Factory carbon emission monitoring method and system based on electricity-carbon calculation model

Abstract

The invention discloses a factory carbon emission monitoring method and system based on an electricity-carbon calculation model, relates to the technical field of carbon emission monitoring and energy management, and is used for solving the problem that the real-time performance and accuracy of carbon emission monitoring are reduced due to the fact that carbon emission results are mostly presented as coarse-grained indexes. An initial carbon flow distribution matrix is constructed by collecting carbon concentration and electric energy consumption data of distributed energy access points in a plant, a carbon flow model is generated based on a digital twinborn body, a carbon flow weight is dynamically corrected by combining a Bayesian inference algorithm, the total carbon emission amount of the plant is calculated, and a monitoring result is output. According to the method, the spatial precision and the real-time correction capability of carbon emission tracking can be improved, early warning of the carbon emission trend of the key node is realized, and data support and decision basis are provided for a low-carbon operation strategy of a factory.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring and energy management, and more specifically, to a plant carbon emission monitoring method and system based on an electricity-carbon calculation model. Background Art

[0002] Plant carbon emissions monitoring technology quantifies the relationship between electricity consumption and carbon emissions, and is used to assess plant carbon emissions and optimize energy use. Monitoring methods based on electricity-carbon calculation models are crucial for improving data accuracy. However, existing technologies have limitations in distributed energy access scenarios, particularly regarding the accumulation of baseline errors in carbon flow tracking and the ability to perform real-time corrections. This hinders their effectiveness in complex plant environments.

[0003] The existing technology has the following deficiencies:

[0004] At present, current systems generally lack the ability to model the sources, paths and flows of carbon emissions, and lack a refined tracking mechanism for the flow characteristics of carbon flows between different functional areas and equipment nodes in the factory. As a result, carbon emission results are mostly presented in the form of coarse-grained indicators, which reduces the real-time and accuracy of carbon emission monitoring and increases the response lag in the formulation of low-carbon regulation strategies. Therefore, a factory carbon emission monitoring method and system based on the electricity-carbon calculation model is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a plant carbon emission monitoring method and system based on the electricity-carbon calculation model. By designing a digital twin calibration device for carbon flow tracking error compensation, a carbon concentration sensor array is deployed at the physical layer, and the Bayesian inference of the digital twin is combined to dynamically adjust the carbon flow distribution weight, thereby effectively solving the problem of carbon flow tracking benchmark error accumulation in distributed energy access scenarios and improving monitoring accuracy and adaptability.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring carbon emissions from a plant area based on an electricity-carbon calculation model, comprising the following steps:

[0008] Step S1: Collecting real-time carbon concentration data of distributed energy access points within the plant, obtaining the carbon concentration value of each access point using a carbon concentration sensor array, and transmitting the collected data to a central processing unit;

[0009] Step S2: Synchronously collect the electricity consumption data within the plant, integrate the carbon concentration data and the electricity consumption data to construct an initial carbon flow distribution matrix, and use the digital twin to generate a carbon flow distribution model of the virtual plant;

[0010] Step S3: Analyze the carbon flow fluctuation characteristics caused by the access of distributed energy resources, dynamically adjust the carbon flow allocation weights through the Bayesian inference algorithm, and generate a modified carbon flow distribution matrix;

[0011] Step S4: Calculate the overall carbon emissions of the plant area based on the revised carbon flow distribution matrix and output the monitoring results.

[0012] In a preferred embodiment, in step S1, the carbon concentration sensor array is composed of multiple high-sensitivity gas sensors, each sensor is installed in the exhaust duct or nearby area of ​​the distributed energy access point, the distance between the sensors is determined according to the plant layout and carbon diffusion characteristics, and the sensor array uses a wireless communication module to establish a connection with the central processing unit.

[0013] In a preferred embodiment, in step S1, the installation position of the carbon concentration sensor array meets the following conditions:

[0014] The sensor installation height is set between 1.5 meters and 3 meters from the ground, the sensor installation direction is facing the carbon emission source, and the sensor array coverage range meets the requirement of no blind spots in the detection area.

