CN116050955B - Digital twinning-based carbon dioxide emission statistics method, device and equipment - Google Patents
Digital twinning-based carbon dioxide emission statistics method, device and equipment Download PDFInfo
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Abstract
The invention discloses a digital twinning-based carbon dioxide emission statistics method, a digital twinning-based carbon dioxide emission statistics device, digital twinning-based carbon dioxide emission statistics equipment and a digital twinning-based carbon dioxide emission statistics storage medium. The method is executed by an internet of things system of a statistical objective, the statistical objective comprising production equipment, the method comprising: acquiring process operation data of production equipment; determining the carbon dioxide emission of production equipment according to a pre-constructed digital twin model and process operation data; the digital twin model is constructed based on a neural network model and a mechanism model matched with the production equipment process; and determining the carbon dioxide emission amount of the statistical target according to the carbon dioxide emission amount of the production equipment. The technical scheme solves the problem of low accuracy of accounting the carbon dioxide emission by a mass balance method, can realize the fine statistics of the carbon dioxide emission through a digital twin model, and improves the accuracy of the carbon dioxide emission statistics.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to a digital twinning-based carbon dioxide emission statistics method, device, equipment and storage medium.
Background
The carbon neutralization is realized by that enterprises counteract the carbon dioxide emission amount generated directly or indirectly in a certain time through modes of tree planting, energy saving, emission reduction and the like. In order to achieve the carbon neutralization objective, the enterprises need to carry out carbon tracking and carbon dioxide emission statistics so that the enterprises can carry out subsequent upgrading and reconstruction and countermeasure.
Currently, mass balance method is generally adopted for calculating carbon dioxide emission in the process production of enterprises such as petroleum, chemical industry and electric power. However, it is difficult to obtain the feeding information in real time for the process production equipment and to obtain the reaction condition in real time in the reactor for the reaction process, so that the accuracy and reliability of the mass balance method for accounting the carbon dioxide emission in the process production process are low.
Disclosure of Invention
The invention provides a digital twin-based carbon dioxide emission statistics method, device, equipment and storage medium, which are used for solving the problem of low accuracy of accounting carbon dioxide emission by a mass balance method, and can realize the fine statistics of carbon dioxide emission by a digital twin model so as to improve the accuracy of carbon dioxide emission statistics.
According to an aspect of the present invention, there is provided a digital twin-based carbon dioxide emission statistical method, which is performed by an internet of things system of statistical targets including production facilities, the method comprising:
acquiring process operation data of production equipment;
determining the carbon dioxide emission of production equipment according to a pre-constructed digital twin model and process operation data; the digital twin model is constructed based on a neural network model and a mechanism model matched with the production equipment process;
and determining the carbon dioxide emission amount of the statistical target according to the carbon dioxide emission amount of the production equipment.
According to another aspect of the present invention, there is provided a digital twin-based carbon dioxide emission statistics apparatus configured in an internet of things system of a statistics target including a production facility, the apparatus comprising:
the process operation data acquisition module is used for acquiring process operation data of the production equipment;
the production equipment emission determining module is used for determining the carbon dioxide emission of the production equipment according to a pre-constructed digital twin model and process operation data; the digital twin model is constructed based on a neural network model and a mechanism model matched with the production equipment process;
the statistical target emission amount determining module is used for determining the statistical target carbon dioxide emission amount according to the carbon dioxide emission amount of the production equipment.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the digital twinning-based carbon dioxide emission statistics method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the digital twinning-based carbon dioxide emission statistics method according to any one of the embodiments of the present invention when executed.
According to the technical scheme, the carbon dioxide emission of the production equipment is determined according to the pre-constructed digital twin model and the process operation data by acquiring the process operation data of the production equipment, and then the carbon dioxide emission of the statistical target is determined according to the carbon dioxide emission of the production equipment. According to the technical scheme, the problem of low accuracy of accounting the carbon dioxide emission by a mass balance method is solved, the fine statistics of the carbon dioxide emission can be realized through a digital twin model constructed based on a neural network model and a mechanism model matched with a production equipment process, and the accuracy of the carbon dioxide emission statistics is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a digital twinning-based carbon dioxide emission statistics method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a digital twinning-based carbon dioxide emission statistics method provided in accordance with a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a digital twin-based carbon dioxide emission statistics device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a digital twin-based carbon dioxide emission statistics method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations.
