Copper product enterprise production and carbon emission reduction optimization management method and system

Abstract

The invention provides a copper product enterprise production and carbon emission reduction optimization management method and system, and belongs to the technical field of carbon emission reduction. According to the invention, the product yield statistics module counts product yield data, the carbon emission monitoring module monitors carbon emission data under different yields, and the carbon emission prediction module carries out fitting and carbon emission prediction by using a least square method based on the product yield and the carbon emission data. The product income prediction module carries out fitting and product income prediction by using a least square method based on product yield data and product income data, the emission reduction cost prediction module carries out emission reduction cost prediction, and the system evaluation module constructs a relation function between product actual income and product yield. And calculating and outputting a product yield value, a product actual income maximum value and an emission reduction cost. According to the method, the production plan can be effectively arranged for an enterprise on the premise of realizing carbon quota performance, the optimal production data suggestion is provided, and the production income maximization is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission reduction, and specifically relates to a production and carbon emission reduction optimization management method and system for a copper product enterprise. Background Art

[0002] Domestic research institutions and universities have conducted a lot of research work on optimizing enterprise production and emission reduction, studying the use of low-carbon technologies and optimized production processes to reduce carbon emissions, and reducing carbon intensity through measures such as improving energy efficiency, updating equipment, and using clean energy to achieve carbon quota compliance; on the other hand, enterprises need to expand production and generate economic benefits to invest in energy conservation and emission reduction. Enterprises face technical and cost issues in balancing carbon emission reduction and production benefits, and need efficient methods and systems to maximize production benefits while achieving carbon quota compliance.

[0003] Copper product enterprises will generate a large amount of carbon emissions during the production process. With the tightening of environmental protection policies and technological advances, they are facing increasingly higher environmental protection standards and emission reduction pressures. Copper product enterprises have reduced carbon emissions through energy-saving and emission reduction measures such as technological improvements, so that carbon emissions do not exceed the carbon quota limit. Copper products play an indispensable role in high-tech industries such as new energy, electric vehicles, and information technology. The industry needs to expand the scale of copper product production to promote the development of such industries, which will generate more carbon dioxide emissions at the same time. This requires enterprises to expand production and create more economic benefits to make up for the investment in energy conservation and emission reduction, provided that carbon emissions do not exceed the carbon quota limit. At present, the products of copper product enterprises are very diverse, but these enterprises usually lack targeted production and emission reduction optimization management methods and systems, and cannot maximize production benefits under the premise of carbon quota compliance.

[0004] Patent document CN118052518A discloses a tobacco production carbon emission monitoring system based on multi-source data, which obtains tobacco production in a specified area within a preset unit time through a production volume statistics module, obtains a carbon emission data set of tobacco production in a specified area within a preset unit time through a carbon emission monitoring module, analyzes the carbon emission data set through a carbon emission analysis module, calculates the total carbon emissions of tobacco production in the specified area within the past preset unit time based on the obtained carbon emission data set, and determines whether the total carbon emissions of tobacco production in the specified area within the preset unit time meet the corresponding carbon emission standards through an intelligent decision-making module, and sends the judgment result to the monitoring background module. However, this technology only solves how to evaluate carbon emissions in the tobacco production process, and then promptly corrects the situation of excessive carbon emissions; it does not consider the comprehensive use of carbon emissions, tobacco production and product benefits to construct an evaluation optimization model to maximize production benefits under the premise of carbon quota compliance.

[0005] Patent document CN113592187A discloses an intelligent carbon emission management system and method, which includes an emission prediction module for predicting carbon emission data according to a preset production plan, an emission reduction prediction module for predicting emission reduction data according to a preset energy conservation and emission reduction plan, an index judgment module for generating predicted carbon emissions according to carbon emission data and emission reduction data, and an index judgment module for generating judgment results of purchase index and sales index by comparing predicted carbon emissions and carbon emission index, and an emission reduction optimization module for generating purchase index signal and sale index signal. However, this technology can only predict carbon emissions and trade carbon emission indexes according to the prediction results; it also does not consider combining the predicted carbon emissions with production volume and product revenue to construct an evaluation model, and predict the carbon emissions and production volume that maximizes production revenue under the premise of carbon quota compliance. Summary of the invention

[0006] The present invention aims to address the technical problems existing in the prior art and provide a method and system for optimizing the management of production and carbon emission reduction for copper product enterprises. By setting a product output statistics module, a carbon emission monitoring module, a carbon emission prediction module, a product revenue prediction module, an emission reduction cost prediction module and a system evaluation module, the product output value, the maximum actual product revenue and the emission reduction cost are calculated, so as to effectively arrange production plans for enterprises, provide optimal production data suggestions on the premise of achieving carbon quota compliance, and maximize production revenue.