[0015] In a preferred embodiment, in step S2, the process of constructing the initial carbon flow distribution matrix includes the following steps:

[0016] Divide the plant into several functional zones, each zone corresponding to a carbon flow node;

[0017] The initial carbon flow intensity value of each node is calculated through correlation analysis between electricity consumption data and carbon concentration data; the carbon flow intensity values ​​of all nodes are summarized to form an initial carbon flow distribution matrix.

[0018] In a preferred embodiment, in step S2, the process of generating the digital twin includes the following steps:

[0019] Use 3D modeling software to build a physical space model of the factory area;

[0020] The collected carbon concentration data and electricity consumption data are input into the model to generate the carbon flow distribution status of the virtual plant;

[0021] The digital twin is kept synchronized with the actual factory through a real-time data update mechanism.

[0022] In a preferred embodiment, in step S3, the application process of the Bayesian inference algorithm includes the following steps:

[0023] Define the prior probability distribution function of the carbon flow allocation weight; calculate the posterior probability distribution function based on the real-time collected carbon concentration data and electricity consumption data;

[0024] The carbon flow distribution weights are dynamically adjusted using the posterior probability distribution function to generate a revised carbon flow distribution matrix.

[0025] In a preferred embodiment, in step S3, the analysis process of the carbon flow fluctuation characteristics includes the following steps:

[0026] Collect real-time operating parameters of distributed energy access points, including power generation, fuel type, and combustion efficiency;

[0027] Combined with the changing trends of these parameters, their impact on carbon flow distribution is evaluated; the main characteristics of carbon flow fluctuations are identified through time series analysis methods.

[0028] In a preferred embodiment, in step S4, the calculation process of the overall carbon emissions of the plant includes the following steps:

[0029] Multiply the carbon flow intensity value of each node in the modified carbon flow distribution matrix by its corresponding weight coefficient;

[0030] The weighted carbon flow intensity values ​​of all nodes are summed up to obtain the overall carbon emissions of the plant.

[0031] The plant carbon emission monitoring system based on the electricity-carbon calculation model includes a data acquisition module, a digital twin module, a carbon flow correction module, and a carbon emission calculation module;

[0032] The data acquisition module is used to collect carbon concentration data and power consumption data of distributed energy access points within the plant area and transmit the collected data to the central processing unit;

[0033] The digital twin module is used to generate a carbon flow distribution model of the virtual plant based on the collected data, and to keep the model synchronized with the actual plant through a real-time data update mechanism;

[0034] The carbon flow correction module is used to analyze the carbon flow fluctuation characteristics caused by the access of distributed energy resources, and dynamically adjust the carbon flow allocation weights through the Bayesian inference algorithm to generate a corrected carbon flow distribution matrix;

[0035] The carbon emission calculation module is used to calculate the overall carbon emissions of the plant based on the revised carbon flow distribution matrix and output the monitoring results.

[0036] The plant carbon emission monitoring system based on the electricity-carbon calculation model includes a data acquisition module, a carbon concentration sensor array, an electricity metering device, and a wireless communication unit;

[0037] The carbon concentration sensor array is responsible for collecting carbon concentration data, the electric energy metering device is responsible for collecting electric energy consumption data, and the wireless communication unit is responsible for transmitting the collected data to the central processing unit.

[0038] The technical effects and advantages of the present invention are as follows:

[0039] The present invention collects real-time carbon concentration data of distributed energy access points within the plant, obtains the carbon concentration value of each access point using a carbon concentration sensor array, synchronously collects electricity consumption data within the plant, and constructs an initial carbon flow distribution matrix by integrating the carbon concentration data and the electricity consumption data. The digital twin is used to generate a carbon flow distribution model of the virtual plant, analyzes the carbon flow fluctuation characteristics caused by distributed energy access, dynamically adjusts the carbon flow allocation weights through the Bayesian inference algorithm, generates a corrected carbon flow distribution matrix, calculates the overall carbon emissions of the plant, and outputs the monitoring results. This improves the spatial accuracy of carbon emission tracking, enhances dynamic adjustment and real-time correction capabilities, realizes early warning of carbon emission trends of key energy nodes, and provides data support and decision-making basis for the low-carbon operation strategy of the plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the module structure of the plant carbon emission monitoring system based on the electricity-carbon calculation model of the present invention.

