CN114399336A - Natural gas energy metering method and system based on Internet of things - Google Patents
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Abstract
The embodiment of the specification provides a natural gas energy metering method and system based on the Internet of things, and the method comprises the steps of responding to a query request received by a user platform, and acquiring natural gas detection parameters detected by a perception control platform through a sensing network platform; processing the natural gas detection parameters to determine natural gas metering data; and sending the natural gas metering data to the user platform via a service platform.
Description
Technical Field
The specification relates to the field of natural gas metering, in particular to a natural gas energy metering method and system based on the Internet of things.
Background
With the development of science and technology, the gas industry is always promoted to be developed towards digitalization, networking, automation, integration and low energy consumption, the demand of natural gas in the urban gas and traffic fields maintains a high-speed growth trend compared with the traditional energy, and with the increasing complexity of customer groups faced by gas companies, the gas application scenes are more diversified, and higher requirements are provided for the accuracy and stability of gas metering.
Therefore, it is necessary to provide a natural gas energy metering method based on the internet of things, which implements real-time control on the natural gas metering process so as to meet diversified natural gas metering requirements.
Disclosure of Invention
One or more embodiments of the present description provide a natural gas energy metering method based on the internet of things. The natural gas energy metering method comprises the following steps: responding to a query request received by a user platform, and acquiring natural gas detection parameters detected by a perception control platform through a sensing network platform; processing the natural gas detection parameters to determine natural gas metering data; and sending the natural gas metering data to the user platform via a service platform.
One or more embodiments of the present description provide a natural gas energy metering internet of things system. The system comprises a user platform, a service platform, a management platform, a sensing network platform and a perception control platform, wherein: the user platform is used for receiving a query request input by a user; the management platform is used for processing the natural gas detection parameters, determining natural gas metering data and sending the natural gas metering data to the service platform; the service platform is used for sending the natural gas metering data to the user platform; and the perception control platform is used for responding to the query request to acquire natural gas detection parameters and sending the natural gas detection parameters to the management platform through the sensing network platform.
One or more embodiments of the present description provide a natural gas energy metering system. The system comprises: the acquisition module is used for responding to the query request received by the user platform and acquiring natural gas detection parameters detected by the perception control platform through the sensing network platform; the processing module is used for processing the natural gas detection parameters and determining natural gas metering data; and the transmission module is used for transmitting the natural gas metering data to the user platform through a service platform.
One or more embodiments of the present description provide a natural gas energy metering system. The system comprises: at least one database, the at least one database comprising query requests; at least one processor in communication with the at least one database, wherein, in responding to the query request, the at least one processor is configured to: acquiring natural gas detection parameters detected by a perception control platform through a sensing network platform; processing the natural gas detection parameters to determine natural gas metering data; and sending the natural gas metering data to the user platform via a service platform.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer, cause the computer to perform a method for natural gas energy metering comprising: responding to a query request received by a user platform, and acquiring natural gas detection parameters detected by a perception control platform through a sensing network platform; processing the natural gas detection parameters to determine natural gas metering data; and sending the natural gas metering data to the user platform via a service platform.
Drawings
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 schematic view of an application scenario of a natural gas energy metering internet of things system 100 according to some embodiments of the present description;
FIG. 2 is a schematic diagram of exemplary hardware and software components of an exemplary computing device 200, shown in accordance with some embodiments of the present description;
FIG. 3 is an exemplary block diagram of a natural gas energy metering system 300 according to some embodiments herein;
FIG. 4 is an exemplary flow diagram of a natural gas energy metering method according to some embodiments described herein;
FIG. 5 is an exemplary flow chart of a natural gas metering data determination method according to some embodiments described herein;
fig. 6 is an exemplary flow chart of an abnormal device determination method according to some embodiments of the present description.
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, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. 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.
Fig. 1 is a schematic view of an application scenario of a natural gas energy metering internet of things system 100 according to some embodiments of the present disclosure.
As shown in fig. 1, the system 100 for measuring the energy of natural gas in the internet of things may include a user platform 110, a service platform 120, a management platform 130, a sensor network platform 140, and a perception control platform 150.
In some embodiments, the natural gas energy metering internet of things system 100 may enable energy metering and management of natural gas by implementing the methods and/or processes disclosed herein.
The user platform 110 may be a management system or platform for a gas supply grid. The gas supply network may be a gas supply network used by a natural gas network company to provide natural gas, and the gas supply network may transport the natural gas from a gas supply node (e.g., a gas supply station, etc.) to a gas terminal (e.g., a factory, a residence, etc.) that requires the use of the natural gas. In some embodiments, the user platform 110 may interact with information and/or data at a back end of a pipe network of a gas supply pipe network (e.g., pipe network users, pipe network centers, etc.), for example, the user platform 110 may be configured to receive a query request input by a pipe network user (e.g., a query request regarding natural gas consumption). For example, the user platform 110 may receive a query request (e.g., a query request for energy data regarding natural gas) input by a grid center. In some embodiments, the user platform 110 may interact with the management platform 130 in the gas energy metering internet of things system 100 through the service platform 120 for information and/or data. For example, the user platform 110 may receive, via the service platform 120, natural gas metering data (e.g., energy data for natural gas, etc.) determined by the management platform 130.
