platform based on the composition data of the natural gas sample to be detected. The off-network cloud platform is independent of a cloud platform outside 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 may be sent to an off-network cloud platform outside the system, and the heating value of the natural gas sample to be detected, which is calculated and returned by the off-network cloud platform, may be obtained.
In some embodiments, the heating 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 composition 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 heating value calculated and returned by the external processor is obtained.
And 360, measuring energy of the downstream gas meter based on the heating value of the natural gas sample to be detected, and sending an energy measurement result to a user platform through a service platform.
The downstream gas meter may be a device for recording and displaying the natural gas energy meter output by the natural gas site (e.g., a natural gas lower level site), it being understood that the natural gas energy meter output by the natural gas lower level site to the user's residence area will be recorded in real time in the downstream gas meter. Further, the user can know the natural gas energy metering consumed in the month through the downstream gas meter.
The energy metering may refer to the manner in which the heating value of the natural gas is metered, it being understood that the 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, the downstream gas meter may be energy metered based on the heating 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 appreciated that the actual use of natural gas by the user is calculated by means of energy metering, and further priced. For example, the calculated result obtained by multiplying the heating value by a loss factor (the loss factor may be set according to the actual situation or an empirical value) 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 natural gas energy metering used for a preset time in the user platform. For example, the service platform sends the natural gas energy metering result to the user platform when the natural gas energy metering result is obtained through each calculation, and the user platform can display the natural gas energy metering result obtained for a customer in real time so that the user can plan the later gas consumption or report the abnormality through the user platform in time when the abnormal gas consumption is counted.
FIG. 4 is an exemplary diagram illustrating determination of the composition of a natural gas sample to be tested by a predictive model, according to some embodiments of the present disclosure.
In some embodiments, the natural gas composition of the natural gas sample to be detected may be determined by a 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, etc.
The inputs to the predictive model may be sample data for the first site, the second site (e.g., known natural gas sample components and their corresponding temperatures and pressures), and the temperature and pressure of the natural gas sample to be detected for the first site; the output may be the natural gas component of the natural gas sample to be tested. It will be appreciated that the natural gas sample components of the first site, the second site, and the corresponding temperatures and pressures thereof, as well as the temperatures and pressures of the natural gas sample to be detected of the first site, are input into the predictive model, and the natural gas components of the natural gas sample to be detected output from the predictive model may be obtained.
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 a prediction model, and parameters of the prediction model are updated through training.
In some embodiments, a set of training samples may include: sample data of known natural gas samples acquired from the first site and the second site are then randomly occluded for component data in part of the sample data, and the sample of the occluded component data is taken as a sample to be tested during training. In some embodiments, the training samples may be obtained from a first site and a 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 labels when the model is trained may be the occluded component values described above.
In some embodiments, the tag may be obtained by measuring the natural gas component of the natural gas sample, e.g., by a meteorological chromatography experiment, etc.
In some embodiments, the predictive model may be trained to update model parameters by various methods based on the samples described above. For example, training may be based on a gradient descent method.
In some embodiments, the training results when the predictive model under training meets a preset condition. The prediction condition may be that the loss function result converges or is smaller than a preset threshold value, etc.
According to the above description, by using a plurality of prediction models trained by training samples, the natural gas composition of the natural gas sample to be detected, which is output from the prediction model, can be obtained quickly based on the input sample data known by the first station and the second station, and the temperature and pressure of the natural gas sample to be detected at the first station. The accuracy and efficiency of the natural gas components of the obtained natural gas sample can be improved, and further the accuracy of natural gas energy metering and pricing thereof is improved.
Fig. 5 is an exemplary diagram of obtaining temperature and pressure of a sample of natural gas to be tested at a first site, according to some embodiments of the present disclosure.
As shown in fig. 5, in some embodiments, a method of obtaining a temperature and pressure of a natural gas sample to be detected at a first site may include: 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 varying the temperature, pressure of the natural gas sample to be tested 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-level site. As the storage conditions, transportation conditions, etc. are different for each site. And the temperature and the pressure can be changed due to the change of the transportation and storage conditions of the natural gas, and certain errors can be caused to the component measurement due to the change of the temperature and the pressure. By reducing the temperature and pressure of the natural gas sample to be detected to the temperature and pressure during extraction, the error of component measurement caused by the change of the temperature and the pressure can be reduced, and the accuracy of the natural gas component of the obtained natural gas sample to be detected is further improved.