[0041] Figure 2 Schematic diagram of the method flow of the plant carbon emission monitoring method based on the electricity-carbon calculation model of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Example 1

[0044] The present invention provides a plant carbon emission monitoring method and system based on an electricity-carbon calculation model, which realizes accurate monitoring of plant carbon emissions through the collaborative work of a data acquisition module, a digital twin module, a carbon flow correction module and a carbon emission calculation module.

[0045] The following combination Figure 1 and Figure 2The specific embodiments of the present invention are described in detail with reference to the accompanying drawings and the component numbers.

[0046] exist Figure 1 The module structure diagram of the entire system is shown in the figure, in which the data acquisition module is responsible for obtaining real-time carbon concentration data and electricity consumption data from distributed energy access points within the plant.

[0047] The module includes a carbon concentration sensor array, an electric energy metering device, and a wireless communication unit.

[0048] The carbon concentration sensor array consists of multiple highly sensitive gas sensors. Each sensor is installed in the exhaust duct or nearby area of ​​the distributed energy access point. The installation height is set between 1.5 meters and 3 meters from the ground to avoid the impact of ground obstacles on the airflow. At the same time, the sensor is ensured to be oriented towards the carbon emission source to improve the accuracy of data collection.

[0049] The distance between sensors is determined based on the plant layout and carbon diffusion characteristics, and the coverage must meet the requirement of no blind spots within the detection area.

[0050] The electric energy metering device is used to measure the electric energy consumption of each functional area within the factory.

[0051] The wireless communication unit transmits the data collected by the sensor array and the energy metering device to the central processing unit, thereby reducing wiring complexity and improving system flexibility.

[0052] The digital twin module receives data from the data acquisition module and generates a carbon flow distribution model of the virtual plant.

[0053] This module includes a 3D modeling unit, a data fusion unit and a real-time updating unit.

[0054] The 3D modeling unit uses 3D modeling software to construct a physical space model of the plant area. The model includes the functional zoning information of the plant area and the carbon flow node locations corresponding to each zone.

[0055] The data fusion unit inputs the collected carbon concentration data and electricity consumption data into the model, calculates the initial carbon flow intensity value of each node through correlation analysis, and then summarizes them to form an initial carbon flow distribution matrix.

[0056] The real-time update unit keeps the digital twin synchronized with the actual factory by continuously receiving the latest data, ensuring that the model can dynamically reflect the actual operating status of the factory.

[0057] The output of the digital twin module is the carbon flow distribution status of the virtual plant, which provides a basis for subsequent carbon flow correction and total amount calculation.

[0058] The carbon flow correction module further analyzes the carbon flow fluctuation characteristics caused by the access of distributed energy resources, and dynamically adjusts the carbon flow allocation weight through the Bayesian inference algorithm.

[0059] This module includes a fluctuation analysis unit and a weight adjustment unit.

[0060] The fluctuation analysis unit first collects the real-time operating parameters of the distributed energy access points, such as power generation power, fuel type and combustion efficiency, and evaluates their impact on carbon flow distribution based on the changing trends of these parameters.

[0061] Subsequently, the main characteristics of carbon flow fluctuations are identified through time series analysis methods to provide a basis for weight adjustment.

[0062] The weight adjustment unit defines the prior probability distribution function of the carbon flow allocation weight, and calculates the posterior probability distribution function based on the real-time collected carbon concentration data and electricity consumption data. Finally, the posterior probability distribution function is used to dynamically adjust the carbon flow allocation weight to generate a revised carbon flow distribution matrix.

[0063] This process effectively solves the problem of accumulated carbon flow tracking benchmark errors in distributed energy access scenarios.

[0064] The carbon emission calculation module calculates the overall carbon emissions of the plant based on the revised carbon flow distribution matrix.