The service platform 120 may be used to perform service information transmission or storage work, wherein the service information includes information related to natural gas metering data, query requests, and the like. In some embodiments, the service platform 120 may be configured as a service information sensor. In some embodiments, the service platform 120 may classify and extract information and/or data of each component in the gas energy metering internet of things system 100, and provide necessary information and/or data for the gas energy metering internet of things system 100. In some embodiments, depending on the type of query request, the service platform 120 may send at least one of natural gas metering data (e.g., energy data and volumetric data) to the user platform 110. For example, when the query request is a volumetric data query, the service platform 120 may send the volumetric data to the user platform 110. In some embodiments, the service platform 120 may also perform further processing on the stored data (e.g., data encryption, etc.). See fig. 4 and its associated description for more on the service platform 120. It should be noted that the above description of the service platform 120 is not necessarily what is implemented.
In some embodiments, in response to a query request received by the user platform 110, a processor in the management platform 130 is configured to: and receiving the natural gas detection parameters detected by the perception control platform 150 via the sensing network platform 140, and processing the natural gas detection parameters to determine natural gas metering data. For example, the processor may process the first detection parameter using a preset algorithm to determine first energy data; processing the second detection parameter by using the prediction model to determine second energy data; and determining an abnormal device based on the first energy data and the second energy data. In some embodiments, in response to a query request received by the user platform 110, a processor in the management platform 130 is configured to: at least one of the energy data and the volumetric data in the natural gas metering data is transmitted to the user platform 110 via the service platform 120 according to the type of the query request.
The sensor network platform 140 may be a gateway device that transmits data and/or information. In some embodiments, sensor network platform 140 may utilize transmission technologies such as Profnet, 5G, ethernet, etc. to transmit data and/or information. In some embodiments, the sensor network platform 140 may provide a network and gateway for data and/or information interaction between one or more components of the gas energy metering internet of things system 100 (e.g., the management platform 130 and the perception control platform 150). For example, in response to the query request received by the user platform 110, the sensor network platform 140 may transmit the natural gas detection parameters acquired by the sensing and control platform 150 to the management platform 130 completely, quickly, safely and effectively for comprehensive calculation. In some embodiments, the sensor network platform 140 may implement various protocol conversions and communication management, so that the gas energy metering internet of things system 100 may be compatible with sensors from different manufacturers, increasing flexibility in sensor selection.
The sensing and control platform 150 may be used to obtain natural gas detection parameters. In some embodiments, the perception control platform 150 may be configured as a natural gas energy metering terminal. The sensing and control platform 150 may obtain the natural gas detection parameters through the detection device. The sensing device may be used to sense a parameter of interest of the natural gas (i.e., a natural gas sensing parameter). In some embodiments, in response to the query request received by the user platform 110, the sensing and control platform 150 may obtain the natural gas detection parameters corresponding to the query request, and send the natural gas detection parameters to the management platform 130 through the sensor network platform 140 for processing. In some embodiments, the natural gas detection parameters collected by the sensing and control platform can also be directly processed at the terminal.
In some embodiments, the sensing and control platform 150 may be located at a user end and/or a pipe network end of the gas supply network. The user side can be a gas terminal in a gas supply pipe network and is used for managing and recording natural gas conveyed to the user. For example, the user terminal may include a smart gas meter for each residence in the cell. The pipe network end can be a key gas supply node in a gas supply pipe network and is used for controlling the transportation of natural gas. For example, the network end may include a gas station, a regional regulatory agency, and the like. In some implementations, whether the gas supply grid is operating properly can be determined by data information (e.g., gas metering data, etc.) of the gas at the customer site and at the grid site.
FIG. 2 is a schematic diagram of exemplary hardware and software components of an exemplary computing device 200 shown in accordance with some embodiments of the present description.
In some embodiments, user platform 110, service platform 120, management platform 130, sensory network platform 140, and awareness control platform 150 may be implemented on computing device 200. For example, a processor in management platform 130 may be implemented on computing device 200 and configured to perform the functions of the processor disclosed in this specification.
The computing device 200 may be a general purpose computer or a special purpose computer, both of which may be used to implement the natural gas energy metering internet of things system 100 of the present specification. The computing device 200 may be used to implement any of the components of the gas energy metering internet of things system 100 as described herein. For example, a processor may be implemented on computing device 200 by its hardware, software programs, firmware, or a combination thereof. For convenience, only one computer is shown in the figures, but the computer functions described herein in connection with energy metering or querying may be implemented in a distributed fashion across multiple similar platforms to spread out the processing load.
The computing device 200 may also include a hard disk controller in communication with a hard disk, a keypad/keyboard controller in communication with a keypad/keyboard, a serial interface controller in communication with a serial interface device, a parallel interface controller in communication with a parallel interface device, a display controller in communication with a display, and the like, or any combination thereof.
For purposes of illustration only, only one CPU and/or processor is illustratively depicted in computing device 200. However, it should be noted that the computing device 200 in this specification may include multiple CPUs and/or processors, and thus the operations and/or methods described in this specification that are implemented by one CPU and/or processor may also be implemented by multiple CPUs and/or processors, collectively or independently. For example, if in this description the CPUs and/or processors of computing device 200 perform operations a and B, it should be understood that operations a and B may also be performed by two different CPUs and/or processors in computing device 200, either jointly or independently (e.g., a first processor performing operation a, a second processor performing operation B, or both a first and second processor performing operations a and B).
FIG. 3 is an exemplary block diagram of a natural gas energy metering system 300, according to some embodiments described herein. The natural gas energy metering system 300 is deployed on a management platform. In some embodiments, the natural gas energy metering system 300 may include an acquisition module 310, a processing module 320, and a transmission module 330.
The obtaining module 310 may be configured to obtain, via the sensor network platform, the natural gas detection parameters detected by the sensing and control platform in response to a query request received by the user platform. See fig. 4 and its associated description for more on query requests, gas detection parameters.