In some embodiments, the temperature and pressure at which the natural gas sample is extracted may be recorded at the site where it is stored, for example, the temperature and pressure data at which the natural gas sample is extracted to be detected may be obtained based on data recorded in the first site or the second site.
In some embodiments, the process of the reduction treatment may include operations of pressurizing, depressurizing, heating, cooling, and the like, for example, the current temperature and pressure of the natural gas sample to be detected obtained from the first station are respectively 20 ℃, 2300pa, and the temperature and pressure when the natural gas sample to be detected is extracted (i.e. the target of reduction) based on the data stored in the first station are respectively 23 ℃, 2000pa. Thus, the natural gas sample to be detected needs to be subjected to heating and depressurization.
The plurality of temperatures and pressures in the reduction process may be determined according to preset data collection conditions, for example, if the preset data collection conditions are that sample data is collected once every 1 degree celsius change or every 100Pa change of the temperature or the pressure in the reduction process, then the data samples at the plurality of temperatures and pressures may be obtained in the reduction process based on the preset data collection conditions. For example only, when the preset data collection condition is that the temperature changes by 0.5 ℃ once, the temperatures in the reduction process of the above example may be 20.5 ℃, 21 ℃, 21.5 ℃, 22 ℃ and 22.5 ℃, and the corresponding pressures may be the pressures of the corresponding natural gas samples to be detected when the temperature of the natural gas samples to be detected changes to the above temperature points.
In some embodiments, multiple 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 multiple temperatures and pressures may be used as the multiple natural gas samples to be detected. Taking the above example as an example: in the process of extracting the natural gas sample, the temperature and the pressure are 20 ℃, 2300pa, and the temperature and the pressure are reduced to 23 ℃ and 2000pa, so that a plurality of groups of temperatures and pressures in the reduction process can be obtained: 20.5℃2250pa;21 ℃, 2200pa;21.5℃2150pa;22℃at 2100pa and 22.5℃at 2050pa. 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 appreciated that the reduction process can bring the temperature and pressure of the sample closer to the temperature and pressure at the time of extraction, reducing errors due to temperature and pressure variations in the sample storage environment, further improving the accuracy of the predicted composition data.
In some embodiments, multiple tests may be performed during the reduction process for predicting the composition of the natural gas sample to be tested at the corresponding temperature and pressure.
The multiple tests can be to test the natural gas samples at different temperatures and pressures respectively in the reduction treatment process so as to obtain components of the natural gas samples to be detected at corresponding temperatures and pressures. For example, multiple tests may be performed by gas chromatography experiments. By way of example only, the above-described sets of natural gas samples at temperatures and pressures (e.g., 20.5℃, 2250Pa;21℃., 2200Pa;21.5℃, 2150Pa;22℃., 2100Pa, and 22.5℃, 2050 Pa), respectively, may be individually subjected to compositional testing to obtain a plurality of sample data.
The predicted components can be the components of the natural gas at different temperatures and pressures, and the obtained natural gas components can be used as the corresponding components at the corresponding temperatures and pressures. For example, it is possible to perform a component test on a natural gas sample to be measured 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 measured at 21 ℃ and 2200pa, respectively.
In some embodiments, the natural gas components corresponding to the natural gas to be detected at a plurality of temperatures and pressures can be obtained through multiple tests, so that each group of natural gas samples to be detected (including the corresponding temperatures, pressures and natural gas components therein) is taken as a known sample, errors caused by insufficient sample numbers can be reduced, for example, the natural gas components corresponding to the natural gas to be detected at a plurality of temperatures and pressures obtained through multiple tests can be taken as the data sources for table look-up interpolation, so that the number of data in the built data table is increased, and the accuracy of the data table is improved.