[0007] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0008] A copper product enterprise production and carbon emission reduction optimization management method comprises the following steps:

[0009] S1. Obtain product output X at different time points t t , where t=1,2,3....N, N represents the Nth time point, N≥15;

[0010] S2. Get product output as X t The carbon emissions generated by t ;

[0011] S3, product output X t and carbon emissions C t Perform fitting and make carbon emission prediction;

[0012] S4. Product output X t and product revenue P t Perform fitting and product revenue forecast;

[0013] S5. Get the output of the product when it is X t 、The emission reduction cost invested by adopting emission reduction measures is B t The carbon emissions generated whent , and then obtain the carbon emission reduction Y t , where Y t =C t -C' t , for carbon emission reduction Y t and abatement costs B t Perform fitting and make emission reduction cost prediction;

[0014] S6. Obtain annual carbon quota Q, and calculate emission reduction cost B according to annual carbon quota Q and emission reduction cost B. t , Carbon emission reduction Y t and product revenue P t , construct the actual product revenue P A Function model with product output X, calculate the output product output value X A 、The maximum actual profit of the product P max and abatement costs B max .

[0015] Preferably, in step S3, before the carbon emission prediction is performed, the product output data and carbon emission data obtained in step S2 are cleaned and preprocessed, and the specific processing process is as follows:

[0016] S301, delete product output X t and carbon emissions C t Abnormal data that is negative;

[0017] S302, the cleaned data is calculated according to the product output X t In order from small to large, the carbon emissions C t Sort by

[0018] S303, delete the abnormal data whose carbon emission of the latter item is less than that of the former item, and obtain the output X of the product after cleaning and pretreatment. t and carbon emissions data C t , where t=1, 2, 3...K, K represents the Kth time point, K≤N.

[0019] Preferably, in step S3, the product output X is calculated based on the least squares method. t and carbon emissions C t The specific steps for fitting and predicting carbon emissions are as follows:

[0020] Construct based on product output X t and carbon emissions C t The relationship function:

[0021] C t =β2·X t 2 +β1·X t +β0,

[0022] Among them, β2, β1 and β0 are all constants;

[0023] Solution,

[0024]

[0025] when

[0026]

[0027] Get the values ​​of β2, β1, and β0 and substitute them back into the product output X t and carbon emissions C t The relationship function is used to obtain the carbon emission prediction model.

[0028] Preferably, in step S4, before performing product revenue forecasting, according to the product output X obtained in step S1, t , record the corresponding product revenue P t .

[0029] Preferably, in step S4, before performing product revenue forecasting, the data of product output and product revenue are cleaned and preprocessed, and the specific processing process is as follows:

[0030] S401, according to product output X t From small to large, the product benefits P t Sort by

[0031] S402, delete the abnormal data where the revenue of the next product is less than the revenue of the previous product, and obtain the output X of the product after cleaning and pretreatment. t and product revenue P t , where t=1, 2, 3...R, R represents the Rth time point, R≤K.

[0032] Preferably, in step S4, the product output X is calculated based on the least squares method. t and product revenue P t The specific steps for fitting and predicting product revenue are as follows:

[0033] Construct based on product output X t and product revenue P t The relationship function:

[0034] P t =a1·X t +a0,

[0035] Where a1 is a constant, a0 is a fixed cost and is a negative value;

[0036] Solution,

[0037]

[0038] when

[0039]

[0040] Get the values ​​of a1 and a0 and substitute them back into the product output X t and product revenue P t The relationship function of is used to obtain the product revenue prediction model.