The processing module 320 may be configured to process the natural gas detection parameters to determine natural gas metering data. In some embodiments, the processing module 320 may process at least one first detection parameter based on a preset algorithm to determine the first energy data, wherein the first detection parameter is detected based on the first detection device. In some embodiments, the processing module 320 may further process at least one second detection parameter based on the prediction model to determine second energy data, the second detection parameter being detected based on a second detection device. See fig. 4, 5 and their associated description for more of the processing module 320.
The transmission module 330 may be used to transmit the natural gas metering data to the user platform via the service platform. In some embodiments, the transmission module 330 may transmit at least one of energy data and volume data to the user platform according to a query request. See fig. 4 and its associated description for more on the energy data and the volume data.
It should be noted that the above description of the natural gas energy metering system 300 and its modules 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 modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the acquisition module 310, the processing module 320 and the transmission module 330 disclosed in fig. 3 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.
FIG. 4 is an exemplary flow diagram of a natural gas energy metering method in accordance with certain embodiments described herein. As shown in fig. 4, the process 400 includes the following steps. In some embodiments, flow 400 may be performed by a processor (e.g., processor 220). For example, the process 400 may be stored in a storage device in the form of a program or instructions, which when executed by a processor or the modules shown in fig. 3, may implement the process 400. In some embodiments, flow 400 may utilize one or more additional operations not described below, and/or may not be accomplished by one or more of the operations discussed below.
And step 410, responding to the query request received by the user platform, and acquiring natural gas detection parameters detected by the perception control platform through the sensing network platform. In some embodiments, step 410 may be performed by acquisition module 310.
The user platform may be a management system or platform for a gas supply network. The user platform can be used for receiving a query request input by a user and also can be used for acquiring natural gas metering data determined by the management platform. See fig. 1 and its associated description for more on the user platform.
The query request may be a request to query data related to natural gas. For example, the query request may be a request to query for volumetric data, energy data, etc. of the natural gas.
In some embodiments, the query request may include a volumetric data query and an energy data query. The volume data query is a request for querying volume data of the natural gas, and the energy data query is a request for querying energy data of the natural gas. The volume data may be data related to the volume of the gas and the energy data may be data related to the energy of the gas. The volumetric data and energy data may be used to make a natural gas charge. In some embodiments, the volumetric data and energy data may be determined based on the natural gas detection parameters, see step 420 for more information on determining the volumetric data and energy data.
In some embodiments, the query request may be directed to one of the network ends and/or the user end in the gas supply network. For example, the query request may be a volume data query to one of the network ends of the gas supply network. In some embodiments, the query request may also be a request to query for gas flow, pressure, etc. of the natural gas.
The sensor network platform may be a gateway device that transmits data and/or information. For example, a sensory network platform may be used to provide a data transport network between a sensory control platform and a management platform. See fig. 1 and its associated description for more on a sensor network platform.
The sensing and controlling platform can be used for acquiring natural gas detection parameters. In some embodiments, the sensing and control platform comprises at least one detection device, and the sensing and control platform can acquire the natural gas detection parameters through the detection device. See fig. 1 and its associated description for more on the perceptual control platform.
The natural gas detection parameter may be a parameter associated with natural gas. For example, the natural gas detection parameters may include natural gas density, natural gas temperature, natural gas pressure, natural gas composition, content of individual components, natural gas flow, natural gas compressibility factor, and the like. The natural gas detection parameters can be detected by detection equipment. In some embodiments, the natural gas detection parameters may also be preset, which may be provided by a back end of the pipeline network (e.g., a natural gas supplier). For example, physical parameters such as natural gas composition, natural gas compression factor, natural gas density, and natural gas calorific value may be provided from the back end of the pipe network.
The detection device may be used to detect a parameter associated with the natural gas. The sensing devices may include component sensors, gas meters, temperature sensors, pressure sensors, and the like. The composition sensor may be used to measure the composition and content of the natural gas, and the composition sensor may include a gas chromatograph. The gas meter can be used for measuring the flow and the mass of natural gas, and can comprise an ultrasonic flowmeter, a membrane gas meter, a turbine flowmeter, an orifice plate flowmeter, a nozzle flowmeter, a precession vortex flowmeter, a volumetric flowmeter, a mass flowmeter, a flow computer and the like. The temperature sensor may be used to measure the temperature of the natural gas and the pressure sensor may be used to measure the pressure of the combustion gas.
The type of the detection equipment for detecting the natural gas detection parameters can be determined according to the natural gas detection parameters which need to be acquired. For example, when the natural gas detection parameter to be acquired is component data of natural gas, the detection device may be a gas chromatograph.
In some embodiments, the sensory control platform comprises at least one first detection device located at the end of the pipeline network. The pipe network end may be an important node in a gas supply pipe network, for example, a gas transmission station in the gas supply pipe network, a natural gas control point of a specific area (e.g., a cell), and the like.
In some embodiments, the obtaining module 310 may obtain at least one first detection parameter through detection by at least one first detection device, where the first detection parameter is a parameter related to natural gas detected by the first detection device, and the first detection device is a detection device located at a pipeline end. The natural gas detection parameters can be detected by using detection equipment with higher precision and more types at the pipe network end, so that the first detection equipment can be used for detecting all types of natural gas detection parameters, that is, the first detection parameters which can be obtained at the pipe network end by the obtaining module 310 include all types of natural gas detection parameters. For example, the first sensed parameter may include natural gas density, natural gas temperature, natural gas pressure, natural gas composition, content of each component, natural gas flow rate, natural gas compressibility factor, and the like.
