Method and system for online carbon emission monitoring of coal-fired power generation enterprises

Abstract

This invention provides an online carbon emission monitoring method and system for coal-fired power plants, relating to the field of carbon emission monitoring technology. Based on an edge-cloud system, the invention includes: firstly, collecting real-time monitoring data through a wireless sensor network on the device side and uploading it to an edge computing gateway; secondly, the edge computing gateway determining whether to issue a risk warning for carbon dioxide volume concentration based on the real-time monitoring data; and thirdly, parsing, preprocessing, and uploading the real-time monitoring data to the cloud; and finally, the cloud obtaining the carbon emission calculation result based on the preprocessed data and a preset quantitative model. This fully utilizes the advantages of IoT and cloud computing technologies, compensating for the shortcomings of existing carbon emission accounting methods for coal-fired power plants, and scientifically, efficiently, and accurately achieving real-time monitoring and control of total carbon emissions from coal-fired power plants, providing effective data support for carbon emission verification work.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, specifically to an online carbon emission monitoring method, system, storage medium, and electronic equipment for coal-fired power plants. Background Technology

[0002] As participants in the national carbon market, selecting scientific and reliable carbon emission monitoring methods and providing high-quality carbon emission data are fundamental for coal-fired power plants to implement carbon asset management and participate in the national carbon market.

[0003] Currently, carbon emission accounting mainly employs three methods: the emission factor method, the material balance method, and the measurement method. Among these, the measurement method has fewer intermediate steps, higher accuracy, and traceable results, offering more advantages. In recent years, cloud computing and the Internet of Things (IoT) technologies have become increasingly prevalent in environmental monitoring. Existing technologies directly transmit environmental monitoring data to the cloud, where it is stored, analyzed, and computed. However, the response latency and high bandwidth of cloud-centric processing platforms can reduce the quality of monitoring data, affecting the monitoring efficiency of the system and hindering real-time, efficient, and accurate comprehensive dynamic monitoring.

[0004] Therefore, based on the actual measurement method, providing an online carbon emission monitoring method and system for coal-fired power plants based on Internet of Things and cloud computing technology to achieve real-time carbon emission monitoring, provide highly accessible and real-time data visualization, and improve the quality of carbon accounting management has become an urgent technical problem to be solved. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a method, system, storage medium, and electronic equipment for online carbon emission monitoring in coal-fired power plants, solving the technical problem of the inability to conduct comprehensive dynamic monitoring.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A method for online carbon emission monitoring of coal-fired power plants, based on an edge-cloud system, includes:

[0010] S1. Collect real-time monitoring data through the wireless sensor network on the device and upload it to the edge computing gateway;

[0011] S2. The edge computing gateway determines whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data; and parses, preprocesses, and uploads the real-time monitoring data to the cloud.

[0012] S3. The cloud platform obtains the carbon emission calculation results based on the preprocessed data and a preset quantitative model.

[0013] The quantitative model in S3 refers to:

[0014]

[0015]

[0016]

[0017] in, For the first coal-fired unit Hour Emissions; It means that for any _th Hour, The calculation formulas all hold true;

[0018] This refers to the hourly dry flue gas emissions under standard conditions. It is the cross-sectional area of ​​the chimney outlet; It is the first The first hour The flue gas velocity at the chimney outlet at all times; It is the first The first hour Constant pressure at the chimney outlet; It is the first The first hour Constant temperature of flue gas at the chimney outlet; It is the first The first hour Constantly monitor the humidity of the flue gas at the chimney outlet; It is a unit time interval; For the first The number of monitoring moments included in an hour;

[0019] This represents the hourly average of the carbon dioxide volume concentration. It is the first in the dataset The first hour The volume concentration of carbon dioxide recorded at each time point;

[0020] The constant 44 represents the molecular weight of carbon dioxide gas; the constant 22.4 represents the molar volume of the gas under standard conditions.

[0021] Preferably, the online carbon emission monitoring method for coal-fired power plants includes:

[0022] S4. The cloud platform generates a carbon emission report based on the carbon emission calculation results and presents it to the user in a visual format.

[0023] Preferably, the real-time monitoring data in S1 includes flue gas parameters and carbon dioxide volume concentration, wherein the flue gas parameters include flue gas velocity, temperature, pressure, humidity, and oxygen volume concentration.