[0041] Preferably, in step S5, the carbon emission reduction amount Y is calculated based on the least squares method. t and abatement costs B t The specific steps for fitting and forecasting emission reduction costs are as follows:

[0042] Construct a carbon reduction based on Y t and abatement costs B t The relationship function:

[0043] B t =γ·Y t 2 ,

[0044] Where γ is a constant;

[0045] Solution,

[0046]

[0047] when

[0048] Get the value of γ and substitute it back into the carbon emission reduction Y t and abatement costs B t The relationship function of is used to obtain the emission reduction cost prediction model.

[0049] Preferably, the emission reduction cost B t When the carbon emissions generated are C' t ≤Q, where C t -Y t ≤Q, in step S6, construct the product based on the actual revenue P A The specific steps of the function model with product output X are as follows:

[0050] Build a product based on actual revenue P A Relationship function with product output X:

[0051] P A =PB=a1·X+a0-γ·Y 2 =a1·X+a0-γ·(β2·X 2+β1·X+β0-Q) 2 ,

[0052] Among them, a1 and a0 are constants obtained from the product revenue prediction model; β2, β1 and β0 are all constants obtained from the carbon emission prediction model; γ is a constant obtained from the emission reduction cost prediction model; Q is the carbon quota;

[0053] Solution,

[0054]

[0055] Get the solution X of the differential equation A , where the product output is X A When , the actual profit of the product is the largest;

[0056] Product output X A Return the actual product income P A The relationship function with product output X, the maximum actual product revenue P is obtained max ,in,

[0057] P max =a1·X A +a0-γ·(β2·X A 2 +β1·X A +β0-Q) 2

[0058] Among them, the emission reduction cost B max for,

[0059] B max =γ·Y 2 =γ·(β2·X A 2 +β1·X A +β0-Q) 2 .

[0060] A system for optimizing the production and carbon emission reduction management of copper product enterprises according to the above method includes a product output statistics module, a carbon emission monitoring module, a carbon emission prediction module, a product revenue prediction module, an emission reduction cost prediction module and a system evaluation module;

[0061] The product output statistics module is used to count the product output of copper products in real time;

[0062] The carbon emission monitoring module is used to monitor carbon emissions under different production volumes;

[0063] The carbon emission prediction module is used to clean and preprocess product output data and carbon emission data, and calculate a carbon emission prediction model;

[0064] The product revenue prediction module is used to clean and preprocess product output data and product revenue data, and calculate a product revenue prediction model;

[0065] The emission reduction cost prediction module is used to calculate and obtain an emission reduction cost prediction model;

[0066] The system evaluation module is used to construct a relationship function between the actual product revenue and the product output, and calculate the output product output value, the maximum actual product revenue and the emission reduction cost.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] (1) A copper product enterprise production and carbon emission reduction optimization management method and system of the present invention can, by using the results output by the system evaluation module, effectively arrange production plans by giving optimal production data suggestions on the premise of achieving carbon quota compliance, reasonably optimize the investment of emission reduction costs, maximize production benefits on the premise of achieving carbon quota compliance, promote the low-carbon transformation of enterprises, and achieve a balance between economic benefits and environmental protection;

[0069] (2) A copper product enterprise production and carbon emission reduction optimization management method and system of the present invention cleans and preprocesses data through a carbon emission prediction module and a product revenue prediction module, removes abnormal data, enhances the accuracy and reliability of the data, and makes the final calculation and output results more accurate;

[0070] (3) A copper product enterprise production and carbon emission reduction optimization management method and system of the present invention, through a carbon emission prediction module, comprehensively considers the product output and the corresponding carbon emission data, uses a second-order linear fitting method to track the nonlinear relationship between the impact of product output changes on carbon emissions, constructs a carbon emission prediction model, and improves the accuracy of the prediction;

[0071] (4) A copper product enterprise production and carbon emission reduction optimization management method and system of the present invention, wherein the carbon emission prediction module makes the prediction work no longer limited to high-consumption scenarios, and can quickly and accurately build a carbon emission prediction model, shorten the evaluation time, and improve the system evaluation efficiency;

[0072] (5) The present invention provides a copper product enterprise production and carbon emission reduction optimization management method and system, which comprehensively considers data such as emission reduction costs, product revenue data and carbon emission reduction data, and helps enterprises to more effectively manage and control carbon emissions through precise data analysis and model construction, ensuring compliance with government-mandated carbon quota requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a work flow chart of a copper product enterprise production and carbon emission reduction optimization management method according to an embodiment of the present invention;