In some embodiments, the first detection parameter may be used to determine the first energy data, see fig. 5 and its associated description for more information regarding determining the first energy data.
In some embodiments, the sensory control platform comprises at least one second detection device located at the user end. The user terminal can be a gas terminal in a gas supply network, for example, an intelligent gas meter of each residence in a community.
In some embodiments, the obtaining module 310 may obtain at least one second detection parameter through detection by at least one second detection device, where the second detection parameter is a parameter related to natural gas detected by the second detection device, and the second detection device is a detection device located at a user end. The natural gas detection parameters can be detected by using detection devices with lower precision and fewer types at the user end, so that the second detection device is only used for detecting partial types of natural gas detection parameters, that is, the second detection parameters that the obtaining module 310 can obtain at the pipe network end include partial types of natural gas detection parameters. For example, the second detection parameters include only readily available natural gas detection parameters such as natural gas pressure, natural gas temperature, natural gas composition (provided by the back end of the pipeline network), and the like.
The second detection parameter may be used to determine the second energy data, see fig. 5 and its associated description for further details regarding determining the second energy data.
In some embodiments, in order to facilitate the detection and measurement of the natural gas data of multiple user terminals managed and controlled by the natural gas energy metering system, more accurate and detailed natural gas detection parameters are often required at the pipe network end. Therefore, the first detection equipment at the end of the pipeline network is more in types and higher in accuracy, and can detect a plurality of natural gas detection parameters with higher accuracy. The cost of installing various types of detection equipment at the user end is high, the installation difficulty is high, and meanwhile, due to the fact that the installation cost and the later maintenance cost of high-precision detection equipment are high (for example, the updating and maintenance frequency of the high-precision detection equipment is high), the installation of the high-precision detection equipment at the user end is not ideal. Therefore, the second detection device at the user end is of a small variety and low accuracy, and can only detect partial natural gas detection parameters, and is often some parameters (such as natural gas pressure, natural gas density, and the like) which are easy to detect. For example, the first detection device at the pipe network end may be a high-precision detection device such as an ultrasonic flow meter, a turbine flow meter, an orifice plate flow meter, a gas chromatograph, and the like. Meanwhile, detection equipment for detecting various natural gas detection parameters can be arranged at the end of the pipe network, such as: the natural gas measuring device comprises a component sensor for measuring components and content of natural gas, a gas meter for measuring flow and mass of the natural gas, a temperature sensor for measuring temperature of the natural gas, a pressure sensor for measuring pressure of the gas and the like. For another example, the second detection device at the user end may be a detection device with relatively low accuracy, such as a model gas meter, a nozzle flow meter, or the like, or may be a detection device that is easy to detect and obtain a corresponding parameter, such as a pressure sensor, a temperature sensor, or the like.
In some embodiments, in response to different query requests, the obtaining module 310 may control corresponding detection devices in the sensing and control platform to perform detection, so as to obtain corresponding natural gas detection parameters. For example, when the query request is a volume data query, the obtaining module 310 may control the gas meter to perform the detection, and obtain the corresponding natural gas detection parameter including the volume data. When the query request is an energy data query, the obtaining module 310 may control a temperature sensor, a pressure sensor, a component sensor, and the like to perform detection, the corresponding natural gas detection parameters may include a natural gas temperature, a natural gas pressure, natural gas components, contents of each component, and the like, and the processing module 320 may process the corresponding natural gas detection parameters to determine the energy data. See step 420 for more on determining energy data.
In some embodiments, the obtaining module 310 may obtain the natural gas detection parameters in real time through the sensing control platform and periodically generate a detection log containing the natural gas detection parameters. When the user platform receives a query request input by a user, the obtaining module 310 may determine corresponding natural gas detection parameters according to a plurality of detection logs stored in the sensing control platform. For example, after receiving the query request, the obtaining module 310 may directly retrieve the detection log closest to the time when the query request is received, and extract the corresponding natural gas detection parameters therefrom.
Responding to a query request received by a user platform, and detecting by a sensing control platform through detection equipment to obtain corresponding natural gas detection parameters; the management platform can acquire the natural gas detection parameters through the sensing network platform, process the natural gas detection parameters to determine natural gas metering data, and send the natural gas metering data to the user platform through the service platform.
The natural gas energy metering system can quickly and accurately realize the metering and the query of the natural gas through the interaction of a plurality of platforms in the natural gas energy metering Internet of things system, and is convenient for a gas supply mechanism to carry out statistics and charging.
And step 420, processing the natural gas detection parameters and determining natural gas metering data. In some embodiments, step 420 may be performed by processing module 320.
The natural gas metering data may be data reflecting information related to natural gas. For example, the natural gas metering data may be data reflecting the energy and/or volume of the natural gas.
In some embodiments, the natural gas gauging data may comprise at least one of energy data and volumetric data. The energy data may be data representing the heat generated by the natural gas and the volume data may represent the volume of the natural gas under standard conditions.
In some embodiments, the processing module 320 may process the gas detection parameters to determine volumetric data of the gas. For example, the processing module 320 may process the natural gas detection parameters (e.g., natural gas flow, natural gas temperature, natural gas pressure, natural gas compression factor, etc.) using the following calculation formula (1) to determine volumetric data of the natural gas:
Vn＝Vt×Pt/Pn×(273.15+Tn)/(273.15+Tt)×Fz 2 (1)
wherein,
is a super compression factor, ZnNatural gas compression factor in standard state (standard condition, atmospheric pressure is 101.325Kpa, temperature is 20 degree centigrade), ZtNatural gas compression factor, V, obtained for a detection devicenIndicating the natural gas flow rate, V, under standard conditionstIndicating the natural gas flow rate, P, captured by the detection devicenIndicating the pressure of the natural gas under standard conditions, PtIndicating the natural gas pressure, T, acquired by the detection devicenIndicating the temperature, T, of the natural gas under standard conditionstIndicating the temperature of the natural gas captured by the sensing device.