[0024] Preferably, in step S2, the edge computing gateway determines whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data, including:

[0025] By combining a pre-defined carbon dioxide concentration monitoring quantification model, the maximum volume concentration of carbon dioxide in the flue gas is obtained; wherein the carbon dioxide concentration monitoring quantification model refers to:

[0026]

[0027] in, These are the volume concentrations of carbon dioxide and oxygen in the flue gas, respectively. The maximum volume concentration of carbon dioxide produced by fuel combustion;

[0028] The maximum volume concentration of carbon dioxide is compared with the corresponding threshold, and an alarm is issued for real-time monitoring data of carbon dioxide volume concentration that exceeds the threshold range.

[0029] Preferably, step S2, which involves parsing, preprocessing, and uploading real-time monitoring data to the cloud, includes:

[0030] The calculation period is one hour.

[0031] The monitoring datasets for all calculation cycles are calibrated to remove faults and out-of-limit data, and hourly preprocessed datasets are obtained and uploaded to the cloud.

[0032] Preferably, in step S4, the daily and annual carbon emission summaries are obtained based on the hourly carbon emission data of the coal-fired unit, and the calculation formula is as follows:

[0033]

[0034]

[0035] in, Daily carbon emissions from coal-fired power generating units; It means that for any _th sky, The calculation formulas all hold true; The annual carbon emissions of coal-fired power generating units; It means that for any _th Year, All the calculation formulas are valid.

[0036] An online carbon emission monitoring system for coal-fired power plants, based on an edge-cloud system, includes the following methods:

[0037] The data acquisition module is used to collect real-time monitoring data through the wireless sensor network on the device and upload it to the edge computing gateway;

[0038] The early warning and preprocessing module is used by the edge computing gateway to determine whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data; and to parse, preprocess, and upload the real-time monitoring data to the cloud.

[0039] The calculation module is used by the cloud to obtain the carbon emission calculation results based on the preprocessed data and a preset quantitative model.

[0040] A storage medium storing a computer program for online carbon emission monitoring of coal-fired power plants, wherein the computer program causes a computer to execute the online carbon emission monitoring method for coal-fired power plants as described above.

[0041] An electronic device, comprising:

[0042] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing online carbon emission monitoring of coal-fired power plants as described above.

[0043] (III) Beneficial Effects

[0044] This invention provides a method, system, storage medium, and electronic device for online carbon emission monitoring in coal-fired power plants. Compared with existing technologies, it has the following advantages:

[0045] This invention, based on an edge-cloud system, includes: first, collecting real-time monitoring data through a wireless sensor network on the device side and uploading it to an edge computing gateway; second, the edge computing gateway determining whether to issue a risk warning for carbon dioxide volume concentration based on the real-time monitoring data; and third, parsing, preprocessing, and uploading the real-time monitoring data to the cloud; and finally, the cloud obtaining carbon emission calculation results based on the preprocessed data and a preset quantitative model. This fully leverages the advantages of IoT and cloud computing technologies, compensating for the shortcomings of existing carbon emission accounting methods for coal-fired power plants, and scientifically, efficiently, and accurately achieving real-time monitoring and control of total carbon emissions from coal-fired power plants, providing effective data support for carbon emission verification work. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A block diagram illustrating an online carbon emission monitoring method for coal-fired power plants, provided by an embodiment of the present invention;

[0048] Figure 2 A block diagram of another online carbon emission monitoring method for coal-fired power plants provided by an embodiment of the present invention.

[0049] Figure 3 A structural block diagram of an online carbon emission monitoring system for coal-fired power plants provided in an embodiment of the present invention;

[0050] Figure 4 This is a structural block diagram of another online carbon emission monitoring system for coal-fired power plants provided in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] This application provides a method, system, storage medium, and electronic device for online carbon emission monitoring in coal-fired power plants, which solves the technical problem of the inability to conduct comprehensive dynamic monitoring.

[0053] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0054] As mentioned in the background section, existing online monitoring technologies transmit monitoring data directly to the cloud, where it is stored, analyzed, and processed. The response latency and high bandwidth of this cloud-centric processing platform can reduce the quality of the monitoring data, impacting the system's efficiency and hindering real-time, efficient, and accurate comprehensive dynamic monitoring.

[0055] The purpose of this invention is to provide an online carbon emission monitoring method and system for coal-fired power plants based on the Internet of Things and cloud computing, so as to make up for the shortcomings of existing carbon emission accounting algorithms, and realize continuous monitoring, timeliness and accuracy of carbon emission measurement. This invention provides highly accessible and real-time data visualization, and provides guidance and optimization suggestions for carbon emission management.

[0056] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0057] Example:

[0058] like Figure 1 As shown, this embodiment of the invention provides an online carbon emission monitoring method for coal-fired power plants, based on an edge-cloud system. The method includes:

[0059] S1. Collect real-time monitoring data through the wireless sensor network on the device and upload it to the edge computing gateway;

[0060] S2. The edge computing gateway determines whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data; and parses, preprocesses, and uploads the real-time monitoring data to the cloud.