[0074] Figure 2 is a workflow diagram for predicting carbon emissions according to an embodiment of the present invention;

[0075] Figure 3 is a workflow diagram for product revenue forecasting according to an embodiment of the present invention;

[0076] Figure 4 is a workflow diagram for forecasting emission reduction costs according to an embodiment of the present invention;

[0077] Figure 5 is a flowchart of obtaining product output data, product actual maximum benefit data and emission reduction cost data in an embodiment of the present invention;

[0078] Figure 6 It is a workflow diagram of a copper product enterprise production and carbon emission reduction optimization management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The following will be combined with the attached Figure 1-6 , a copper product enterprise production and carbon emission reduction optimization management method and system of the present invention is clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] Example 1

[0081] like Figure 1-5 As shown, according to a specific implementation scheme of the present invention, a copper product enterprise production and carbon emission reduction optimization management method of the present invention is described in detail below.

[0082] The present invention provides a copper product enterprise production and carbon emission reduction optimization management method, comprising the following steps:

[0083] S1. Obtain product output X at different time points t t , where t=1,2,3....N, N represents the Nth time point, N≥15;

[0084] S2. Get product output as X t The carbon emissions generated by t ;

[0085] S3, product output X t and carbon emissions C t Perform fitting and make carbon emission prediction;

[0086] S4. Product output X tand product revenue P t Perform fitting and product revenue forecast;

[0087] S5. Get the output of the product when it is X t 、The emission reduction cost invested by adopting emission reduction measures is B t The carbon emissions generated when t , and then obtain the carbon emission reduction Y t , where Y t =C t -C' t , for carbon emission reduction Y t and abatement costs B t Perform fitting and make emission reduction cost prediction;

[0088] S6. Obtain annual carbon quota Q, and calculate emission reduction cost B according to annual carbon quota Q and emission reduction cost B. t , Carbon emission reduction Y t and product revenue P t , construct the actual product revenue P A Function model with product output X, calculate the output product output value X A 、The maximum actual profit of the product P max and abatement costs B max .

[0089] In actual applications, the product output statistics module can obtain product output data by manually collecting statistical data or through the enterprise production management system; the carbon emission monitoring module can obtain carbon emission data through a carbon emission monitor based on non-dispersive infrared (NDIR) technology.

[0090] In some embodiments, in step S3, before the carbon emission prediction is performed, the product output data and carbon emission data obtained in step S2 are cleaned and preprocessed, and the specific processing process is as follows:

[0091] S301, delete product output X t and carbon emissions C t Abnormal data that is negative;

[0092] S302, the cleaned data is calculated according to the product output X t In order from small to large, the carbon emissions C t Sort by

[0093] S303, delete the abnormal data whose carbon emission of the latter item is less than that of the former item, and obtain the output X of the product after cleaning and pretreatment. t and carbon emissions data C t , where t=1, 2, 3...K, K represents the Kth time point, K≤N.

[0094] Further, in step S3, the product output X is calculated based on the least square method. t and carbon emissions C t The specific steps for fitting and predicting carbon emissions are as follows:

[0095] Construct based on product output X t and carbon emissions C t The relationship function:

[0096] C t =β2·X t 2 +β1·X t +β0,

[0097] Among them, β2, β1 and β0 are all constants;

[0098] Solution,

[0099]

[0100] when

[0101]

[0102] Get the values ​​of β2, β1, and β0 and substitute them back into the product output X t and carbon emissions C t The relationship function is used to obtain the carbon emission prediction model.

[0103] In some other embodiments, in step S4, before performing product revenue forecasting, the product output X obtained in step S1 is t , professionals calculate and record the corresponding product revenue P t , the data of product output and product revenue are cleaned and preprocessed. The specific processing process is as follows:

[0104] S401, according to product output X t From small to large, the product benefits P t Sort by

[0105] S402, delete the abnormal data where the revenue of the next product is less than the revenue of the previous product, and obtain the output X of the product after cleaning and pretreatment. t and product revenue P t , where t=1, 2, 3...R, R represents the Rth time point, R≤K.

[0106] By cleaning and preprocessing the data through the carbon emission prediction module and the product revenue prediction module, abnormal data can be removed, the accuracy and reliability of the data can be enhanced, and the final calculation and output results can be more accurate.