[0065] The module includes a matrix processing unit and a total amount calculation unit. The matrix processing unit performs weighted processing on the modified carbon flow distribution matrix, that is, multiplying the carbon flow intensity value of each node by its corresponding weight coefficient.

[0066] The total calculation unit sums the weighted carbon flow intensity values ​​of all nodes to obtain the overall carbon emissions of the plant and outputs the monitoring results. This process ensures the accuracy and reliability of the monitoring results.

[0067] In the entire system, the data acquisition module and the digital twin module realize data transmission through the wireless communication unit. The output result of the digital twin module serves as the input of the carbon flow correction module, and the output result of the carbon flow correction module is passed to the carbon emission calculation module for final processing.

[0068] The connections between the various modules are tight and logically clear, forming a complete factory carbon emission monitoring system.

[0069] In actual application scenarios, assume that there are multiple distributed energy access points in an industrial plant, including photovoltaic power stations, wind turbines, and natural gas boilers.

[0070] The carbon concentration sensor array in the data acquisition module is installed near the exhaust ducts of these access points, and the electricity metering devices are distributed in the distribution cabinets of each functional area.

[0071] When the system is started, the carbon concentration sensor array and the electric energy metering device begin to collect carbon concentration data and electric energy consumption data in real time and transmit them to the central processing unit through the wireless communication unit.

[0072] The logic for acquiring carbon concentration data is to map the carbon concentration data to the spatial nodes of the virtual model through the three-dimensional twin mapping function by setting the sampling frequency and data expression format through the acquisition equipment and layout:

[0073]

[0074] Where t represents the current acquisition time, N is the total number of carbon concentration sensors deployed in the current system, C(t) represents the carbon dioxide concentration value collected by the i-th sensor at time t, and is the multidimensional observation vector composed of the carbon concentration data of all sensors at time t, with dimensions N and R. 3 The spatial field is a three-dimensional scalar field used to construct the initial carbon flow matrix, reflecting the initial estimation of the carbon emission source intensity at different spatial points;

[0075] It should be noted that the spatial field distribution formed after the carbon concentration data is mapped to the virtual space nodes through the three-dimensional twin mapping function is only used as a reference input for the initial carbon flow matrix. Its specific interpolation method, spatial constraint model and time synchronization mechanism can be flexibly adjusted according to the actual deployment environment and will not be elaborated here.

[0076] The logic for acquiring power consumption data is to use a multi-circuit power metering device, set the sampling mechanism and parameter expression, and map all circuit power data to functional areas through the regional mapping function to obtain power consumption data;

[0077] It should be noted that the spatial topology model, device identification strategy, and data deduplication mechanism involved in the mapping process depend on the platform architecture and the specific conditions of the deployment site, and will not be elaborated here.

[0078] After receiving the data, the digital twin module uses the three-dimensional modeling unit to build a physical space model of the plant, and generates the initial carbon flow distribution matrix of the virtual plant through the data fusion unit.

[0079] Specifically, the data fusion unit maps the carbon concentration data and the electricity consumption data to the carbon flow node and constructs the initial carbon flow intensity matrix through the function. The specific function is expressed as:

[0080] F i =α×C i +β×E i ;

[0081] Where, F i is the carbon flow intensity of the i-th node, C i is the carbon concentration data, E i is the electricity consumption data, α and β are the weight coefficients of carbon concentration data and electricity consumption data, respectively, indicating the relative importance of carbon concentration and electricity consumption data in constructing carbon flow intensity.

[0082] The real-time update unit continuously receives the latest data to keep the model synchronized with the actual plant.

[0083] Specifically, carbon concentration data and electricity consumption data are periodically received and the model is updated to ensure synchronization with the real-time operating status of the plant.

[0084] It should be noted that the periodicity set by the real-time update unit is determined by the experimenters based on the sensor sampling capability of the system and the change pattern of the plant operation load to set the cycle length and cycle span, which will not be elaborated here.

[0085] The carbon flow correction module analyzes the changes in the operating parameters of distributed energy access points, dynamically adjusts the carbon flow allocation weights through the Bayesian inference algorithm, and generates a corrected carbon flow distribution matrix.