The determined volume data of the natural gas is the natural gas flow rate in unit time under standard conditions. In some embodiments, Zn、Pnn、TnMay be preset. Zt、Pt、TtThe detection device can be provided by the back end of the pipe network and can also be detected and obtained by the detection equipment.
In some embodiments, the processing module 320 may process the gas detection parameters to determine energy data for the gas. For example, the processing module 320 may process the natural gas detection parameters (e.g., natural gas composition, content of each component, natural gas pressure, natural gas compression factor, etc.) to determine the energy data of the natural gas using the following calculation formula (2):
wherein E represents the energy generated by the complete combustion of natural gas under standard conditions,
For the true volumetric heat value, V, of natural gasnIndicating the natural gas flow rate, t, under standard conditions1Reference of temperature, t, for combustion2For measuring reference condition temperature, p2Is a pressure.
In some embodiments, the real volumetric heating value of the natural gas can be calculated by equation (2-1):
wherein,
is a temperature t1Ideal volumetric heating value Z when lower natural gas is used as ideal gasmixTo measure the compression factor at the reference condition (i.e. when t)2Is 20 degrees Celsius, p2A compression factor at one standard atmosphere).
In some embodiments, the desired volumetric heating value of the natural gas may be calculated by equation (2-2):
wherein,
at a temperature t1The molar calorific value when natural gas is used as ideal gas, R is the molar gas constant, T2Is absolute temperature, T2＝t2+273.15。
In some embodiments, the molar calorific value of the natural gas may be calculated by equation (2-3):
wherein,
reference temperature t for combustion of natural gas as ideal gas1The molar heating value of component j in natural gas.
In summary, the calculation formula (3) for determining the energy data of the natural gas is:
the volume data is V under the condition that the determined energy of the natural gas is standardnEnergy data corresponding to natural gas. In some embodiments of the present invention, the,
and
may be preset and may be calculated according to the actually provided preset value when determining the energy data of the natural gas.
In some embodiments, the processing module 320 may process the natural gas detection parameters obtained at different gas supply nodes (e.g., a pipe network end, a user end) of the gas supply pipe network by using different processing methods to determine the energy data. For example, for a first prediction parameter obtained at the pipe network end, the processing module 320 may process the first detection parameter based on the above calculation formula (3) to determine corresponding energy data (i.e., first energy data). For example, for the second detection parameter obtained at the user end, the processing module 320 may process the second detection parameter based on the prediction model to determine corresponding energy data (i.e., second energy data). See fig. 5 and its associated description for further details regarding the processing of the first detection parameter and the second detection parameter.
In some embodiments, the processing module 320 may also directly process the natural gas detection parameters at one or more sensing control platforms to determine natural gas metering data.
By directly processing the natural gas detection parameters on one or more perception control platforms, the calculation amount of the management platform can be distributed on each perception control platform, the performance requirement on the management platform is reduced, and the calculation efficiency of the system is improved.
And step 430, transmitting the natural gas metering data to the user platform through the service platform. In some embodiments, step 430 may be performed by transmission module 330.
The service platform may be used to perform service information transfer or storage work, for example, the gas metering data may be periodically transmitted to the service platform, and reference is made to fig. 1 and its related description for more about the service platform.
In some embodiments, the transmission module 330 may transmit the natural gas metering data to the user platform through the service platform after the service platform receives the natural gas metering data. In some embodiments, the service platform may store the natural gas metering data in advance, and the transmission module 330 may transmit the natural gas metering data to the user platform after the user platform receives the query request.
In some embodiments, the service platform may encrypt the natural gas metering data after receiving the natural gas metering data, so as to ensure the safety of the natural gas metering data. When the user platform receives the query request, the transmission module 330 may process the encrypted data in the service platform, and send the corresponding natural gas metering data to the user platform via the service platform.
In some embodiments, the transmission module 330 may transmit at least one of the energy data and the volume data to the user platform via the service platform according to the type of the query request, i.e., the transmission module 330 may transmit the energy data and/or the volume data corresponding to the query request to the user platform according to the type of the query request. For example, when the query request is a volumetric data query, the transmission module 330 may transmit the volumetric data of the natural gas to the user platform.
And corresponding natural gas metering data are sent to the user platform according to the type of the query request, so that the corresponding data can be acquired in a targeted manner, and the gas supply mechanisms with different metering modes can charge by using the natural gas metering data. Meanwhile, the calculated amount of the natural gas energy metering system can be reduced by acquiring corresponding data in a targeted manner, and the transmission efficiency of the system is improved.
Through interaction of various platforms and/or systems in the natural gas energy metering Internet of things system, the natural gas energy metering method can be realized, and the acquisition and feedback of natural gas related data (such as natural gas detection parameters and natural gas metering data) are realized.
FIG. 5 is an exemplary flow chart of a method of natural gas metering data determination shown in accordance with some embodiments herein. As shown in fig. 5, the process 500 includes the following steps. In some embodiments, flow 500 may be performed by a processor (e.g., processor 220). For example, the process 500 may be stored in a storage device in the form of a program or instructions, which when executed by a processor or the modules shown in fig. 3, may implement the process 500. In some embodiments, flow 500 may utilize one or more additional operations not described below, and/or be accomplished without one or more of the operations discussed below.