[0107] Further, in step S4, the product output X is calculated based on the least square method. t and product revenue P t The specific steps for fitting and predicting product revenue are as follows:

[0108] Construct based on product output X t and product revenue P t The relationship function:

[0109] P t =a1·X t +a0,

[0110] Where a1 is a constant, a0 is a fixed cost and is a negative value;

[0111] Solution,

[0112]

[0113] when

[0114]

[0115] Get the values ​​of a1 and a0 and substitute them back into the product output X t and product revenue P t The relationship function of is used to obtain the product revenue prediction model.

[0116] Furthermore, in step S5, the carbon emission reduction amount Y is calculated based on the least square method. t and abatement costs B t The specific steps for fitting and forecasting emission reduction costs are as follows:

[0117] Construct a carbon reduction based on Y t and abatement costs B t The relationship function:

[0118] B t =γ·Y t 2 ,

[0119] Where γ is a constant;

[0120] Solution,

[0121]

[0122] when

[0123] Get the value of γ and substitute it back into the carbon emission reduction Y t and abatement costs B tThe relationship function of is used to obtain the emission reduction cost prediction model.

[0124] Furthermore, government regulators will set a carbon emission cap for enterprises every year. Enterprises can set a carbon quota Q for their products on the premise that carbon emissions do not exceed the cap. According to government regulatory requirements, the actual annual carbon emissions of products shall not exceed the carbon quota Q. Therefore, when investing in emission reduction costs B t After that, the carbon emissions generated should be C' t ≤Q, where C t -Y t ≤Q; in step S6, construct a product based on actual revenue P A The specific steps of the function model with product output X are as follows:

[0125] Build a product based on actual revenue P A Relationship function with product output X:

[0126] P A =PB=a1·X+a0-γ·Y 2 =a1·X+a0-γ·(β2·X 2 +β1·X+β0-Q) 2 ,

[0127] Among them, a1 and a0 are constants obtained from the product revenue prediction model; β2, β1 and β0 are all constants obtained from the carbon emission prediction model; γ is a constant obtained from the emission reduction cost prediction model; Q is the carbon quota;

[0128] Solution,

[0129]

[0130] Get the solution X of the differential equation A , where the product output is X A When , the actual profit of the product is the largest;

[0131] Product output X A Return the actual product income P A The relationship function with product output X, the maximum actual product revenue P is obtained max ,in,

[0132] P max =a1·X A +a0-γ·(β2·X A 2 +β1·X A +β0-Q) 2

[0133] Among them, the emission reduction cost B max for,

[0134] B max =γ·Y 2 =γ·(β2·X A 2 +β1·X A +β0-Q) 2 ;

[0135] The final system evaluation module outputs the maximum annual product actual revenue P max , and the corresponding product output value X when the actual product revenue is the largest A and abatement costs B max .

[0136] In practical applications, copper product companies may not be able to accurately control product output to X A , according to the actual situation, the output can be controlled within X A Nearby, under the premise of achieving carbon quota compliance, through energy conservation and emission reduction investment, gradually eliminate or transform backward production lines, and promote high-quality development of enterprises. When the annual optimal output of an enterprise cannot meet market demand, the enterprise can purchase carbon quotas or CCER (China Certified Emission Reduction) to supplement the quota in the carbon trading market, and generate more income under the premise of achieving carbon emission compliance.

[0137] Example 2

[0138] According to a specific implementation scheme of the present invention, a copper product enterprise production and carbon emission reduction optimization management system of the present invention is described in detail below.

[0139] like Figure 6 As shown, the present invention provides a system for optimizing the production and carbon emission reduction of copper product enterprises according to the above method, including a product output statistics module, a carbon emission monitoring module, a carbon emission prediction module, a product revenue prediction module, an emission reduction cost prediction module and a system evaluation module;

[0140] The product output statistics module is used to count the product output of copper products in real time;

[0141] The carbon emission monitoring module is used to monitor carbon emissions under different production volumes;

[0142] The carbon emission prediction module is used to clean and pre-process product output data and carbon emission data, and fit the cleaned and pre-processed product output data and carbon emission data based on the least squares method to calculate and obtain a carbon emission prediction model;