[0086] The carbon flow correction module is used to dynamically correct the carbon flow under the background of distributed energy access fluctuations, including the following steps:

[0087] The fluctuation analysis unit is used to collect the power generation, fuel type, and combustion efficiency of the access point and identify the fluctuation characteristics using time series analysis;

[0088] The weight adjustment unit is used to construct the prior probability distribution π(w i ), through the Bayesian inference algorithm:

[0089] π(w i ∣D t )∝L(D t ∣w i )·π(w i );

[0090] Where D t Represents the observed data at time t, calculates the posterior distribution and updates the node weight w i , generate the modified carbon flow matrix F * =[w1F1,w2F2,w n F n ];

[0091] Finally, the carbon emission calculation module calculates the overall carbon emissions of the plant based on the revised matrix and displays the results on the screen of the monitoring center for reference by managers.

[0092] It can be seen from the above embodiments that the plant carbon emission monitoring system provided by the present invention has a modular design feature, is easy to expand and maintain, and is suitable for the needs of efficient and accurate carbon emission monitoring in complex plant environments.

[0093] Each module in the system achieves comprehensive monitoring of the factory's carbon emissions through clear connection relationships and collaboration mechanisms, providing factory managers with a scientific basis for decision-making.

[0094] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below in combination with a specific application scenario.

[0095] In an industrial plant, there are multiple distributed energy access points, including photovoltaic power stations, wind turbines and natural gas boilers.

[0096] Plant managers hope to optimize energy efficiency by accurately monitoring carbon emissions and ensure the real-time and accuracy of carbon emissions data.

[0097] To this end, operations are carried out based on the system and method provided by the present invention.

[0098] First, the carbon concentration sensor array and the electric energy metering device in the data acquisition module are started.

[0099] The carbon concentration sensor array is installed near the exhaust duct of the distributed energy access point. Its installation height is set to 2 meters from the ground to avoid the impact of ground obstacles on the airflow, while ensuring that the sensor is facing the carbon emission source.

[0100] The distance between sensors is determined according to the plant layout and carbon diffusion characteristics, and the coverage range meets the requirement of no blind spots in the detection area.

[0101] The electricity metering devices are distributed in the distribution cabinets of each functional area and are used to measure the electricity consumption of each functional area within the factory.

[0102] The wireless communication unit transmits real-time data collected by the sensor array and energy metering device to the central processing unit, thereby reducing wiring complexity and improving system flexibility.

[0103] Subsequently, the digital twin module receives data from the data acquisition module and generates a carbon flow distribution model of the virtual plant.

[0104] The 3D modeling unit uses 3D modeling software to construct a physical space model of the plant area. The model includes the functional zoning information of the plant area and the carbon flow node locations corresponding to each zone.

[0105] The data fusion unit inputs the collected carbon concentration data and electricity consumption data into the model, calculates the initial carbon flow intensity value of each node through correlation analysis, and then summarizes them to form an initial carbon flow distribution matrix.

[0106] The real-time update unit continuously receives the latest data, keeps the digital twin synchronized with the actual factory, and ensures that the model can dynamically reflect the actual operating status of the factory.

[0107] Next, the carbon flow correction module analyzes the carbon flow fluctuation characteristics caused by the access of distributed energy resources, and dynamically adjusts the carbon flow allocation weight through the Bayesian inference algorithm.

[0108] The fluctuation analysis unit collects real-time operating parameters of distributed energy access points, such as the power generation of photovoltaic power stations, the fuel type and combustion efficiency of natural gas boilers, etc., and evaluates their impact on carbon flow distribution based on the changing trends of these parameters.

[0109] Time series analysis is used to identify the key characteristics of carbon flow fluctuations, providing a basis for weight adjustment. The weight adjustment unit defines a priori probability distribution function for carbon flow allocation weights and calculates a posterior probability distribution function based on real-time carbon concentration and electricity consumption data. Ultimately, the posterior probability distribution function is used to dynamically adjust the carbon flow allocation weights to generate a revised carbon flow distribution matrix.

