Carbon emission sharing model establishment method, system and equipment and storage medium

Abstract

The invention provides a carbon emission allocation model establishment method, system and device and a storage medium, and the method comprises the steps: obtaining the historical allocation data of each energy consumption unit, carrying out the data preprocessing of the historical allocation data, and obtaining the preprocessed data, which comprises the carbon emission and correlation control factors of each energy consumption unit; the predicted total displacement is obtained; the value of the correlation control factor is used as an independent variable, the carbon emission is used as a dependent variable, and an apportionment expression of an energy consumption unit is obtained; and obtaining an objective function of the minimum total cost allocation based on the allocation expression, establishing a constraint condition that the sum of the carbon emissions allocated by the energy consumption units is equal to the predicted total emission and the value of the correlation control factor in the threshold interval corresponding to the energy consumption units, and obtaining a carbon emission allocation model. According to the method, the carbon emission allocation result can be objectively obtained without manually maintaining the allocation proportion on the whole.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a method, system, device and storage medium for establishing a carbon emission allocation model. Background Art

[0002] Carbon emission allocation refers to the process of allocating the total carbon emissions to each participant in a system, organization or supply chain according to certain principles or methods. It is crucial for setting emission reduction targets and allocating responsibilities.

[0003] In the related art, in the commonly used carbon emission allocation method, the allocation ratio is often manually maintained for allocation calculation. The above technical solution has the following defects: it is highly subjective and cannot objectively and truly determine the allocation amount based on the actual situation of each energy-consuming unit; and in the process of determining the allocation amount, it does not combine the on-site energy consumption data, cost situation, etc. for analysis, and cannot achieve reasonable allocation of resources and maximization of economic benefits.

[0004] Based on the above analysis of the development status of this technical field, the existing technology lacks a solution for solving the carbon emission allocation problem by establishing a mathematical model. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system, device and storage medium for establishing a carbon emission allocation model, aiming to solve the above-mentioned problems in the prior art.

[0006] According to a first aspect of an embodiment of the present invention, a method for establishing a carbon emission allocation model is provided, comprising:

[0007] Obtaining historical allocation data of each energy-consuming unit, performing data preprocessing on the historical allocation data, and obtaining preprocessed data, wherein the preprocessed data includes the carbon emissions of each energy-consuming unit and correlation control factors;

[0008] Get the estimated total displacement;

[0009] Taking the value of the correlation control factor as the independent variable and the carbon emission as the dependent variable, the allocation expression of the energy consumption unit is obtained; and

[0010] Based on the allocation expression, the objective function of minimum allocated total cost is obtained, and the constraint conditions that the sum of carbon emissions allocated to each energy-consuming unit is equal to the expected total emissions and the value of the correlation control factor is within the corresponding threshold range of the energy-consuming unit are established to obtain the carbon emission allocation model.

[0011] According to a second aspect of an embodiment of the present invention, a carbon emission allocation model establishment system is provided, comprising:

[0012] A data preprocessing module is used to obtain the historical allocation data of each energy-consuming unit, perform data preprocessing on the historical allocation data, and obtain preprocessed data, wherein the preprocessed data includes the carbon emissions of each energy-consuming unit and correlation control factors;

[0013] A total amount acquisition module is used to obtain the estimated total displacement;

[0014] An allocation expression building module is used to obtain an allocation expression of energy consumption units by taking the value of the correlation control factor as an independent variable and the carbon emission as a dependent variable; and

[0015] The model building module is used to obtain the objective function of the minimum shared total cost based on the sharing expression, establish the constraint conditions that the sum of the carbon emissions shared by each energy-consuming unit is equal to the expected total emissions, and the value of the correlation control factor is within the corresponding threshold range of the energy-consuming unit, and obtain the carbon emission sharing model.

[0016] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the method for establishing a carbon emission allocation model as provided in the first aspect of the present disclosure are implemented.

[0017] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a program for implementing information transmission is stored. When the program is executed by a processor, the steps of the method for establishing a carbon emission allocation model provided in the first aspect of the present disclosure are implemented.