[0143] The product revenue prediction module is used to clean and pre-process product output data and product revenue data, and fit the cleaned and pre-processed product output data and product revenue data based on the least squares method, and calculate the product revenue prediction model;

[0144] The emission reduction cost prediction module fits the emission reduction cost and the carbon emission reduction amount based on the least square method to calculate the emission reduction cost prediction model;

[0145] The system evaluation module can obtain product carbon quota data. Government regulatory agencies will set a carbon emission cap for enterprises every year. Enterprises can set carbon quotas for products on the premise that carbon emissions do not exceed the cap. The system evaluation module uses carbon quota data, carbon emission data, emission reduction cost data and product revenue data to construct a relationship function between product actual revenue and product output, conduct a comprehensive evaluation, calculate and output product output value, maximum actual product revenue and emission reduction cost, and give optimal production data recommendations to maximize production revenue on the premise of achieving carbon quota compliance.

[0146] By utilizing the output results of the system evaluation module, we can effectively arrange production plans, rationally optimize the investment in emission reduction costs, promote the low-carbon transformation of enterprises, and achieve a balance between economic benefits and environmental protection, while achieving carbon quota compliance.

[0147] Through the carbon emission prediction module, based on product output data and combined with the characteristics of the copper product production process, the carbon emission data corresponding to the product output is comprehensively considered, and the nonlinear relationship between the impact of product output changes on carbon emissions is tracked using the second-order linear fitting method. A carbon emission prediction model is constructed to improve the accuracy of the prediction;

[0148] Moreover, the carbon emission prediction module of the present invention makes the prediction work no longer limited to high-consumption scenarios, and can quickly and accurately build a carbon emission prediction model. For example, a prediction model is built based on the carbon emission data when producing 5 tons and 10 tons of products, without having to wait until the carbon emissions when producing 100 tons of products are monitored, thus shortening the evaluation time and improving the system evaluation efficiency.

[0149] In addition, it comprehensively considers data such as emission reduction costs, product revenue data and carbon emission reduction, and through precise data analysis and model construction, helps companies to manage and control carbon emissions more effectively and ensure compliance with government-mandated carbon quota requirements.

[0150] The above description is only an embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement and improvement made within the application scope of the present invention shall be included in the protection scope of the present invention.

Claims

1. A copper product enterprise production and carbon emission reduction optimization management method, characterized in that: The following steps are involved: S1. Obtain product output X at different time points t t , where t=1,2,3....N, N represents the Nth time point, N≥15; S2. Get product output as X t The carbon emissions generated by t ; S3, product output X t and carbon emissions C t Perform fitting and make carbon emission prediction; S4. Product output X t and product revenue P t Perform fitting and product revenue forecast; S5. Get the output of the product when it is X t 、The emission reduction cost invested by adopting emission reduction measures is B t The carbon emissions generated when t , and then obtain the carbon emission reduction Y t , where Y t =C t -C' t , for carbon emission reduction Y t and abatement costs B t Perform fitting and make emission reduction cost prediction; S6. Obtain annual carbon quota Q, and calculate emission reduction cost B according to annual carbon quota Q and emission reduction cost B. t , Carbon emission reduction Y t and product revenue P t , construct the actual product revenue P A Function model with product output X, calculate the output product output value X A 、The maximum actual profit of the product P max and abatement costs B max .

2. The copper product enterprise production and carbon emission reduction optimization management method according to claim 1 is characterized in that: In step S3, before the carbon emission prediction is performed, the product output data and carbon emission data obtained in step S2 are cleaned and preprocessed. The specific processing process is as follows: S301, delete product output X t and carbon emissions C t Abnormal data that is negative; S302, the cleaned data is calculated according to the product output X t In order from small to large, the carbon emissions C t Sort by S303, delete the abnormal data whose carbon emission of the latter item is less than that of the former item, and obtain the output X of the product after cleaning and pretreatment. t and carbon emissions data C t , where t=1, 2, 3...K, K represents the Kth time point, K≤N.