In some embodiments, the composition data of the natural gas sample to be detected at the time of extraction may be predicted based on the machine learning model processing the temperature and pressure of the natural gas sample to be detected at the time of extraction, the plurality of temperatures and pressures of the natural gas sample to be detected, and the corresponding composition data thereof.
The component data during extraction can be component data corresponding to the temperature and the pressure of the natural gas sample to be detected during extraction, and more accurate energy metering data can be obtained based on the component data during extraction, so that 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 the temperature and the pressure of the natural gas sample to be detected when the natural gas sample to be detected is extracted, and the plurality of temperatures, pressures and corresponding component data of the natural gas sample to be detected in the reduction process, wherein the plurality of temperatures, pressures and corresponding component data of the natural gas sample to be detected in the reduction process may be represented by sample data 1, sample data 2, … sample data n, respectively, and the sample data 1 may include the following data, for example: 20.5 ℃, 2250pa, composition 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 sample extraction to be detected. It can be understood that the temperature and pressure of the natural gas sample to be detected during extraction, and the plurality of temperatures and pressures of the natural gas sample to be detected during reduction and 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 sample with the label is input into the machine learning model, and the parameters of the machine learning model are updated through training.
In some embodiments, the set of training samples may include a temperature and pressure at which the historical natural gas sample to be tested was extracted, as well as a plurality of temperatures and pressures of the historical natural gas sample to be tested and their corresponding components during the historical reduction process.
In some embodiments, the tag may be a natural gas component at the time of the historical extraction of the natural gas sample to be detected.
In some embodiments, the tag may be obtained by detecting a component of a natural gas sample to be detected during extraction, for example, by gas chromatography.
In some embodiments, the machine learning model may train update model parameters by various methods based on the samples described above. For example, 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 value, etc.
In some embodiments, component data of temperature and pressure at the time of extraction of a natural gas sample to be detected is predicted by a machine learning model with higher accuracy and efficiency than component data at the time of extraction analyzed by an experimental means (e.g., a gas chromatography experiment).
Fig. 6 is an exemplary diagram of component data for testing a natural gas sample to be tested as shown in some embodiments of the present disclosure.
In some embodiments, the composition data at the time of extraction of the natural gas sample to be detected may be predicted by a predictive model processing sample data of the natural gas sample at the first site, sample data of the natural gas sample at the second site, temperature and pressure of the natural gas sample to be detected. Wherein, as shown in fig. 6, the prediction model may include a plurality of transform layers and a prediction layer.
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 by way of example only, the prediction model may be a combination of multiple transform layer models and prediction layer models.
In some embodiments, the inputs to the predictive model may be the temperature and pressure of the natural gas sample to be tested, and the sample data of the first and second sites (including the natural gas composition and its corresponding temperature and pressure), and the output may be the composition data of the natural gas sample to be tested as it is extracted.
In some embodiments, the input of the plurality of transform layers may be sample data of the known natural gas sample (e.g., the natural gas composition of the first site, the second site, and their corresponding temperatures and pressures), and the output may be the temperature, pressure, and composition data of the known natural gas sample at the time of extraction (e.g., the reduced temperature, pressure, and composition data) predicted based on the sample data of the input known natural gas sample. The inputs to the predictive layer may be the temperature and pressure of the natural gas sample to be detected, as well as the output of the transform layer (i.e., the temperature, pressure, and composition data of the known natural gas sample at the time of extraction). The output of the predictive 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 predictive model may be to pre-train the transform layer and then to jointly train the transform layer and the predictive 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 at a first site and a second site during extraction, and may mask component data during extraction in part of the sample data, and use a known sample of the masked 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 transform layer may be updated through training by various methods based on the samples described above. For example, gradient descent methods are used for training. In some embodiments, the training is ended when the transformation 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 value, etc.