[0018] The technical solution provided by the embodiment of the present invention includes the following beneficial effects: establishing a carbon emission allocation model based on linear programming, taking the minimum allocated total cost as the objective function, and realizing the allocation solution under the premise of maximizing economic benefits; considering the correlation between the allocated amount and the correlation control factors in the process of model establishment to accurately express the allocated amount; and adding threshold interval rules to the constraint conditions to ensure that the allocated carbon emissions of each energy-consuming unit are in line with the actual application situation; in general, there is no need to manually maintain the allocation ratio, and the carbon emission allocation result can be objectively obtained.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 is a flow chart of a method for establishing a carbon emission allocation model according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of a carbon emission allocation model framework according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of a carbon emission allocation model establishment system according to an embodiment of the present invention;

[0024] Figure 4 is a schematic diagram of a system cloud platform according to an embodiment of the present invention;

[0025] Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0027] Method Embodiment

[0028] According to an embodiment of the present invention, a method for establishing a carbon emission allocation model is provided. Figure 1 is a flow chart of a method for establishing a carbon emission allocation model according to an embodiment of the present invention. Figure 1 As shown, the method for establishing a carbon emission allocation model according to an embodiment of the present invention specifically includes:

[0029] In step S110, historical allocation data of each energy-consuming unit is obtained, and data preprocessing is performed on the historical allocation data to obtain preprocessed data, wherein the preprocessed data includes the carbon emissions of each energy-consuming unit and correlation control factors, specifically including:

[0030] The historical allocation data of each energy-consuming unit is collected through the Industrial Internet, and the historical allocation data is cleaned and collected to ensure the accuracy and completeness of the data.

[0031] In the embodiment of the present invention, the correlation control factor is energy consumption. There is a linear correlation between carbon emissions and energy consumption. Energy consumption linearly affects carbon emissions data. Controlling the allocation of carbon emissions actually controls the energy consumption of each energy-consuming unit.

[0032] In step S120, the estimated total displacement is obtained, which specifically includes:

[0033] Obtain the estimated total emissions of the carbon emission plan, which is equivalent to the value set by the higher-level decision-makers;

[0034] In step S130, the value of the correlation control factor is used as the independent variable, and the carbon emission is used as the dependent variable to obtain the allocation expression of the energy consumption unit.

[0035] In step S140, the objective function of the minimum total apportionment cost is obtained based on the apportionment expression, and the constraint conditions that the sum of the carbon emissions apportioned to each energy-consuming unit is equal to the estimated total emissions and the value of the correlation control factor is within the threshold range corresponding to the energy-consuming unit are established to obtain the carbon emission apportionment model, which specifically includes:

[0036] Use formula 1 to establish the objective function and use formula 2 to express the apportionment expression:

[0037] min(C)=min(sum(ci×qi)) Formula 1;

[0038] qi=k×ei Formula 2;

[0039] Where C represents the total apportioned cost, ci represents the apportioned unit cost of the i-th energy-consuming unit, qi represents the apportioned carbon emissions of the i-th energy-consuming unit, ei represents the value of the energy consumption of the correlation control factor of the i-th energy-consuming unit, ci×qi represents the actual apportioned cost, k represents the correlation coefficient of the apportionment expression, sum represents the sum, and min represents the minimum value;

[0040] For different energy consumption units, the value of the correlation coefficient k is the same;

[0041] Preferably, the amortized expression can be expanded according to actual conditions. For example, on the basis of carbon emissions, other correlation control factors can be considered and corresponding constraints can be added thereto. That is, the independent variable in Formula 2 can have multiple factors.