Example 1
Fig. 1 is a flowchart of a method for counting carbon dioxide emissions based on digital twin, which is applicable to the situation of counting carbon dioxide emissions of units of petroleum, chemical industry and the like, according to an embodiment of the present invention, the method may be performed by a device for counting carbon dioxide emissions based on digital twin, the device may be implemented in a form of hardware and/or software, and the device may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, acquiring process operation data of production equipment.
The scheme can be executed by an internet of things system of a statistical target, wherein the statistical target can be petroleum, chemical industry, electric power and other production units, such as chemical plants. The statistical targets may include hardware devices such as production devices, power consumption devices, and steam pipe networks. The production facility may be a reactor, such as a boiler, in which the material changes. The power consuming device may be a device that consumes electrical energy, such as a computer, a water pump, etc.
It can be appreciated that the internet of things system can include information collection, information transfer, information processing, and other devices. The information collection device may include various types of sensors, such as a gravity sensor, a temperature sensor, a gas sensor, and the like. The information transfer device may include a network communication device such as a router, a switch, etc., and the information processing device may include a computer, a server, etc. The statistical targets may deploy sensors in the hardware devices described above for acquiring process operation data such as feed quality, temperature, and gas concentration. The process operating data may be used for calculation of carbon dioxide emissions.
S120, determining the carbon dioxide emission amount of the production equipment according to the pre-constructed digital twin model and the process operation data.
The Internet of things system can pre-establish a digital twin model of the production equipment for synchronizing the process production process of the production equipment. The process operation data are input into the digital twin model, and the Internet of things system can determine the carbon dioxide emission of the production equipment, so that the problems that the feeding information of the production equipment is difficult to obtain, the reaction process is ambiguous and the like are solved. The digital twin model can be constructed based on a neural network model and a mechanism model matched with the production equipment process. The mechanism model may be a mathematical model obtained by abstracting a process of manufacturing equipment. The carbon dioxide emission of the production equipment can be estimated according to the mechanism model, and accurate carbon dioxide emission is difficult to obtain. The neural network model may be pre-trained for determining high accuracy carbon dioxide emissions based on process operational data and/or carbon dioxide reference emissions output by the mechanism model.
In one possible implementation, the process of constructing the digital twin model includes:
determining a mechanism model of process matching of production equipment, and acquiring at least one set of historical process operation data;
determining carbon dioxide reference emission matched with each set of historical process operation data according to the mechanism model;
determining sample data according to each set of historical process operation data, the carbon dioxide reference emission amount matched with each set of historical process operation data and the carbon dioxide detection emission amount matched with each set of historical process operation data, which are acquired in advance;
according to the sample data, carrying out iterative training on a pre-constructed neural network, and outputting a neural network model conforming to a preset precision threshold;
and determining a digital twin model matched with the production equipment according to the neural network model and the mechanism model matched with the production equipment process.
It is easy to understand that the internet of things system can determine the matched mechanism model according to the process of the production equipment. The Internet of things system can acquire one or more groups of historical process operation data, input each group of historical process operation data into the mechanism model, and can obtain the carbon dioxide reference emission output by the mechanism model. While the production facility performs process production according to each set of historical process operation data, the internet of things system may deploy a carbon dioxide detection facility at the carbon dioxide discharge site to obtain carbon dioxide detection emissions that match each set of historical process operation data. The internet of things system may form a set of sample data from a set of historical process operation data, a reference emission amount of carbon dioxide corresponding to the set of historical process operation data, and a detected emission amount of carbon dioxide corresponding to the set of historical process operation data. The internet of things system can utilize each group of sample data to perform iterative training on the pre-constructed neural network so as to obtain a neural network model conforming to a preset precision threshold.
Specifically, the internet of things system can divide sample data into a training set and a testing set, train the neural network by using the sample data in the training set, and test the prediction effect of the neural network model by using the sample data in the testing set. The number of sample data in the training set and the number of sample data in the test set may be 80% and 20% of the total number of sample data, may be 70% and 30% of the total number of sample data, and may be 60% and 40% of the total number of sample data, respectively.