[0110] This process effectively solves the problem of accumulated carbon flow tracking benchmark errors in distributed energy access scenarios.

[0111] Finally, the carbon emission calculation module calculates the overall carbon emissions of the plant based on the revised carbon flow distribution matrix.

[0112] The matrix processing unit performs weighted processing on the modified carbon flow distribution matrix, that is, multiplying the carbon flow intensity value of each node by its corresponding weight coefficient.

[0113] The total amount calculation unit sums up the weighted carbon flow intensity values ​​of all nodes to obtain the overall carbon emissions of the plant, and displays the result on the screen of the monitoring center for reference by managers.

[0114] It can be seen from the above steps that in actual application in the industrial plant, the system provided by the present invention realizes comprehensive monitoring of carbon emissions in the plant.

[0115] The carbon concentration sensor array collects carbon concentration data of distributed energy access points with high precision, while the digital twin module generates a dynamically updated virtual plant carbon flow distribution model through three-dimensional modeling and data fusion.

[0116] The carbon flow correction module dynamically adjusts the carbon flow distribution weight through the Bayesian inference algorithm, effectively compensating for the carbon flow tracking error and improving the monitoring accuracy.

[0117] The carbon emission calculation module outputs the overall carbon emissions of the plant by weighted processing and summing the corrected carbon flow distribution matrix, providing a scientific decision-making basis for plant managers.

[0118] In addition, the present invention achieves system scalability and maintenance convenience through modular design, which is suitable for the needs of efficient and accurate carbon emission monitoring in complex factory environments.

[0119] Each module in the system achieves comprehensive monitoring of the factory's carbon emissions through clear connection relationships and collaboration mechanisms, providing reliable technical support for factory managers to optimize energy use and formulate carbon emission reduction strategies.

[0120] Example 2

[0121] See also Figure 2 The method for monitoring carbon emissions in a plant area based on an electricity-carbon calculation model includes the following steps:

[0122] Step S1: Collecting real-time carbon concentration data of distributed energy access points within the plant, obtaining the carbon concentration value of each access point using a carbon concentration sensor array, and transmitting the collected data to a central processing unit;

[0123] Step S2: Synchronously collect the electricity consumption data within the plant, integrate the carbon concentration data and the electricity consumption data to construct an initial carbon flow distribution matrix, and use the digital twin to generate a carbon flow distribution model of the virtual plant;

[0124] Step S3: Analyze the carbon flow fluctuation characteristics caused by the access of distributed energy resources, dynamically adjust the carbon flow allocation weights through the Bayesian inference algorithm, and generate a modified carbon flow distribution matrix;

[0125] Step S4: Calculate the overall carbon emissions of the plant area based on the revised carbon flow distribution matrix and output the monitoring results.

[0126] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0127] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0128] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0129] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0130] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0131] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0132] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0135] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0136] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0137] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A plant carbon emissions monitoring method based on an electricity-carbon calculation model, characterized by: The following steps are involved: Step S1: Collecting real-time carbon concentration data of distributed energy access points within the plant, obtaining the carbon concentration value of each access point using a carbon concentration sensor array, and transmitting the collected data to a central processing unit; Step S2: Synchronously collect the electricity consumption data within the plant, integrate the carbon concentration data and the electricity consumption data to construct an initial carbon flow distribution matrix, and use the digital twin to generate a carbon flow distribution model of the virtual plant; Step S3: Analyze the carbon flow fluctuation characteristics caused by the access of distributed energy resources, dynamically adjust the carbon flow allocation weights through the Bayesian inference algorithm, and generate a modified carbon flow distribution matrix; Step S4: Calculate the overall carbon emissions of the plant area based on the revised carbon flow distribution matrix and output the monitoring results.

2. The method for monitoring plant carbon emissions based on the electricity-carbon calculation model according to claim 1, characterized in that: In step S1, the carbon concentration sensor array consists of multiple high-sensitivity gas sensors. Each sensor is installed in the exhaust duct or nearby area of ​​the distributed energy access point. The distance between the sensors is determined according to the plant layout and carbon diffusion characteristics, and the sensor array uses a wireless communication module to establish a connection with the central processing unit.