In some embodiments, the first detection parameter may be used to determine energy data of the natural gas at the grid end. The first energy data may be energy data of natural gas at the grid end. In some embodiments, the first energy data may be an energy value per unit mass or an energy value per unit volume.
The preset algorithm may be used to determine the first energy data based on the first detection parameter, for example, the preset algorithm may be the calculation formula (3) in step 420 described above.
In some embodiments, after the first detection device determines the first detection parameter, the processing module 420 may process the first detection parameter using a predetermined algorithm to determine the first energy data.
Because the precision of the detection equipment at the pipe network end is higher and the types are more, the precision of the natural gas detection parameters at the pipe network end is higher and the types are more, so that the natural gas metering data can be directly obtained through calculation of a preset algorithm, and subsequent data correction and fault detection are facilitated.
In some embodiments, the second detection parameter may be used to determine energy data of the natural gas at the user end. The second energy data may be energy data of natural gas at the user end. In some embodiments, the second energy data may be an energy value per unit mass or an energy value per unit volume.
The predictive model may determine second energy data based on the second detection parameter. The predictive model may be a machine learning model, for example, a Deep Neural Networks (DNN) model. The input to the predictive model includes at least one second sensed parameter (e.g., natural gas temperature, natural gas pressure, natural gas composition, natural gas density, etc.), and the output is second energy data (i.e., an energy value per unit mass or an energy value per unit volume).
The parameters of the predictive model may be obtained by training. In some embodiments, the predictive model may be trained based on a large number of labeled training samples. For example, a training sample with a label is input into an initial prediction model, a loss function is constructed through the label and the prediction result of the initial prediction model, and the parameters of the model are updated iteratively based on the loss function. And when the trained model meets the preset condition, finishing the training. The preset conditions include loss function convergence, threshold reaching of iteration times and the like.
The training sample at least comprises at least one sample second detection parameter detected at the sample user end, and the label can be second energy data corresponding to the at least one sample second detection parameter. In some embodiments, the label may be obtained by processing a first detection parameter of the sample obtained by the sample pipe network end corresponding to the sample user end based on a preset algorithm. The sample network end corresponding to the sample user end may be a network end for measuring the same object as the sample user end. For example, when a second detection parameter acquired at a certain user terminal at a historical time is taken as a training sample, a first detection parameter of a pipe network terminal closer to the user terminal (within 5km, for example) at the same time may be acquired, and a corresponding first energy data may be determined by processing the first detection parameter based on a preset algorithm, where the first energy data is a label corresponding to the second detection parameter of the sample.
In some cases, a sample client may not have a corresponding sample pipe network. In order to collect the training data and ensure the effect of the trained model, in some embodiments, the training samples of the predictive model may further include at least one sample first detection parameter detected at the sample pipe network end, and the corresponding label is first energy data corresponding to the at least one sample first detection parameter. In some embodiments, the tag may be obtained by processing the at least one sample first detection parameter based on a preset algorithm.
If the training samples for training the prediction model are collected based on the sample pipe network end, in some embodiments, when the prediction model is actually applied, and the second energy data of the user end is determined based on the input second detection data of the user end, the input features in the actual application can be processed because the training samples are different from the input feature types in the actual application. For example, a value of a feature type which is missing compared to the training sample at the time of actual application is set to a preset value, such as 0.
In some embodiments, the training samples of the prediction model may further include at least one sample natural gas detection parameter detected by other sample gas supply nodes (e.g., other sample user terminals), a relationship between the sample user terminal and the other sample gas supply nodes (e.g., a distance relationship in a gas supply pipe network, etc.), and the corresponding label is the first energy data corresponding to the at least one sample first detection parameter. The label can be obtained by processing a first detection parameter obtained by the network side based on a preset algorithm. This makes it possible to further strengthen the prediction model and improve the prediction accuracy of the prediction model. Correspondingly, the input of the prediction model may include, in addition to the at least one second detection parameter detected at the current user end, natural gas detection parameters acquired by other gas supply nodes (e.g., other user ends), and a relationship between the other gas supply nodes and the current user end. For example, the input of the prediction model may further include second detection parameters obtained at other user terminals closer to the current user terminal (within 3 km) at the same time, and a relationship between the current user terminal and the other user terminals. For example, when the second energy data of a certain house in the cell is determined by using the prediction model, in addition to the second detection parameter detected at the current house being input into the prediction model, the second detection parameter detected at other houses in the same gas supply network of the cell and the relationship between the other houses and the current house can be input into the prediction model together as input parameters for prediction. Therefore, the influence of the natural gas use conditions of other gas supply nodes on the current user side can be fully considered, and the prediction accuracy of the prediction model is improved.
The accuracy of the detection equipment at the user end is low, and the difficulty of using the high-accuracy detection equipment is high, so that the accuracy of the natural gas detection parameters at the user end is low, the types of the natural gas detection parameters are few, and the natural gas metering data cannot be directly calculated through a preset algorithm. And through the prediction model, corresponding natural gas metering data can be accurately predicted under the condition that the natural gas detection parameters are few in types.
It should be noted that, there is no order limitation on the foregoing steps 510 and 520, and the steps 510 and 520 may be executed separately or together according to actual needs, for example, when a pipe network center needs to obtain energy data, the step 510 may be executed separately.
In some embodiments, the processing module 320 may determine whether an anomalous device is present based on the first energy data and the second energy data. In response to the presence of the anomalous device, for each of the at least one first detection device and the at least one second detection device, the processing module 320 may determine a probability that the anomalous device is present for the detection device based on the information about the detection device, the first energy data, and the second energy data, and determine the anomalous device based on the probability. See fig. 6 and its associated description for more on determining an anomalous device.