The internet of things system can train the neural network by taking historical process operation data and carbon dioxide reference emission in the training set as input of the neural network and taking carbon dioxide detection emission as supervision. The system of the internet of things can test the neural network model output by the iteration by using the test set after each iteration training, and can test the neural network model output by the last iteration by using the test set after the iteration number reaches the preset iteration number. For the test of the neural network model, the internet of things system can take historical process operation data and carbon dioxide reference emission in the test set as inputs of the neural network model, and determine the prediction accuracy of the neural network model according to a comparison result of the carbon dioxide predicted emission and the carbon dioxide detected emission output by the neural network model. And if the prediction precision is larger than the preset precision threshold, determining that the neural network model accords with the prediction precision, and if the prediction precision is smaller than or equal to the preset precision threshold, determining that the neural network model does not accord with the prediction precision. Under the condition that the neural network model does not accord with the preset precision threshold, the Internet of things system can continuously train the neural network by utilizing the training set through adjusting the training parameters of the neural network so as to obtain the neural network model accord with the preset precision threshold. The training parameters may include parameters such as the number of neurons in each layer, the number of iterations, and the type of activation function.
After the neural network model is obtained, the Internet of things system can generate a digital twin model according to the neural network model and a mechanism model matched with the production equipment process. It should be noted that the digital twin model may be updated according to process variations of the production facility.
According to the scheme, the carbon dioxide reference emission output by the mechanism model is used as input data of the neural network, an initial value close to the carbon dioxide detection emission can be provided for the neural network, the learning efficiency of the neural network is improved, and the interpretability of the neural network model is improved.
S130, determining the carbon dioxide emission amount of the statistical target according to the carbon dioxide emission amount of the production equipment.
If the statistical target includes only production equipment, the internet of things system may take the carbon dioxide emissions of the production equipment as the carbon dioxide emissions of the statistical target. If the statistical objective includes other equipment related to the carbon dioxide emission amount, such as power consumption equipment, steam pipe network, etc., in addition to the production equipment, the internet of things system can determine the carbon dioxide emission amount of the other equipment, and determine the carbon dioxide emission amount of the statistical objective.
According to the technical scheme, the process operation data of the production equipment are obtained, the carbon dioxide emission of the production equipment is determined according to the digital twin model constructed in advance and the process operation data, and then the carbon dioxide emission of the statistical target is determined according to the carbon dioxide emission of the production equipment. According to the technical scheme, the problem of low accuracy of accounting the carbon dioxide emission by a mass balance method is solved, the fine statistics of the carbon dioxide emission can be realized through a digital twin model constructed based on a neural network model and a mechanism model matched with a production equipment process, and the accuracy of the carbon dioxide emission statistics is improved.
Example two
Fig. 2 is a flowchart of a digital twin-based carbon dioxide emission statistics method according to a second embodiment of the present invention, which is refined based on the above embodiment. As shown in fig. 2, the method includes:
s210, acquiring process operation data of production equipment.
The internet of things system can acquire process operation data of production equipment in real time. The process operation data may be obtained by an industrial data communication protocol such as OPC (Object Linking and Embedding for Process Control, object linking and embedded process control), musbus, etc.
In one specific scenario, using a boiler as an example of a production facility, process operating data may include fuel quality, steam production, and boiler thermal efficiency.
S220, determining the reference emission amount of carbon dioxide according to the process operation data and the mechanism model matched with the process of the production equipment.
The internet of things system can take process operation data as input of a mechanism model, and the mechanism model can output carbon dioxide reference emission.
In the scheme, a boiler is taken as production equipment as an example, and the carbon dioxide reference emission amount of the production equipment is determined. It is understood that the boiler is used as an energy conversion device and is commonly used in the industries of chemical industry, petrochemical industry, oil refining industry, electric power industry and the like. The fuels for industrial boilers mainly include solid fuels, liquid fuels and gas fuels. Carbon dioxide emissions from boilers are mainly derived from fuel combustion, but the composition of the fuel is relatively complex, with a fixed fuel supply, process exhaust gases, process residues, etc. being fed directly into the boiler for combustion as fuel. Therefore, it is difficult to calculate the actual carbon dioxide emission by the mass conservation method, and thus the mechanism model cannot accurately calculate the carbon dioxide emission of the boiler.
The boiler fuel is assumed to be solid fuel bituminous coal, the main carbon content of the bituminous coal is 80-90%, the hydrogen content is 4-6%, and the oxygen content is 10-15%. Thus, in the mechanism model, bituminous coal can be equivalent to，/>The chemical reaction formula of the catalyst is expressed as follows:
；
because the solid fuel is influenced by factors such as caking, quality and the like, the fuel cannot fully react, so that the conversion rate of the reaction needs to be calculated, and a mechanism model can calculate the heat generated by fuel combustion reversely by adopting the production amount of steam in the boiler, so that the conversion rate of the fuel is calculated.