In some embodiments, the pre-trained at least one transform layer may be end-to-end co-trained with the prediction layer. Specifically, in one round of iterative training, the training samples include: the method comprises the steps of using a plurality of known sample data and sample data to be evaluated, wherein the sample data (including natural gas components and the temperature and the pressure of the natural gas components) of the known natural gas samples of a first site and a second site can be used as the known sample data, the natural gas samples of the sample data at the known extraction time of the shielded component data can be used as the sample data to be evaluated by shielding the component data of the natural gas samples of the sample data at the known extraction time of the part of the known extraction time, the known sample data are respectively input into a plurality of transformation layers in a corresponding manner, the sample data to be evaluated are input into a prediction layer, the loss function a of at least one transformation layer is determined (the loss functions such as a1, a2 and a3 can be correspondingly changed into a plurality of loss functions when the transformation layers are changed), and the loss function b of the prediction layer is constructed by processing the loss function a and the loss function b.
In some embodiments, the loss functions a and b may also be weighted, such as by averaging, when they are processed. The weights may be pre-determined to reflect the different levels of importance of the two to the predictive model. In some embodiments, the Loss function Loss is combined c Can be expressed as: loss (Low Density) c ＝Loss A +Loss B 。
Further, the parameters of the prediction model are updated based on the joint loss function, and because the transformation layer is already 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 made to be 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 simplified by adopting an end-to-end joint training mode.
Fig. 7 is an exemplary diagram illustrating a determination of a composition of a natural gas sample to be tested according to some embodiments of the present disclosure.
In some embodiments, 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: and determining the natural gas component of the natural gas sample to be detected based on the correction coefficient, the sample data of the first station, the sample data of the second station and the temperature and the pressure of the natural gas sample to be detected of the first station through a prediction model. Wherein the correction factor 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 at the time of extraction.
The correction factor may be a reference factor for the extent to which the natural gas composition of the natural gas sample to be detected is corrected by the predictive model. In some embodiments, the correction factor may be related to the temperature or pressure differential between the extraction and the reduction of the natural gas sample to be tested.
In some embodiments, the correction factor may be the difference between the temperature or pressure of the natural gas sample to be tested 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 before reduction of the natural gas sample to be tested is 20 ℃, 2300pa, and the temperature and pressure at extraction is 23 ℃, 2000pa. It is understood that the correction coefficient may be 23 ℃ -20 ℃ =3, or 2000pa-2300 pa= -300, and it is known that the correction coefficient may be 3 or-300.
In some embodiments, the temperature and pressure before reduction of the natural gas sample to be detected are subtracted by the temperature and pressure at the time of extraction, respectively, and the difference between the temperature and pressure is weighted to obtain a correction coefficient.
In some embodiments, taking the above example as an example, the temperature and pressure before reduction of the natural gas sample to be tested is 20 ℃, 2300pa, and the temperature and pressure at extraction is 23 ℃, 2000pa. It will be appreciated that the correction factor determined based on the temperature change is-3 and the correction factor determined based on the pressure change is 300; the two correction coefficients may be weighted summed to obtain the final correction coefficient. For example, if it is determined based on historical experience that the temperature change has a smaller effect on the natural gas composition than the pressure change, a relatively smaller weight, such as 0.2, may be assigned to the temperature-based correction factor, and correspondingly, a greater weight, such as 0.8, may be assigned to the pressure-based correction factor, with a final correction factor of 239.4 (-3 x 0.2+300 x 0.8= 239.4).
In some embodiments, the inputs to the predictive model may be a correction factor, sample data for a natural gas sample at a first site, sample data for a natural gas sample at a second site, and 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 detected.
In some embodiments, a set of training samples may include: the sample data of the known natural gas sample obtained from the first station and the second station are randomly shielded, the component data in part of the sample data are randomly shielded, the sample of the shielded component data is used as a sample to be tested in training, the correction coefficient of the natural gas sample to be tested is used, and the label can be the shielded component value.
It will be appreciated that the predictive model is similar to the predictive model of fig. 4, and for a detailed description of the training method of the predictive model, reference is made to the relevant description of fig. 4.