[0042] Formula 3 is used to establish the constraint that the sum of carbon emissions allocated to each energy-consuming unit is equal to the estimated total emissions, and Formula 4 is used to establish the constraint that the value of the correlation control factor is within the corresponding threshold range of the energy-consuming unit:

[0043] Q = sum qi, Formula 3;

[0044] Lower threshold of energy-consuming unit i < ei < Upper threshold of energy-consuming unit i, Formula 4;

[0045] Among them, Q represents the predicted total emissions;

[0046] The reason for establishing the threshold range corresponding to the energy-consuming unit is that for the energy-consuming unit, too little sharing amount may lead to unqualified production quality of the energy-consuming unit, and too much sharing amount may lead to exceeding the maximum amount that can be processed, resulting in an additional burden; the threshold range is a rule preset under the condition of ensuring the accuracy, reasonableness, and scientific nature of the sharing principle, which reflects the limiting factors in actual operation from the side;

[0047] The carbon emission sharing model obtained by the above steps is essentially a linear programming model;

[0048] Preferably, since one of the constraint conditions in the carbon emission sharing model is that the sum of the carbon emissions shared by each energy-consuming unit is equal to the predicted total emissions, this equation increases the probability of having no feasible solution. However, since Q itself is a predicted planning value in the embodiments of the present invention and is not necessarily allocated strictly according to this value, in the case of having no feasible solution, the Q value can be dynamically adjusted according to the actual situation until a feasible solution appears.

[0049] The method further includes:

[0050] In step S150, use a model solving tool to solve the carbon emission sharing model to obtain the shared carbon emissions of each energy-consuming unit, and use a sharing tool to allocate the shared carbon emissions to the corresponding energy-consuming units;

[0051] In step S160, obtain a pre-established relationship expression with the value of the non-control factor of relevance as the independent variable and the carbon emissions as the dependent variable, input the result of the shared carbon emissions into the relationship expression to obtain the control value of the non-control factor of relevance, and generate a conclusion report based on the control value;

[0052] The non-control factors of relevance specifically include: process production efficiency, equipment operation efficiency, machine temperature, etc.;

[0053] In the embodiments of the present invention, the control factors of relevance are easy to control subjectively. For example, the energy consumption amount can control the processing amount corresponding to each energy-consuming unit under the condition of actual production feasibility. While the non-control factors of relevance are not easy to control subjectively and need to be optimized by other technical means. For example, the equipment operation efficiency cannot be improved arbitrarily under normal circumstances. However, analyzing the control value of the non-control factors of relevance can give suggestions for the energy field or actual operation to a certain extent;

[0054] The pre-established expression is qi = α × fi, where fi represents the non-control factor of correlation and α represents the correlation coefficient. Compared with the coefficient k, the value of α is smaller because energy consumption significantly affects carbon emissions. Substitute qi obtained by solving the carbon emission allocation model into the expression. If α is known, fi can be solved by reverse substitution.

[0055] The solved fi is the recommended value of the relevant non-control factor in the conclusion report, and the corresponding opinion analysis is given. For example, it is recommended that the equipment operating efficiency should be at least 90%. The average efficiency of the equipment in the past was about 85%. It is necessary to adopt technical means to reduce certain equipment parameters, such as reducing the machine temperature.

[0056] The opinion analysis is generated by a pre-trained convolutional neural network, and the parameters of the convolutional neural network are modified by feedback from the opinion analysis during the pre-training process;

[0057] The conclusion report also includes: the carbon emission allocation model outputs the allocated carbon emissions of each energy-consuming unit, and the actual allocated costs, etc.

[0058] In step S170, the actual allocation of each energy-consuming unit is monitored in real time.

[0059] Apply the allocation results calculated by the model to the actual production process to ensure that each energy-consuming unit is allocated according to the corresponding quota, and regularly monitor the allocation progress to promptly discover situations where the allocated carbon emissions are exceeded, or adjust the quota in a timely manner if problems are found;

[0060] Preferably, the carbon emission allocation model is continuously optimized according to actual application conditions, such as by changing constraint conditions.

[0061] The above technical solutions of the embodiments of the present invention are illustrated with reference to the following drawings.

[0062] Figure 2 Schematic diagram of the carbon emission allocation model framework of the embodiment of the present invention. Figure 2 As shown in the figure, the process of establishing the carbon emission allocation model is demonstrated, which is specifically: analyzing the constraints and objective functions; establishing a linear programming model and solving the linear programming model through mathematical tools.