The steam yield of the boiler can be acquired by a flow meter, the heat required for producing a certain amount of steam can be calculated according to a steam enthalpy meter, and the total heat required for producing steam by the steam can be expressed by the following formula:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the amount of steam produced by the boiler,/->Represents the vaporization heat of water in a given state, < >>Indicating the total heat required to produce the steam.
The heat generated by the combustion of the fuel is transferred to the water in the boiler, the heat transfer effect is called heat efficiency, the heat efficiency of the coal-fired boiler is 70-85%, and the heat generated by the combustion of the fuel can be calculated through the heat efficiency. Specifically, the heat generated by the combustion of fuel can be expressed as:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,heat generated for combustion of the fuel; />Indicating the thermal efficiency of the coal-fired boiler.
Through experimental means, the Internet of things system can acquire the heat capacity values of the bituminous coal at different temperatures, a heat capacity formula of the bituminous coal is obtained through fitting, and the heat capacity of the bituminous coal can be fitted into the following formula:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating bituminous coal heat capacity,/->Indicate temperature,/->Each fitting coefficient is represented.
The calculation model of the enthalpy value of the bituminous coal can be expressed as:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the reference temperature, usually +.>。
After the soft coal enthalpy value is obtained, the Internet of things system can calculate the heat released by the boiler reaction, and the required theoretical fuel quantity is converted according to the heat generated by the combustion of the required fuel, so that the conversion rate of the reaction is calculated. The calculation formula of the reaction conversion rate can be expressed as:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the conversion of bituminous coal reactions, +.>Indicating bituminous coal response, ->Indicating the amount of bituminous coal fed.
According to a chemical reaction equation of bituminous coal combustion, the reference emission amount of carbon dioxide can be calculated, and the calculation formula is as follows:
；
in the method, in the process of the invention,represents the reference emission amount of carbon dioxide, 192.2 is +.>Molecular weight of 14 represents->Molecular weight of 44 represents->Molecular weight of (a) is determined.
According to the mechanism model matched with the boiler, the steam quantity, the fuel quantity and the thermal efficiency of the boiler are input into the boiler, and the Internet of things system can output the carbon dioxide reference emission quantity of the boiler.
S230, determining the carbon dioxide emission amount of the production equipment according to the process operation data, the carbon dioxide reference emission amount and the neural network model.
The internet of things system can take the process operation data and the carbon dioxide reference emission as inputs of the neural network model, and take outputs of the neural network model as carbon dioxide emission of the production equipment.
According to the scheme, the carbon dioxide reference emission output by the mechanism model is used as the input of the neural network model, the prior carbon dioxide emission reference data is provided for the neural network model, and the prediction accuracy of the neural network model is improved.
S240, determining the carbon dioxide emission amount of the power consumption equipment and determining the carbon dioxide emission amount of the steam pipe network.
The internet of things system can acquire the power consumption of the power consumption equipment through the power consumption detection equipment, and determine the carbon dioxide emission of the power consumption equipment according to the power consumption. Meanwhile, the Internet of things system can acquire acquisition data of temperature, pressure, flow and the like of the steam pipe network, and can determine the carbon dioxide emission of the steam pipe network according to the acquisition data of the steam pipe network.
In this aspect, optionally, the determining the carbon dioxide emission amount of the power consumption device includes:
acquiring power consumption data of power consumption equipment;
and determining the carbon dioxide emission of the power consumption equipment according to the power consumption data and the first conversion coefficient acquired in advance.
Specifically, the carbon dioxide emission amount calculation formula of the power consumption device may be expressed as:
carbon dioxide emission = power consumption x first conversion factor;
the first conversion coefficient may be a conversion coefficient of power consumption and carbon dioxide emission, and the first conversion coefficient may be 0.5810t/MWh.
The traditional electric quantity consumption calculation is easy to cause incomplete and inaccurate energy consumption data, so that the analysis and diagnosis of the electric quantity consumption cannot be performed, and the carbon dioxide emission of a statistical target due to electric power cannot be accurately calculated. The power consumption equipment is monitored, so that the carbon dioxide emission generated by the power can be accurately calculated, the fine management of enterprises is facilitated, the high-energy equipment is positioned, and the energy conservation and consumption reduction optimization is performed in a certain direction.
