Enterprise carbon emission prediction method and device, computer program product and storage medium

Abstract

The invention relates to an enterprise carbon emission prediction method and device, a computer program product and a storage medium. The enterprise carbon emission prediction method comprises the steps that a data relation model of enterprise production process carbon emission and enterprise carbon emission total amount is established in advance; on the basis of the order information, carbon emission in the enterprise production process in the target period is calculated; and determining the total enterprise carbon emission amount in the target period according to the enterprise production process carbon emission amount in the target period and the data relation model. According to the method and the device, the monthly, quarterly or annual carbon emission of the discrete manufacturing enterprise can be quickly, efficiently and accurately predicted based on the order information.

Description

Technical Field

[0001] The present disclosure relates to the field of carbon emission prediction, and in particular to a method and device for predicting carbon emissions of an enterprise, a computer program product, and a storage medium. Background Art

[0002] Related technologies For discrete enterprises or factories, the production process is carried out according to orders or tasks. At the same time, discrete manufacturing enterprises have the characteristics of many product series and small production quantities of a single series. The number of orders directly affects the energy and non-energy usage of the factory. This leads to the need to flexibly adjust the production plan to adapt to changes in market demand due to unstable product output. The production of greenhouse gases in manufacturing enterprises is mainly directly or indirectly generated by the use of conventional energy such as electricity, natural gas, steam, diesel and gasoline. The prediction of carbon emissions depends largely on the use of these energy sources. Summary of the invention

[0003] The inventors found through research that most of the related technologies for predicting the total carbon emissions of enterprises or factories use the historical carbon emission intensity reduction method to force the reduction of carbon emission intensity in previous years to predict and calculate the total annual carbon emissions. For discrete enterprises with significant annual output fluctuations, the total carbon emissions predicted based on output value and carbon emission intensity have the disadvantages of low accuracy and large deviation in prediction results.

[0004] In view of at least one of the above technical problems, the present disclosure provides a method and device for predicting enterprise carbon emissions, a computer program product and a storage medium, which can quickly, efficiently and accurately predict the carbon emissions of discrete manufacturing enterprises on a monthly, quarterly or annual basis based on order information.

[0005] According to one aspect of the present disclosure, a method for predicting carbon emissions of an enterprise is provided, comprising:

[0006] Establish in advance a data relationship model between the carbon emissions of the enterprise's production process and the total carbon emissions of the enterprise;

[0007] Based on order information, calculate the carbon emissions of the enterprise's production process within the target period;

[0008] The total carbon emissions of the enterprise within the target period are determined according to the carbon emissions of the enterprise's production process within the target period and the data relationship model.

[0009] In some embodiments of the present disclosure, calculating the carbon emissions of the enterprise production process within the target period based on the order information includes:

[0010] For each model of product in the enterprise's production process within the target period, calculate the total carbon emissions of a single product of that model in the production process;

[0011] For each order in the enterprise's production process within the target period, the total carbon emissions of all products in the order during the production process are determined based on the total carbon emissions of each model of the product during the production process and the number of products of each model to be produced in the order;

[0012] The carbon emissions of the enterprise's production process within the target period are determined based on the total carbon emissions of all products in the production process of each order in the enterprise's production process within the target period.

[0013] In some embodiments of the present disclosure, for each model of product in the enterprise's production process within the target period, calculating the total carbon emissions of a single product of this model in the production process includes:

[0014] For each process in the enterprise's production process within the target period, the carbon emissions per working hour of the process shall be determined based on the usage of each energy source per working hour of the process and the energy carbon emission factor coefficient of each energy source;

[0015] For each model of product in the enterprise's production process within the target period, the total carbon emissions of a single product of this model in the production process are determined based on the number of working hours used in each process during the production of a single product of this model and the carbon emissions per working hour of each process.

[0016] In some embodiments of the present disclosure, for each process of the enterprise production process within the target period, determining the carbon emissions per working hour of the process according to the usage of each energy per working hour of the process and the energy carbon emission factor coefficient of each energy includes:

[0017] Determine the process matrix based on the usage of each energy source in each process of the enterprise's production process within the target period;

[0018] Determine the energy carbon emission factor coefficient matrix based on the energy carbon emission factor coefficient of each energy source in the enterprise production process within the target period;

[0019] According to the process matrix and the energy carbon emission factor coefficient matrix, the carbon emission matrix for each process per working hour is determined, wherein the carbon emission matrix for each process per working hour is a column matrix, and the elements of the carbon emission matrix for each process per working hour are the carbon emission for each process in the enterprise's production process within the target period.

[0020] In some embodiments of the present disclosure, determining the process matrix according to the usage of each energy source by each process in the enterprise production process within the target period includes:

[0021] The process matrix is ​​determined based on the process information, energy information and the usage of various energy sources corresponding to different processes within the target cycle, wherein the row information of the process matrix is ​​the process information, the column information of the process matrix is ​​the energy information, and the elements of the process matrix are the usage of the energy in the column during a single operation in the current row process.

[0022] In some embodiments of the present disclosure, the energy carbon emission factor coefficient matrix is ​​a column matrix, the row information of the energy carbon emission factor coefficient matrix is ​​the energy carbon emission factor coefficient, and the elements of the energy carbon emission factor coefficient matrix are the energy carbon emission factor coefficients of the current row energy type number.