[0063] To sum up, in response to the existing problems, the present invention proposes a method for establishing a carbon emission allocation model, establishes a carbon emission allocation model based on linear programming, takes the minimum total allocation cost as the objective function, and realizes the allocation solution under the premise of maximizing economic benefits; in the process of model establishment, the correlation between the allocation amount and the correlation control factors is considered to accurately express the allocation amount; and the threshold interval rule is added to the constraint conditions to ensure that the allocated carbon emissions of each energy-consuming unit meet the actual application situation; the integrated model solving tool and the allocation tool are used to solve the carbon emission allocation model and the actual allocation respectively. In general, there is no need to manually maintain the allocation ratio, and the carbon emission allocation result can be objectively obtained; after obtaining the allocated carbon emissions, the control value of the relevant non-control factors such as equipment operating efficiency can be obtained through the formula inverse substitution method, and the corresponding opinion analysis is given to optimize the allocation efficiency; and when executing the carbon emission allocation, the actual allocation process is monitored in real time and adjusted in time.

[0064] System Example

[0065] According to an embodiment of the present invention, a system for establishing a carbon emission allocation model is provided. Figure 3 Schematic diagram of a system for establishing a carbon emission allocation model according to an embodiment of the present invention. Figure 3 As shown, the carbon emission allocation model establishment system according to the embodiment of the present invention specifically includes:

[0066] The data preprocessing module 30 is used to obtain the historical allocation data of each energy-consuming unit, perform data preprocessing on the historical allocation data, and obtain preprocessed data, wherein the preprocessed data includes the carbon emissions of each energy-consuming unit and the correlation control factors.

[0067] The total amount acquisition module 32 is used to obtain the estimated total emission of the carbon emission limit planning, specifically for:

[0068] Get the estimated total emissions for the carbon cap program.

[0069] An allocation expression building module 34 is used to obtain an allocation expression of energy consumption units by taking the value of the correlation control factor as an independent variable and the carbon emission as a dependent variable;

[0070] The model building module 36 is used to obtain the objective function of the minimum total cost of the allocation based on the allocation expression, establish the constraint conditions that the sum of the carbon emissions allocated to each energy-consuming unit is equal to the estimated total emissions, and the value of the correlation control factor is within the threshold range corresponding to the energy-consuming unit, and obtain the carbon emission allocation model, which is specifically used for:

[0071] Use formula 1 to establish the objective function and use formula 2 to express the apportionment expression:

[0072] min(C)=min(sum(ci×qi)) Formula 1;

[0073] qi = k × ei, Equation 2;

[0074] Where C represents the total cost to be shared, ci represents the unit cost to be shared for the i-th energy-consuming unit, qi represents the carbon emission to be shared for the i-th energy-consuming unit, ei represents the value of the energy consumption of the correlation control factor for the i-th energy-consuming unit, k represents the correlation coefficient of the sharing expression, sum represents summation, and min represents finding the minimum value.

[0075] Use Equation 3 to establish a constraint condition where the sum of the carbon emissions shared by each energy-consuming unit is equal to the expected total emissions, and use Equation 4 to establish a constraint condition where the value of the correlation control factor is within the corresponding threshold range of the energy-consuming unit:

[0076] Q = sum qi, Equation 3;

[0077] Lower threshold of energy-consuming unit i < ei < upper threshold of energy-consuming unit i, Equation 4;

[0078] Where Q represents the expected total emissions.

[0079] The system further includes:

[0080] A solving module 38, configured to solve the carbon emission sharing model using a model solving tool to obtain the carbon emissions to be shared for each energy-consuming unit, and use a sharing tool to allocate the carbon emissions to be shared to the corresponding energy-consuming units;

[0081] A report generation module 310, configured to obtain a pre-established relationship expression with the value of the non-control factor of correlation as the independent variable and the carbon emissions as the dependent variable, input the result of the carbon emissions to be shared into the relationship expression to obtain the control value of the non-control factor of correlation, and generate a conclusion report based on the control value;

[0082] A monitoring module 312, configured to perform real-time monitoring on the actual sharing of each energy-consuming unit.