In some embodiments, by adding the correction coefficient of the natural gas sample to be detected into the prediction model, a prediction basis can be further provided for the prediction model, which is beneficial to improving the prediction precision.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative 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 included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims (8)

1. A natural gas energy metering component acquisition method, the method 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 site based on an object platform;
acquiring sample data of a natural gas sample of a second site based on the object platform, wherein the sample data comprises natural gas components and corresponding temperatures and pressures;
acquiring sample data of a natural gas sample of a first site based on the object platform; the sample data includes natural gas components and corresponding temperatures and pressures;
determining, based on the sample data of the first site and the second site summarized by the sensor network platform and the temperature and the pressure of the natural gas sample to be detected of the first site, a natural gas component of the natural gas sample to be detected of the first site by a prediction model, wherein the prediction model is a machine learning model, the prediction model comprises a plurality of transformation layers and a prediction layer, inputs of the plurality of transformation layers comprise natural gas components of the first site and the second site and corresponding temperatures and pressures thereof, outputs of the plurality of transformation layers are temperature, pressure and component data of the natural gas components of the first site and the second site after reduction, inputs of the prediction layer comprise the temperature, the pressure of the natural gas sample to be detected and outputs of the plurality of transformation layers, outputs of the prediction layer are component data of the natural gas sample to be detected when the natural gas sample is extracted, and training of the prediction model comprises:
Pre-training the transformation layer to obtain a pre-trained transformation layer;
and carrying out joint training on the pre-trained transformation layer and the prediction layer to obtain the prediction model.
2. The method of claim 1, the object-based platform acquiring the temperature and pressure of the natural gas sample to be detected at the first site comprising: 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 an object platform.
3. The method of claim 1, the determining, by a predictive model, natural gas composition of the natural gas sample to be detected for the first site based on the sample data for the first site and the second site summarized by a sensing network platform and a temperature and a pressure of the natural gas sample to be detected for the first site comprising:
determining, by a predictive model, a natural gas composition of the natural gas sample to be detected based on a correction factor, the sample data of the first site, the sample data of the second site, and a temperature and a pressure of the natural gas sample to be detected of the first site;
The correction coefficient is determined based on the temperature and/or pressure before reduction and the temperature and/or pressure at extraction of the natural gas sample to be detected.
4. An internet of things system for natural gas energy metering component 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 site based on an object platform;
acquiring sample data of a natural gas sample of a second site based on the object platform, wherein the sample data comprises natural gas components and corresponding temperatures and pressures;
acquiring sample data of a natural gas sample of a first site based on the object platform; the sample data includes natural gas components and corresponding temperatures and pressures;
determining, based on the sample data of the first site and the second site summarized by the sensor network platform and the temperature and the pressure of the natural gas sample to be detected of the first site, a natural gas component of the natural gas sample to be detected of the first site by a prediction model, wherein the prediction model is a machine learning model, the prediction model comprises a plurality of transformation layers and a prediction layer, inputs of the plurality of transformation layers comprise natural gas components of the first site and the second site and corresponding temperatures and pressures thereof, outputs of the plurality of transformation layers are temperature, pressure and component data of the natural gas components of the first site and the second site after reduction, inputs of the prediction layer comprise the temperature, the pressure of the natural gas sample to be detected and outputs of the plurality of transformation layers, outputs of the prediction layer are component data of the natural gas sample to be detected when the natural gas sample is extracted, and training of the prediction model comprises:
Pre-training the transformation layer to obtain a pre-trained transformation layer;
and carrying out joint training on the pre-trained transformation layer and the prediction layer to obtain the prediction model.
5. The system of claim 4, the object platform configured to further perform the following operations: and obtaining a plurality of natural gas samples to be detected under a plurality of temperatures and pressures in the reduction treatment process of the natural gas samples to be detected.
6. The system of claim 4, the management platform configured to further perform the following operations:
determining, by a predictive model, a natural gas composition of the natural gas sample to be detected based on a correction factor, the sample data of the first site, the sample data of the second site, and a temperature and a pressure of the natural gas sample to be detected of the first site;
the correction coefficient is determined based on the temperature and/or pressure before reduction and the temperature and/or pressure at extraction of the natural gas sample to be detected.
7. A computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer operates the natural gas energy metering composition obtaining method according to any one of claims 1 to 3.
8. A natural gas energy metering component obtaining apparatus comprising a processor for performing the natural gas energy metering component obtaining method according to any one of claims 1 to 3.
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