Fig. 6 is an exemplary flow chart of an abnormal device determination method according to some embodiments of the present description. As shown in fig. 6, the process 600 includes the following steps. In some embodiments, flow 600 may be performed by a processor (e.g., processor 220). For example, flow 600 may be stored in a memory device in the form of a program or instructions, which when executed by a processor or module as shown in fig. 3, may implement flow 600. In some embodiments, flow 600 may utilize one or more additional operations not described below, and/or be accomplished without one or more of the operations discussed below.
And step 610, comparing the first energy data with the second energy data, and judging whether abnormal equipment exists. In some embodiments, step 610 may be performed by processing module 320.
The abnormal device may be a detection device that is in an abnormal state or detects abnormal data, and for example, the abnormal device may include a detection device that is not in an operating state, a detection device that detects an error parameter, or the like. When abnormal equipment exists, the natural gas detection parameters acquired by the perception control platform are wrong, and therefore the first energy data or the second energy data are inaccurate.
The data comparison may be used to determine the first energyThe magnitude of the difference between the data and the second energy data. The result of the data comparison can be used to determine whether an abnormal device exists. When the difference between the first energy data and the second energy data exceeds a preset threshold, it may be determined that an abnormal device exists in the detection device. The preset threshold may be a threshold of a difference between the preset first energy data and the second energy data, for example, the preset threshold may be 50J/m3。
And step 620, responding to the existence of the abnormal device, and determining the probability of the existence of the abnormality of the detection device according to the relevant information of the detection device, the first energy data and the second energy data for each detection device in the at least one first detection device and the at least one second detection device.
The related information of the detection device may refer to information related to the detection device. The related information of the detection device may include an operating environment of the detection device (e.g., a temperature, a pressure, etc. of the operating environment), a detection parameter corresponding to the detection device, device information of the detection device, and the like. The device information of the detection device may include an error range of the detection device, a type of the detection device, a model of the detection device, a maintenance record of the detection device, a usage duration of the detection device, and the like.
In some embodiments, the processing module 320 may determine the probability that the anomaly exists in the detection device according to the relevant information of the detection device, the first energy data and the second energy data. For example, the processing module 320 may determine the probability of detecting the presence of an anomaly by the anomaly determination model. The anomaly determination model may be used to determine the probability of detecting the presence of an anomaly in the device.
The abnormality determination model may be a machine learning model, for example, DNN, Transformer, or the like. The input of the abnormality determination model includes information about the detection devices, the first energy data, and the second energy data, and the output is a probability of the presence of an abnormality of each detection device.
In some embodiments, the processing module 320 may input the related information of each detection device, the first energy data and the second energy data into the anomaly determination model respectively, and determine the probability of the anomaly existing in each detection device. For example, by inputting the related information of the detection device a, the first energy data, and the second energy data into the abnormality determination model, the probability that the detection device a has an abnormality can be determined.
In some embodiments, the processing module 320 may construct a related information input vector based on the related information of each detection device, where the related information input vector corresponds to the related information of each detection device one to one, and then input the related information input vector, the first energy data, and the second energy data into the anomaly determination model, and output an anomaly probability vector, where the anomaly probability vector corresponds to the probability of each detection device having an anomaly. For example, the related information input vector may be (a, b, c …), where a is related information of the detecting device a, b is related information of the detecting device b, and c is related information of the detecting device c, the related information input vector (a, b, c …), the first energy data, and the second energy data are input to the abnormality determination model, and the output abnormality probability vector is (0.1, 0.6, 0.2 …), where 0.1 is a probability that the detecting device a has an abnormality, 0.6 is a probability that the detecting device b has an abnormality, and 0.2 is a probability that the detecting device c has an abnormality.
The parameters of the abnormality determination model may be obtained by training. In some embodiments, the anomaly determination model may be trained based on a large number of labeled training samples. For example, a training sample with a label is input into an initial anomaly determination model, a loss function is constructed through the label and a prediction result of the initial anomaly determination model, and parameters of the model are updated iteratively based on the loss function. And when the trained model meets the preset condition, finishing the training. The preset conditions include loss function convergence, threshold reaching of iteration times and the like.
The training sample of the anomaly determination model at least comprises relevant information of the sample detection device, the first energy data of the sample and the second energy data of the sample, and the label can be the probability of the corresponding anomaly of the sample detection device. In some embodiments, the label may be obtained by human annotation.
In some embodiments, the processing module 320 may determine that the detection device is an abnormal device when the probability that the detection device has an abnormality is greater than a preset threshold. The preset threshold may be a preset probability magnitude (e.g., 0.5). For example, when the probability that the detecting device a has an abnormality is 0.6 and the preset threshold value is 0.5, it is determined that the detecting device a is an abnormal device.
Through judging whether abnormal equipment exists, the influence of the abnormal equipment on the natural gas metering can be avoided, and the accuracy of the natural gas metering is improved. Meanwhile, abnormal equipment can be positioned and processed in time, and stable operation of the natural gas energy metering Internet of things system is guaranteed.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. 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 than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
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 method based on the Internet of things is characterized in that the method is executed by a management platform and comprises the following steps:
responding to a query request received by a user platform, and acquiring natural gas detection parameters detected by a perception control platform through a sensing network platform;
processing the natural gas detection parameters to determine natural gas metering data; and
and sending the natural gas metering data to the user platform through a service platform.