[0083] With reference to the following drawings, the above technical solutions of the embodiments of the present invention will be illustrated by examples.

[0084] Figure 4 It is a schematic diagram of the system cloud platform of the embodiments of the present invention. As Figure 4 shown, the system of the embodiments of the present invention can be built on a cloud platform, used to execute the corresponding method of the embodiments of the present invention. The platform integrates a tool for solving linear programming algorithms and a carbon emission sharing tool;

[0085] The process of carbon emission allocation for each energy-consuming unit in the cloud platform is as follows: predicting the total carbon emission, setting the threshold rules for allocation, using the linear programming algorithm to solve the carbon emission allocation result with the lowest cost, and using the carbon emission allocation tool to decompose the result into each energy-consuming unit; in actual use, additional monitoring and early warning functions are also set, including: real-time collection of the real-time emissions of each energy-consuming unit, monitoring the remaining amount of emissions and allocation, and issuing an alarm when the amount is insufficient; preferably, in the cloud platform, the carbon emission allocation model can be continuously optimized according to the actual situation, such as adding new constraints.

[0086] To sum up, in response to the existing problems, this invention proposes a carbon emission allocation model establishment system, establishes a carbon emission allocation model based on linear programming, takes the minimum total allocation cost as the objective function, and realizes the allocation solution under the premise of maximizing economic benefits; in the process of model establishment, the correlation between the allocation amount and the correlation control factors is considered to accurately express the allocation amount; and the threshold interval rule is added to the constraint conditions to ensure that the allocated carbon emissions of each energy-consuming unit meet the actual application situation; the integrated model solving tool and the allocation tool are used to solve the carbon emission allocation model and the actual allocation respectively. In general, there is no need to manually maintain the allocation ratio, and the carbon emission allocation result can be objectively obtained; after obtaining the allocated carbon emissions, the control value of the relevant non-control factors such as equipment operating efficiency can be obtained through the formula inverse method, and the corresponding opinion analysis can be given to optimize the allocation efficiency; and when executing the carbon emission allocation, the actual allocation process is monitored in real time and adjusted in time.

[0087] Electronic device embodiment

[0088] Figure 5 Schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 500 may include at least one processor 510 and a memory 520. The processor 510 may execute instructions stored in the memory 520. The processor 510 is connected to the memory 520 through a data bus. In addition to the memory 520, the processor 510 may also be connected to an input device 530, an output device 540, and a communication device 550 through a data bus.

[0089] The processor 510 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0090] The memory 520 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0091] In the embodiment of the present disclosure, executable instructions are stored in the memory 520, and the processor 510 can read the executable instructions from the memory 520 and execute the instructions to implement all or part of the steps of any of the carbon emission allocation model establishment methods in the above exemplary embodiments.

[0092] Computer Readable Storage Medium Embodiments

[0093] In addition to the above-mentioned methods and systems, an exemplary embodiment of the present disclosure may also be a computer program product or a computer-readable storage medium storing the computer program product, wherein the computer product includes computer program instructions that can be executed by a processor to implement all or part of the steps described in the carbon emission allocation model establishment method of any of the above-mentioned exemplary embodiments.

[0094] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages ​​and scripting languages ​​(e.g., Python). The program code may be executed entirely on the user computing device, partially on the user computing device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0095] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of readable storage media include: a static random access memory (SRAM) with one or more wires electrically connected, an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk, or any suitable combination of the above.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for establishing a carbon emission allocation model, characterized in that Including: Obtain the historical allocation data of each energy-consuming unit, perform data preprocessing on the historical allocation data to obtain preprocessed data, where the preprocessed data includes the carbon emissions and correlation control factors of each energy-consuming unit; Obtain the estimated total emissions; Use the value of the correlation control factor as the independent variable and the carbon emissions as the dependent variable to obtain the allocation expression of the energy-consuming unit; and Based on the allocation expression, obtain the objective function of the minimum total allocation cost, establish the constraint conditions that the sum of the allocated carbon emissions of each energy-consuming unit is equal to the estimated total emissions, and the value of the correlation control factor is within the corresponding threshold range of the energy-consuming unit, to obtain the carbon emissions allocation model.