By establishing a digital twin model of the power consumption equipment, the required power of the power consumption equipment can be calculated and displayed in real time, and the theoretical power is compared with the power monitored by the actual power consumption equipment to generate a comparison graph, so that theoretical guidance can be provided for energy conservation and consumption reduction.
In a specific example, the internet of things system may configure an intelligent metering plug with a wireless transmission function for a power consumption device, collect data such as voltage, current, power and power consumption of the power consumption device through the intelligent metering plug, and transmit the data to the internet of things system through Wifi.
In this embodiment, optionally, the determining the carbon dioxide emission amount of the steam pipe network includes:
acquiring steam consumption data, steam temperature data and steam pressure data of a steam pipe network, and determining a second conversion coefficient according to the steam temperature data and the steam pressure data;
and determining the carbon dioxide emission of the steam pipe network according to the steam consumption data and the second conversion coefficient.
On the basis of the above scheme, the determining the second conversion coefficient according to the steam temperature data and the steam pressure data includes:
determining a steam enthalpy value according to the steam temperature data and the steam pressure data;
and determining a second conversion coefficient according to the steam enthalpy value, the pre-acquired steam coal folding coefficient and the pre-acquired standard coal carbon dioxide emission coefficient.
The information acquisition equipment of the steam pipe network can transmit the temperature, pressure, flow and other data of the steam pipe network to the Internet of things system through an industrial data communication protocol. The internet of things system can calculate the vapor enthalpy value through the temperature and the pressure of the vapor pipe network, so that the second conversion coefficient is calculated. Specifically, the calculation formula of the second conversion coefficient may be expressed as:
second conversion coefficient = steam coal fold coefficient × steam enthalpy value × standard coal carbon dioxide emission coefficient;
the carbon dioxide emission amount calculation formula of the steam pipe network can be expressed as:
carbon dioxide emissions = steam consumption.
S250, determining a carbon dioxide emission amount of a statistical target according to the carbon dioxide emission amount of production equipment, the carbon dioxide emission amount of power consumption equipment and the carbon dioxide emission amount of a steam pipe network.
After the carbon dioxide emission of the production equipment, the power consumption equipment and the steam pipe network is obtained, the Internet of things system can summarize the carbon dioxide emission of the production equipment, the carbon dioxide emission of the power consumption equipment and the carbon dioxide emission of the steam pipe network, calculate the carbon dioxide emission of the statistical target, and generate a carbon dioxide emission report of the statistical target. The carbon dioxide emission report may include information such as a device type, a device model, a location, and a carbon dioxide emission.
According to the technical scheme, the process operation data of the production equipment are obtained, the carbon dioxide emission of the production equipment is determined according to the digital twin model constructed in advance and the process operation data, and then the carbon dioxide emission of the statistical target is determined according to the carbon dioxide emission of the production equipment. According to the technical scheme, the problem of low accuracy of accounting the carbon dioxide emission by a mass balance method is solved, the fine statistics of the carbon dioxide emission can be realized through a digital twin model constructed based on a neural network model and a mechanism model matched with a production equipment process, and the accuracy of the carbon dioxide emission statistics is improved.
Example III
Fig. 3 is a schematic structural diagram of a digital twin-based carbon dioxide emission statistics device according to a third embodiment of the present invention. The device is configured in an Internet of things system of a statistical target, and the statistical target comprises production equipment. As shown in fig. 3, the apparatus includes:
a process operation data acquisition module 310 for acquiring process operation data of the production facility;
a production facility emission determination module 320, configured to determine a carbon dioxide emission of the production facility according to the pre-constructed digital twin model and the process operation data; the digital twin model is constructed based on a neural network model and a mechanism model matched with the production equipment process;
the statistical target emission amount determination module 330 is configured to determine a statistical target emission amount of carbon dioxide according to the emission amount of carbon dioxide of the production facility.
In this solution, optionally, the apparatus further includes a digital twin model building module configured to:
determining a mechanism model of process matching of production equipment, and acquiring at least one set of historical process operation data;
determining carbon dioxide reference emission matched with each set of historical process operation data according to the mechanism model;
determining sample data according to each set of historical process operation data, the carbon dioxide reference emission amount matched with each set of historical process operation data and the carbon dioxide detection emission amount matched with each set of historical process operation data, which are acquired in advance;
according to the sample data, carrying out iterative training on a pre-constructed neural network, and outputting a neural network model conforming to a preset precision threshold;
and determining a digital twin model matched with the production equipment according to the neural network model and the mechanism model matched with the production equipment process.