[0061] S3. The cloud platform obtains the carbon emission calculation results based on the preprocessed data and a preset quantitative model.

[0062] In an alternative embodiment, such as Figure 2 As shown, it also includes:

[0063] S4. The cloud platform generates a carbon emission report based on the carbon emission calculation results and presents it to the user in a visual format.

[0064] This invention fully leverages the advantages of IoT and cloud computing technologies, making up for the shortcomings of existing carbon emission accounting methods for coal-fired power plants. It enables real-time monitoring and control of total carbon emissions from coal-fired power plants in a scientific, efficient, and accurate manner, providing effective data support for carbon emission verification.

[0065] The following will detail each step of the above solution:

[0066] In step S1, real-time monitoring data is collected through the wireless sensor network on the device and uploaded to the edge computing gateway.

[0067] Currently, coal-fired power plants have generally installed continuous emission monitoring systems (CEMS). However, the CEMS systems currently installed still have some shortcomings. They can only collect information such as the concentrations of gaseous pollutants NOx and SO2, particulate matter concentration, and flue gas parameters (temperature, pressure, flow rate or volume, humidity, oxygen content, etc.), and are not equipped with a separate CO2 monitoring unit.

[0068] In contrast, the wireless sensor network in this step consists of the flue gas parameter measurement and monitoring unit of the continuous emission monitoring system and the newly added carbon dioxide monitoring unit connected by wireless communication technology, enabling real-time monitoring and acquisition of flue gas parameters (flow rate, temperature, pressure, humidity, oxygen concentration) and carbon dioxide concentration data.

[0069] In step S2, the edge computing gateway determines whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data; and parses, preprocesses, and uploads the real-time monitoring data to the cloud.

[0070] This step can be explained in two parts, among which,

[0071] Part 1: The edge computing gateway determines whether to issue a risk warning for carbon dioxide emission concentration based on real-time monitoring data, including:

[0072] By combining a pre-defined carbon dioxide concentration monitoring quantification model, the maximum volume fraction of carbon dioxide in the flue gas is obtained; wherein the carbon dioxide concentration monitoring quantification model refers to:

[0073]

[0074] in, The volume concentrations (%) of carbon dioxide and oxygen in the flue gas are respectively. The maximum volume fraction (%) of carbon dioxide produced by fuel combustion.

[0075] The system compares the maximum volume fraction of carbon dioxide with the corresponding threshold, and issues an alarm for online monitoring data of carbon dioxide concentration exceeding the threshold range. In practice, different warning thresholds can be set according to different fuel types.

[0076] It is easy to understand that by using edge computing gateways to parse and preprocess large amounts of monitoring data and provide early warnings of carbon emission concentrations, the amount of data processing in the cloud is reduced, the computing pressure in the cloud is alleviated, and a foundation is provided for subsequent data analysis and other operations in the cloud, thereby improving data processing speed and improving the efficiency and quality of carbon emission monitoring.

[0077] Part Two: The process of parsing, preprocessing, and uploading real-time monitoring data to the cloud in S2 includes:

[0078] The calculation period is one hour (1h).

[0079] The monitoring datasets for all calculation cycles are calibrated to remove faults and out-of-limit data, and hourly preprocessed datasets are obtained and uploaded to the cloud.

[0080] The reason for using 1 hour as the calculation period is that, in this embodiment of the invention, it is considered that during normal operation of the unit, as long as the equipment does not malfunction, the system operating status will not change much within 1 hour. Therefore, 1 hour is used as the calculation period. Secondly, considering that the data acquisition frequency of on-site carbon emission monitoring parameters can be as fast as seconds and as slow as about 1 minute, there will be 60 to 3600 records within the 1-hour calculation period, forming an hourly monitoring dataset of moderate data volume.

[0081] It is easy to understand that edge computing gateways preprocess real-time carbon emission data and share and exchange data with the cloud, improving data utilization and transmission efficiency, reducing the pressure on cloud data processing, avoiding the huge bandwidth and network resources required for long-distance, large-volume data transmission, and alleviating network bandwidth pressure.

[0082] In step S3, the cloud platform obtains the carbon emission calculation results based on the preprocessed data and a preset quantitative model.