2. The method according to claim 1, characterized in that The method further includes: Use a model solving tool to solve the carbon emissions allocation model to obtain the allocated carbon emissions of each energy-consuming unit, and use an allocation tool to allocate each allocated carbon emissions to the corresponding energy-consuming unit; Obtain a pre-established relationship expression with the value of the non-correlation control factor as the independent variable and the carbon emissions as the dependent variable, input the result of the allocated carbon emissions into the relationship expression to obtain the control value of the non-correlation control factor, and generate a conclusion report based on the control value; Perform real-time monitoring on the actual allocation of each energy-consuming unit.

3. The method according to claim 1, characterized in that Obtaining the estimated total emissions specifically includes: obtaining the estimated total emissions of the carbon emissions plan.

4. The method according to claim 1, characterized in that: Based on the allocation expression, obtaining the objective function of the minimum total allocation cost specifically includes: Use formula 1 to establish the objective function, and use formula 2 to represent the allocation expression: min(C) = min(sum(ci × qi)) formula 1; qi = k × ei formula 2; Where, C represents the total allocation cost, ci represents the allocation unit cost of the i-th energy-consuming unit, qi represents the allocated carbon emissions of the i-th energy-consuming unit, ei represents the value of the energy consumption of the correlation control factor of the i-th energy-consuming unit, k represents the correlation coefficient of the allocation expression, sum represents summation, and min represents finding the minimum value.

5. The method according to claim 4, characterized in that Establishing the constraint conditions that the sum of the allocated carbon emissions of each energy-consuming unit is equal to the estimated total emissions, and the value of the correlation control factor is within the corresponding threshold range of the energy-consuming unit specifically includes: Use formula 3 to establish the constraint condition that the sum of the allocated carbon emissions of each energy-consuming unit is equal to the estimated total emissions, and use formula 4 to establish the constraint condition that the value of the correlation control factor is within the corresponding threshold range of the energy-consuming unit: Q = sum qi formula 3; Lower threshold of energy-consuming unit i < ei < upper threshold of energy-consuming unit i formula 4; Where, Q represents the estimated total emissions.

6. The method according to claim 2, characterized in that The non-correlation control factors specifically include: process production efficiency and equipment operation efficiency.

7. A carbon emission allocation model establishment system, characterized in that: Including: A data preprocessing module for obtaining the historical allocation data of each energy-consuming unit, performing data preprocessing on the historical allocation data to obtain preprocessed data, where the preprocessed data includes the carbon emissions and correlation control factors of each energy-consuming unit; A total amount obtaining module for obtaining the estimated total emissions; An allocation expression establishment module for using the value of the correlation control factor as the independent variable and the carbon emissions as the dependent variable to obtain the allocation expression of the energy-consuming unit; and A model building module is used to obtain the objective function of the minimum shared total cost based on the sharing expression, establish the constraint conditions that the sum of the carbon emissions shared by each energy consuming unit is equal to the expected total emissions, and the value of the correlation control factor is within the corresponding threshold range of the energy consuming unit, and obtain the carbon emission sharing model.

8. The system according to claim 7, characterized in that The system further comprises: A solution module, used to solve the carbon emission allocation model using a model solution tool to obtain the allocated carbon emission of each energy-consuming unit, and use the allocation tool to allocate each allocated carbon emission to the corresponding energy-consuming unit; A report generation module, used to obtain a pre-established relational expression with the value of the correlation non-control factor as an independent variable and the carbon emission as a dependent variable, input the result of the apportioned carbon emission into the relational expression, obtain the control value of the correlation non-control factor, and generate a conclusion report based on the control value; The monitoring module is used to monitor the actual allocation of each energy-consuming unit in real time.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the method for establishing a carbon emission allocation model as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the method for establishing a carbon emission allocation model according to any one of claims 1 to 6 are implemented.

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