On the basis of the above-mentioned scheme, optionally, the production equipment emission amount determining module 320 includes:
the reference emission determining unit is used for determining the reference emission of carbon dioxide according to the process operation data and the mechanism model matched with the process of the production equipment;
and the production equipment emission amount determining unit is used for determining the carbon dioxide emission amount of the production equipment according to the process operation data, the carbon dioxide reference emission amount and the neural network model.
In one possible solution, the statistical objective further includes power consumption equipment and a steam pipe network;
the statistical target emission determination module 330 includes:
an electricity consumption device emission amount determination unit for determining the carbon dioxide emission amount of the electricity consumption device;
the steam pipe network emission determining unit is used for determining the carbon dioxide emission of the steam pipe network;
the statistical target emission amount determining unit is used for determining the statistical target carbon dioxide emission amount according to the carbon dioxide emission amount of the production equipment, the carbon dioxide emission amount of the power consumption equipment and the carbon dioxide emission amount of the steam pipe network.
On the basis of the above scheme, optionally, the electricity consumption device emission amount determining unit is specifically configured to:
acquiring power consumption data of power consumption equipment;
and determining the carbon dioxide emission of the power consumption equipment according to the power consumption data and the first conversion coefficient acquired in advance.
Optionally, the steam pipe network emission determining unit includes:
the conversion coefficient determining subunit is used for acquiring steam consumption data, steam temperature data and steam pressure data of the steam pipe network and determining a second conversion coefficient according to the steam temperature data and the steam pressure data;
and the steam pipe network emission determining subunit is used for determining the carbon dioxide emission of the steam pipe network according to the steam consumption data and the second conversion coefficient.
In one possible solution, the transformation coefficient determining subunit is specifically configured to:
determining a steam enthalpy value according to the steam temperature data and the steam pressure data;
and determining a second conversion coefficient according to the steam enthalpy value, the pre-acquired steam coal folding coefficient and the pre-acquired standard coal carbon dioxide emission coefficient.
The carbon dioxide emission statistics device based on digital twin provided by the embodiment of the invention can execute the carbon dioxide emission statistics method based on digital twin provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of an electronic device 410 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 410 includes at least one processor 411, and a memory, such as a Read Only Memory (ROM) 412, a Random Access Memory (RAM) 413, etc., communicatively connected to the at least one processor 411, wherein the memory stores computer programs executable by the at least one processor, and the processor 411 may perform various suitable actions and processes according to the computer programs stored in the Read Only Memory (ROM) 412 or the computer programs loaded from the storage unit 418 into the Random Access Memory (RAM) 413. In the RAM 413, various programs and data required for the operation of the electronic device 410 may also be stored. The processor 411, the ROM 412, and the RAM 413 are connected to each other through a bus 414. An input/output (I/O) interface 415 is also connected to bus 414.
Various components in the electronic device 410 are connected to the I/O interface 415, including: an input unit 416 such as a keyboard, a mouse, etc.; an output unit 417 such as various types of displays, speakers, and the like; a storage unit 418, such as a magnetic disk, optical disk, or the like; and a communication unit 419 such as a network card, modem, wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The processor 411 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 411 performs the various methods and processes described above, such as a digital twin based carbon dioxide emissions statistical method.