[0083] Among them, the quantitative model refers to:

[0084] (1) Hourly carbon dioxide emissions from coal-fired power units:

[0085]

[0086] in, For the first coal-fired unit Hour Emissions (t); It means that for any _th Hour, The calculation formulas all hold true;

[0087] (2) Hourly dry flue gas emissions under standard conditions:

[0088]

[0089] in, The hourly dry flue gas emission (ten thousand m³) under standard conditions. It is the cross-sectional area of ​​the chimney outlet (m²) 2 ); It is the first The first hour The flue gas velocity at the chimney outlet at any given time (m / s); It is the first The first hour Pressure at the chimney outlet at any given time (Pa); It is the first The first hour Temperature of flue gas at the chimney outlet at any given time (°C); It is the first The first hour Humidity of flue gas at chimney outlet at any given time (%) It is a unit time interval (h); For the first The number of monitoring moments included in an hour;

[0090] (3) Hourly average of carbon dioxide volume fraction:

[0091] The hourly average (%) of carbon dioxide volume fraction; (%); n is the number of records in the dataset;

[0092] The constant 44 is the molecular weight of carbon dioxide gas (g / mol); the constant 22.4 is the molar volume of the gas under standard conditions (L / mol).

[0093] Specifically, in this step, in addition to calculating the hourly carbon emission data of the coal-fired power unit, the daily and annual carbon emission summaries are also obtained through simple summation. The calculation formula is as follows:

[0094]

[0095]

[0096] in, The daily carbon emissions (t) of coal-fired power generating units. It means that for any _th sky, The calculation formulas all hold true; The annual carbon emissions (t) of a coal-fired power generation unit. It means that for any _th Year, All the calculation formulas are valid.

[0097] In step S4, the cloud generates a carbon emission report based on the carbon emission calculation results and presents it to the user in a visual format.

[0098] By leveraging cloud-based analysis and visualization of carbon monitoring data, users can analyze carbon emissions and trends, enabling real-time monitoring of dynamic changes in carbon emissions. This provides a solid basis for carbon monitoring management and accounting, significantly improving the quality of carbon accounting management, reducing labor costs, and supporting coal-fired power plants in developing more precise carbon trading schemes.

[0099] like Figure 3 As shown, this embodiment of the invention provides an online carbon emission monitoring system for coal-fired power plants, based on an edge-cloud system. The method includes:

[0100] The data acquisition module is used to collect real-time monitoring data through the wireless sensor network on the device and upload it to the edge computing gateway;

[0101] The early warning and preprocessing module is used by the edge computing gateway to determine whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data; and to parse, preprocess, and upload the real-time monitoring data to the cloud.

[0102] The calculation module is used by the cloud to obtain the carbon emission calculation results based on the preprocessed data and a preset quantitative model.

[0103] In an alternative embodiment, such as Figure 4 As shown, it also includes:

[0104] The visualization module is used to generate carbon emission reports based on the carbon emission calculation results in the cloud and present them to the user in a visual format.

[0105] This invention provides a storage medium storing a computer program for online carbon emission monitoring of coal-fired power plants, wherein the computer program causes a computer to execute the online carbon emission monitoring method for coal-fired power plants as described above.

[0106] This invention provides an electronic device, comprising:

[0107] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing online carbon emission monitoring of coal-fired power plants as described above.

[0108] It is understood that the online carbon emission monitoring system, storage medium and electronic equipment for coal-fired power plants provided in the embodiments of the present invention correspond to the online carbon emission monitoring method for coal-fired power plants provided in the embodiments of the present invention. The explanation, examples and beneficial effects of the relevant contents can be referred to the corresponding parts of the online carbon emission monitoring method for coal-fired power plants, and will not be repeated here.

[0109] In summary, compared with existing technologies, it has the following beneficial effects:

[0110] 1. The embodiments of the present invention fully utilize the advantages of Internet of Things and cloud computing technologies, which can make up for the shortcomings of existing carbon emission accounting methods for coal-fired power plants, and scientifically, efficiently and accurately realize the real-time monitoring and control of the total carbon emissions of coal-fired power plants, providing effective data support for carbon emission verification work.

[0111] 2. Based on the edge computing gateway, a large amount of monitoring data is parsed and preprocessed, and carbon emission concentration is given early warning. This reduces the amount of data processing in the cloud, alleviates the computing pressure in the cloud, and provides a foundation for subsequent data analysis and other operations in the cloud. It also improves the data processing speed and helps to improve the monitoring efficiency and data quality of carbon emissions.

[0112] 3. Edge computing gateways preprocess real-time carbon emission data and share and exchange data with the cloud, improving data utilization and transmission efficiency, reducing the pressure on cloud data processing, avoiding the huge bandwidth and network resources required for long-distance, large-volume data transmission, and alleviating network bandwidth pressure.