3. The copper product enterprise production and carbon emission reduction optimization management method according to claim 2 is characterized in that: In step S3, the product output X is calculated based on the least squares method. t and carbon emissions C t The specific steps for fitting and predicting carbon emissions are as follows: Construct based on product output X t and carbon emissions C t The relationship function: C t =β2·X t 2 +β1·X t +β0, Among them, β2, β1 and β0 are all constants; Solution, when Get the values ​​of β2, β1, and β0 and substitute them back into the product output X t and carbon emissions C t The relationship function is used to obtain the carbon emission prediction model.

4. The copper product enterprise production and carbon emission reduction optimization management method according to claim 3 is characterized in that: In step S4, before the product revenue forecast is performed, the product output X obtained in step S1 is used to t , record the corresponding product revenue P t .

5. The copper product enterprise production and carbon emission reduction optimization management method according to claim 4 is characterized in that: In step S4, before the product revenue forecast is performed, the product output data and product revenue data are cleaned and preprocessed. The specific processing process is as follows: S401, according to product output X t From small to large, the product benefits P t Sort by S402, delete the abnormal data where the revenue of the next product is less than the revenue of the previous product, and obtain the output X of the product after cleaning and pretreatment. t and product revenue P t , where t=1, 2, 3...R, R represents the Rth time point, R≤K.

6. The copper product enterprise production and carbon emission reduction optimization management method according to claim 5 is characterized in that: In step S4, the product output X is calculated based on the least squares method. t and product revenue P t The specific steps for fitting and predicting product revenue are as follows: Construct based on product output X t and product revenue P t The relationship function: P t =a1·X t +a0, Where a1 is a constant, a0 is a fixed cost and is a negative value; Solution, when Get the values ​​of a1 and a0 and substitute them back into the product output X t and product revenue P t The relationship function of is used to obtain the product revenue prediction model.

7. The copper product enterprise production and carbon emission reduction optimization management method according to claim 6 is characterized in that: In step S5, the carbon emission reduction amount Y is calculated based on the least square method. t and abatement costs B t The specific steps for fitting and forecasting emission reduction costs are as follows: Construct a carbon reduction based on Y t and abatement costs B t The relationship function: B t =γ·Y t 2 Where γ is a constant; Solution, when Get the value of γ and substitute it back into the carbon emission reduction Y t and abatement costs B t The relationship function of is used to obtain the emission reduction cost prediction model.

8. The copper product enterprise production and carbon emission reduction optimization management method according to claim 7 is characterized in that: Invest in emission reduction costs B t When the carbon emissions generated are C' t ≤Q, where C t -Y t ≤Q, in step S6, construct the product based on the actual revenue P A The specific steps of the function model with product output X are as follows: Build a product based on actual revenue P A Relationship function with product output X: P A =PB=a1·X+a0-γ·Y 2 =a1·X+a0-γ·(β2·X 2 +β1·X+β0-Q) 2 , Among them, a1 and a0 are constants obtained from the product revenue prediction model; β2, β1 and β0 are all constants obtained from the carbon emission prediction model; γ is a constant obtained from the emission reduction cost prediction model; Q is the carbon quota; Solution, Get the solution X of the differential equation A , where the product output is X A When , the actual profit of the product is the largest; Product output X A Return the actual product income P A The relationship function with product output X, the maximum actual product revenue P is obtained max ,in, P max =a1·X A +a0-γ@(β2·X A 2 +β1·X A +β0-Q) 2 Among them, the emission reduction cost B max for, B max =γ·Y 2 =γ·(β2·X A 2 +β1·X A +β0-Q) 2 。 9. A system for optimizing the production and carbon emission reduction management of copper product enterprises according to the method described in any one of claims 1 to 8, characterized in that: It includes product output statistics module, carbon emission monitoring module, carbon emission prediction module, product revenue prediction module, emission reduction cost prediction module and system evaluation module; The product output statistics module is used to count the product output of copper products in real time; The carbon emission monitoring module is used to monitor carbon emissions under different production volumes; The carbon emission prediction module is used to clean and preprocess product output data and carbon emission data, and calculate a carbon emission prediction model; The product revenue prediction module is used to clean and preprocess product output data and product revenue data, and calculate a product revenue prediction model; The emission reduction cost prediction module is used to calculate and obtain an emission reduction cost prediction model; The system evaluation module is used to construct a relationship function between the actual product revenue and the product output, and calculate the output product output value, the maximum actual product revenue and the emission reduction cost.

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