In some embodiments, the digital twinning-based carbon dioxide emissions statistical method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 418. In some embodiments, some or all of the computer program may be loaded and/or installed onto the electronic device 410 via the ROM 412 and/or the communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the digital twinned-based carbon dioxide emission statistics method described above may be performed. Alternatively, in other embodiments, the processor 411 may be configured to perform a digital twinned based carbon dioxide emissions statistical method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. A digital twinning-based carbon dioxide emissions statistical method, the method being performed by an internet of things system of statistical targets including production facilities, the method comprising:
acquiring process operation data of production equipment;
determining the carbon dioxide emission of production equipment according to a pre-constructed digital twin model and process operation data; the digital twin model is constructed based on a neural network model and a mechanism model matched with the production equipment process;
determining a statistical target carbon dioxide emission according to the carbon dioxide emission of the production equipment;
the construction process of the digital twin model comprises the following steps:
determining a mechanism model of process matching of production equipment, and acquiring at least one set of historical process operation data;
determining carbon dioxide reference emission matched with each set of historical process operation data according to the mechanism model;
determining sample data according to each set of historical process operation data, the carbon dioxide reference emission amount matched with each set of historical process operation data and the carbon dioxide detection emission amount matched with each set of historical process operation data, which are acquired in advance;
according to the sample data, carrying out iterative training on a pre-constructed neural network, and outputting a neural network model conforming to a preset precision threshold;
determining a digital twin model matched with production equipment according to the neural network model and a mechanism model matched with the production equipment process;
the method for determining the carbon dioxide emission of the production equipment according to the pre-constructed digital twin model and the process operation data comprises the following steps:
determining a carbon dioxide reference emission amount according to the process operation data and a mechanism model matched with the process of production equipment;
and determining the carbon dioxide emission of the production equipment according to the process operation data, the carbon dioxide reference emission and the neural network model.
2. The method of claim 1, wherein the statistical objective further comprises power consumers and a steam pipe network;
the method for determining the carbon dioxide emission amount of the statistical target according to the carbon dioxide emission amount of the production equipment comprises the following steps:
determining the carbon dioxide emission of power consumption equipment and determining the carbon dioxide emission of a steam pipe network;
and determining the carbon dioxide emission of the statistical target according to the carbon dioxide emission of the production equipment, the carbon dioxide emission of the power consumption equipment and the carbon dioxide emission of the steam pipe network.
3. The method of claim 2, wherein the determining the carbon dioxide emissions of the power consuming device comprises:
acquiring power consumption data of power consumption equipment;
and determining the carbon dioxide emission of the power consumption equipment according to the power consumption data and the first conversion coefficient acquired in advance.
4. The method of claim 2, wherein determining the carbon dioxide emissions of the steam pipe network comprises:
acquiring steam consumption data, steam temperature data and steam pressure data of a steam pipe network, and determining a second conversion coefficient according to the steam temperature data and the steam pressure data;
and determining the carbon dioxide emission of the steam pipe network according to the steam consumption data and the second conversion coefficient.
5. The method of claim 4, wherein determining the second conversion factor based on the steam temperature data and the steam pressure data comprises:
determining a steam enthalpy value according to the steam temperature data and the steam pressure data;
and determining a second conversion coefficient according to the steam enthalpy value, the pre-acquired steam coal folding coefficient and the pre-acquired standard coal carbon dioxide emission coefficient.
6. A digital twinning-based carbon dioxide emission statistics device, characterized in that the device is configured in an internet of things system of a statistics target, the statistics target comprising production equipment, the device comprising:
the process operation data acquisition module is used for acquiring process operation data of the production equipment;
the production equipment emission determining module is used for determining the carbon dioxide emission of the production equipment according to a pre-constructed digital twin model and process operation data; the digital twin model is constructed based on a neural network model and a mechanism model matched with the production equipment process;
the statistical target emission amount determining module is used for determining the statistical target carbon dioxide emission amount according to the carbon dioxide emission amount of the production equipment;
wherein the apparatus further comprises a digital twin model building module for:
determining a mechanism model of process matching of production equipment, and acquiring at least one set of historical process operation data;
determining carbon dioxide reference emission matched with each set of historical process operation data according to the mechanism model;
determining sample data according to each set of historical process operation data, the carbon dioxide reference emission amount matched with each set of historical process operation data and the carbon dioxide detection emission amount matched with each set of historical process operation data, which are acquired in advance;
according to the sample data, carrying out iterative training on a pre-constructed neural network, and outputting a neural network model conforming to a preset precision threshold;
determining a digital twin model matched with production equipment according to the neural network model and a mechanism model matched with the production equipment process;
the production facility discharge determination module includes:
the reference emission determining unit is used for determining the reference emission of carbon dioxide according to the process operation data and the mechanism model matched with the process of the production equipment;
and the production equipment emission amount determining unit is used for determining the carbon dioxide emission amount of the production equipment according to the process operation data, the carbon dioxide reference emission amount and the neural network model.
7. An electronic device, the electronic device comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the digital twinning-based carbon dioxide emission statistical method of any one of claims 1-5.
8. A computer readable storage medium storing computer instructions for causing a processor to implement the digital twinning-based carbon dioxide emission statistical method of any one of claims 1-5 when executed.
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