3. The method for monitoring carbon emissions from a plant area based on an electricity-carbon calculation model according to claim 2, characterized in that: In step S1, the installation position of the carbon concentration sensor array meets the following conditions: The sensor installation height is set between 1.5 meters and 3 meters from the ground, the sensor installation direction is facing the carbon emission source, and the sensor array coverage range meets the requirement of no blind spots in the detection area.

4. The method for monitoring carbon emissions from a plant area based on an electricity-carbon calculation model according to claim 3, characterized in that: In step S2, the process of constructing the initial carbon flow distribution matrix includes the following steps: Divide the plant into several functional zones, each zone corresponding to a carbon flow node; The initial carbon flow intensity value of each node is calculated through correlation analysis between electricity consumption data and carbon concentration data; the carbon flow intensity values ​​of all nodes are summarized to form an initial carbon flow distribution matrix.

5. The method for monitoring plant carbon emissions based on the electricity-carbon calculation model according to claim 4 is characterized in that: In step S2, the digital twin generation process includes the following steps: Use 3D modeling software to build a physical space model of the factory area; The collected carbon concentration data and electricity consumption data are input into the model to generate the carbon flow distribution status of the virtual plant; The digital twin is kept synchronized with the actual factory through a real-time data update mechanism.

6. The method for monitoring plant carbon emissions based on an electricity-carbon calculation model according to claim 1, characterized in that: In step S3, the application process of the Bayesian inference algorithm includes the following steps: Define the prior probability distribution function of the carbon flow allocation weight; calculate the posterior probability distribution function based on the real-time collected carbon concentration data and electricity consumption data; The carbon flow distribution weights are dynamically adjusted using the posterior probability distribution function to generate a revised carbon flow distribution matrix.

7. The method for monitoring plant carbon emissions based on the electricity-carbon calculation model according to claim 6, characterized in that: In step S3, the analysis process of the carbon flow fluctuation characteristics includes the following steps: Collect real-time operating parameters of distributed energy access points, including power generation, fuel type, and combustion efficiency; Combined with the changing trends of these parameters, their impact on carbon flow distribution is evaluated; the main characteristics of carbon flow fluctuations are identified through time series analysis methods.

8. The method for monitoring plant carbon emissions based on the electricity-carbon calculation model according to claim 7, characterized in that: In step S4, the calculation process of the overall carbon emissions of the plant includes the following steps: Multiply the carbon flow intensity value of each node in the modified carbon flow distribution matrix by its corresponding weight coefficient; The weighted carbon flow intensity values ​​of all nodes are summed up to obtain the overall carbon emissions of the plant.

9. A plant carbon emission monitoring system based on an electricity-carbon calculation model, for implementing the plant carbon emission monitoring method based on an electricity-carbon calculation model according to any one of claims 1 to 8, characterized in that: Includes data acquisition module, digital twin module, carbon flow correction module and carbon emission calculation module; The data acquisition module is used to collect carbon concentration data and power consumption data of distributed energy access points within the plant area and transmit the collected data to the central processing unit; The digital twin module is used to generate a carbon flow distribution model of the virtual plant based on the collected data, and to keep the model synchronized with the actual plant through a real-time data update mechanism; The carbon flow correction module is used to analyze the carbon flow fluctuation characteristics caused by the access of distributed energy resources, and dynamically adjust the carbon flow allocation weights through the Bayesian inference algorithm to generate a corrected carbon flow distribution matrix; The carbon emission calculation module is used to calculate the overall carbon emissions of the plant based on the revised carbon flow distribution matrix and output the monitoring results.

10. The plant carbon emission monitoring system based on the electricity-carbon calculation model according to claim 9 is characterized in that: The data acquisition module includes a carbon concentration sensor array, an electric energy metering device, and a wireless communication unit; The carbon concentration sensor array is responsible for collecting carbon concentration data, the electric energy metering device is responsible for collecting electric energy consumption data, and the wireless communication unit is responsible for transmitting the collected data to the central processing unit.

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