[0113] 4. Based on cloud-based analysis and visualization of carbon monitoring data, it helps users analyze carbon emissions and trends, enabling real-time perception of dynamic changes in carbon emissions. This provides effective evidence for carbon monitoring management and accounting, thereby greatly improving the quality of carbon accounting management, reducing labor costs, and supporting coal-fired power generation companies in developing more accurate carbon trading schemes.

[0114] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0115] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for online carbon emission monitoring in coal-fired power plants, characterized in that, Based on an edge-cloud system, this method includes: S1. Collect real-time monitoring data through the wireless sensor network on the device and upload it to the edge computing gateway; S2. The edge computing gateway determines whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data; and parses, preprocesses, and uploads the real-time monitoring data to the cloud. S3. The cloud platform obtains the carbon emission calculation results based on the preprocessed data and a preset quantitative model. The quantitative model in S3 refers to: in, For the first coal-fired unit Hour Emissions; It means that for any _th Hour, The calculation formulas all hold true; This refers to the hourly dry flue gas emissions under standard conditions. It is the cross-sectional area of ​​the chimney outlet; It is the first The first hour The flue gas velocity at the chimney outlet at all times; It is the first The first hour Constant pressure at the chimney outlet; It is the first The first hour Constant temperature of flue gas at the chimney outlet; It is the first The first hour Constantly monitor the humidity of the flue gas at the chimney outlet; It is a unit time interval; For the first The number of monitoring moments included in an hour; This represents the hourly average of the carbon dioxide volume concentration. It is the first in the dataset The first hour The volume concentration of carbon dioxide recorded at each time point; The constant 44 represents the molecular weight of carbon dioxide gas; the constant 22.4 represents the molar volume of the gas under standard conditions.

2. The online carbon emission monitoring method for coal-fired power plants as described in claim 1, characterized in that, include: S4. The cloud platform generates a carbon emission report based on the carbon emission calculation results and presents it to the user in a visual format.

3. The online carbon emission monitoring method for coal-fired power plants as described in claim 2, characterized in that, The real-time monitoring data in S1 includes flue gas parameters and carbon dioxide volume concentration, wherein the flue gas parameters include flue gas velocity, temperature, pressure, humidity, and oxygen volume concentration.

4. The online carbon emission monitoring method for coal-fired power plants as described in claim 3, characterized in that, The edge computing gateway in S2 determines whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data, including: By combining a pre-defined carbon dioxide concentration monitoring quantification model, the maximum volume concentration of carbon dioxide in the flue gas is obtained; wherein the carbon dioxide concentration monitoring quantification model refers to: in, These are the volume concentrations of carbon dioxide and oxygen in the flue gas, respectively. The maximum volume concentration of carbon dioxide produced by fuel combustion; The maximum volume concentration of carbon dioxide is compared with the corresponding threshold, and an alarm is issued for real-time monitoring data of carbon dioxide volume concentration that exceeds the threshold range.

5. The online carbon emission monitoring method for coal-fired power plants as described in claim 4, characterized in that, The process of parsing, preprocessing, and uploading real-time monitoring data to the cloud in S2 includes: The calculation period is one hour. The monitoring datasets for all calculation cycles are calibrated to remove faults and out-of-limit data, and hourly preprocessed datasets are obtained and uploaded to the cloud.

6. The online carbon emission monitoring method for coal-fired power plants as described in claim 5, characterized in that, In step S4, the daily and annual carbon emissions are also calculated based on the hourly carbon emission data of the coal-fired power units. The calculation formula is as follows: in, Daily carbon emissions from coal-fired power generating units; It means that for any _th sky, The calculation formulas all hold true; The annual carbon emissions of coal-fired power generating units; It means that for any _th Year, All the calculation formulas are valid.

7. An online carbon emission monitoring system for coal-fired power plants, characterized in that, Based on an edge-cloud system, this system is used to execute the online carbon emission monitoring method for coal-fired power plants as described in claim 1, including: The data acquisition module is used to collect real-time monitoring data through the wireless sensor network on the device and upload it to the edge computing gateway; The early warning and preprocessing module is used by the edge computing gateway to determine whether to issue a risk warning for carbon dioxide volume concentration based on real-time monitoring data; and to parse, preprocess, and upload the real-time monitoring data to the cloud. The calculation module is used by the cloud to obtain the carbon emission calculation results based on the preprocessed data and a preset quantitative model.

8. A storage medium, characterized in that, It stores a computer program for online carbon emission monitoring of coal-fired power plants, wherein the computer program causes the computer to execute the online carbon emission monitoring method for coal-fired power plants as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, include: One or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing online carbon emission monitoring of coal-fired power plants as described in any one of claims 1 to 6.

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