2. The method of claim 1, wherein the natural gas gauging data comprises at least one of energy data and volumetric data,
the sending the natural gas metering data to the user platform via the service platform comprises:
sending, via a service platform, at least one of the energy data and the volume data to the user platform according to a type of the query request.
3. The method of claim 2, wherein the sensing and control platform comprises at least one first sensing device located at the end of the pipeline, wherein the natural gas sensing parameter comprises at least one first sensing parameter sensed by the at least one first sensing device,
the processing the natural gas detection parameters and determining natural gas metering data comprises:
and processing the first detection parameter based on a preset algorithm to determine first energy data.
4. The method of claim 3, wherein the sensing and control platform comprises at least one second detection device located at a user end, wherein the gas detection parameters comprise second detection parameters detected by the at least one second detection device,
the processing the natural gas detection parameters and determining natural gas metering data comprises:
the at least one second detection parameter is processed based on the predictive model to determine second energy data.
5. The method of claim 4, further comprising:
determining an anomalous device of the at least one first detection device and the at least one second detection device based on the first energy data and the second energy data, comprising:
comparing the first energy data with the second energy data, and judging whether the abnormal equipment exists or not;
in response to the presence of the anomaly device, for each of the at least one first detection device and the at least one second detection device, determining a probability that the anomaly is present for the detection device based on information about the detection device, the first energy data, and the second energy data;
determining the abnormal device based on a probability that each of the at least one first detection device and the at least one second detection device has an abnormality.
6. A natural gas energy measurement Internet of things system is characterized by comprising a user platform, a service platform, a management platform, a sensing network platform and a perception control platform, wherein:
the user platform is used for receiving a query request input by a user;
the management platform is used for processing the natural gas detection parameters, determining natural gas metering data and sending the natural gas metering data to the service platform;
the service platform is used for sending the natural gas metering data to the user platform;
and the perception control platform is used for responding to the query request to acquire natural gas detection parameters and sending the natural gas detection parameters to the management platform through the sensing network platform.
7. A natural gas energy metering system, the system comprising:
the acquisition module is used for responding to the query request received by the user platform and acquiring natural gas detection parameters detected by the perception control platform through the sensing network platform;
the processing module is used for processing the natural gas detection parameters and determining natural gas metering data; and
and the transmission module is used for transmitting the natural gas metering data to the user platform through a service platform.
8. A natural gas energy metering system, the system comprising:
at least one database, the at least one database comprising query requests;
at least one processor in communication with the at least one database, wherein, in responding to the query request, the at least one processor is configured to:
acquiring natural gas detection parameters detected by a perception control platform through a sensing network platform;
processing the natural gas detection parameters to determine natural gas metering data; and
and sending the natural gas metering data to a user platform through a service platform.
9. The system of claim 8, wherein the natural gas gauging data comprises at least one of energy data and volume data, and wherein to send the natural gas gauging data via the service platform to the user platform, the at least one processor is configured to:
sending, via a service platform, at least one of the energy data and the volume data to the user platform according to a type of the query request.
10. A computer-readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer performs the following natural gas energy metering method:
responding to a query request received by a user platform, and acquiring natural gas detection parameters detected by a perception control platform through a sensing network platform;
processing the natural gas detection parameters to determine natural gas metering data; and
and sending the natural gas metering data to the user platform through a service platform.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114637270A (en) * | 2022-05-17 | 2022-06-17 | 成都秦川物联网科技股份有限公司 | Intelligent manufacturing industry Internet of things based on distributed control and control method |
| CN115790908A (en) * | 2023-02-08 | 2023-03-14 | 成都千嘉科技股份有限公司 | Natural gas metering method and device based on heat metering |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140351187A1 (en) * | 2010-06-25 | 2014-11-27 | Petroliam Nasional Barhad (Petronas) | Method and System for Validating Energy Measurement in a High Pressure Gas Distribution Network |
| CN109936525A (en) * | 2017-12-15 | 2019-06-25 | 阿里巴巴集团控股有限公司 | A kind of abnormal account preventing control method, device and equipment based on graph structure model |
| CN112946231A (en) * | 2021-02-04 | 2021-06-11 | 成都秦川物联网科技股份有限公司 | Natural gas full-period energy metering system and method |
| CN113298127A (en) * | 2021-05-12 | 2021-08-24 | 深圳前海微众银行股份有限公司 | Method for training anomaly detection model and electronic equipment |
| CN113778802A (en) * | 2021-09-15 | 2021-12-10 | 深圳前海微众银行股份有限公司 | Anomaly prediction method and device |
-
2022
- 2022-01-14 CN CN202210043885.8A patent/CN114399336B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140351187A1 (en) * | 2010-06-25 | 2014-11-27 | Petroliam Nasional Barhad (Petronas) | Method and System for Validating Energy Measurement in a High Pressure Gas Distribution Network |
| CN109936525A (en) * | 2017-12-15 | 2019-06-25 | 阿里巴巴集团控股有限公司 | A kind of abnormal account preventing control method, device and equipment based on graph structure model |
| CN112946231A (en) * | 2021-02-04 | 2021-06-11 | 成都秦川物联网科技股份有限公司 | Natural gas full-period energy metering system and method |
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| US11681283B1 (en) | 2022-05-17 | 2023-06-20 | Chengdu Qinchuan Iot Technology Co., Ltd. | Intelligent manufacturing industrial Internet of Things based on distributed control, control methods and media thereof |
| CN115790908A (en) * | 2023-02-08 | 2023-03-14 | 成都千嘉科技股份有限公司 | Natural gas metering method and device based on heat metering |
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