[0023] In some embodiments of the present disclosure, for each model of product in the enterprise production process within the target period, determining the total carbon emissions of a single product of this model in the production process according to the number of working hours used in each process of a single product of this model in the production process and the carbon emissions per working hour of each process includes:

[0024] Determine the manufacturing time matrix of the product according to the number of working hours used in each process during the production of a single product of the model;

[0025] According to the product manufacturing working hour matrix and the carbon emission matrix of each process single working hour, the production carbon emission matrix of each product model is determined, wherein the production carbon emission matrix of each product model is a column matrix, and the elements of the production carbon emission matrix of each product model are the total carbon emissions of a single product of this model in the production process during the target period.

[0026] In some embodiments of the present disclosure, determining the product manufacturing man-hour matrix according to the number of man-hours used in each process during the production process of a single product of the model includes:

[0027] The product manufacturing time matrix is ​​determined based on the process information, product model information and the total number of working hours required for a single product of the current model in the process within the target cycle, wherein the row information of the product manufacturing time matrix is ​​the product model information, the column information of the product manufacturing time matrix is ​​the process information, and the elements of the product manufacturing time matrix are the number of working hours used in the current column process during the production process of a single product of the current row product model.

[0028] In some embodiments of the present disclosure, for each order in the enterprise production process within the target period, determining the total carbon emissions of all products in the order during the production process according to the total carbon emissions of each model of a single product during the production process and the number of products of each model to be produced in the order includes:

[0029] Determine the product production order matrix based on the quantity of each model of product to be produced in each order of the enterprise's production process within the target period;

[0030] According to the product production order matrix and the production carbon emission matrix of each product model, the carbon emission matrix of each product production order is determined, wherein the carbon emission matrix of each product production order is a column matrix, and the elements of the carbon emission matrix of each product production order are the total carbon emissions of all products in each order during the target period in the production process.

[0031] In some embodiments of the present disclosure, determining the product production order matrix according to the quantity of each model of product to be produced in each order of the enterprise production process within the target period includes:

[0032] The product production order matrix is ​​determined based on the order number information within the target period, the product model information in the order, and the quantity information of products of different models in the order, wherein the row information of the product production order matrix is ​​the order number information, the column information of the product production order matrix is ​​the product model, and the elements of the product production order matrix are the quantity to be produced of products of the product model in this column in the current order.

[0033] In some embodiments of the present disclosure, the pre-established data relationship model of the carbon emissions of the enterprise's production process and the total carbon emissions of the enterprise includes:

[0034] According to the historical data of carbon emissions in the production process of the enterprise and the total carbon emissions of the enterprise, data fitting is performed to determine the data relationship model.

[0035] In some embodiments of the present disclosure, the data relationship model is a power function model.

[0036] According to another aspect of the present disclosure, there is provided a device for predicting carbon emissions of an enterprise, comprising:

[0037] A relational model building module is configured to pre-build a data relational model of carbon emissions during the enterprise production process and the total carbon emissions of the enterprise;

[0038] A first emission calculation module is configured to calculate the carbon emissions of the enterprise production process within a target period based on the order information;

[0039] The second emission determination module is configured to determine the total carbon emissions of the enterprise within the target period according to the carbon emissions of the enterprise's production process within the target period and the data relationship model.

[0040] According to another aspect of the present disclosure, there is provided a device for predicting carbon emissions of an enterprise, comprising:

[0041] a memory configured to store instructions;

[0042] The processor is configured to execute the instructions so that the enterprise carbon emission prediction device implements the enterprise carbon emission prediction method as described in any of the above embodiments.

[0043] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the enterprise carbon emission prediction method as described in any of the above embodiments is implemented.

[0044] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the enterprise carbon emission prediction method as described in any of the above embodiments is implemented.

[0045] The present disclosure can quickly, efficiently and accurately predict the carbon emissions of discrete manufacturing enterprises on a monthly, quarterly or annual basis based on order information. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present disclosure 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 of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 Schematic diagram of some embodiments of the enterprise carbon emission prediction method disclosed in the present invention.

[0048] Figure 2 This is a process breakdown diagram for predicting carbon emissions for discrete enterprises based on product orders in some embodiments of the present disclosure.

[0049] Figure 3 Schematic diagrams of other embodiments of the enterprise carbon emission prediction method disclosed in the present invention.

[0050] Figure 4 Schematic diagram of some embodiments of the enterprise carbon emission prediction device disclosed in the present invention.

[0051] Figure 5 Schematic diagram of the structure of other embodiments of the enterprise carbon emission prediction device disclosed in the present invention. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0053] The relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0054] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0055] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0056] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0057] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0058] Through research, the inventors found that by predicting the carbon emissions of discrete manufacturing enterprises, it is possible to effectively evaluate whether the enterprise's potential carbon emissions meet the emission requirements, evaluate the carbon emission quota compliance capabilities in advance, and prepare for the timely and full completion of carbon quota clearance. Therefore, the carbon emission prediction of discrete manufacturing enterprises has great research value.

[0059] In order to accurately calculate carbon emissions, relevant technical enterprises, factories or industrial parks conduct annual carbon emissions accounting for all greenhouse gas emission sources within the factory's operating scope, generally at the end of the accounting year or the beginning of the following year. Energy inventory data mainly based on electricity, steam, natural gas, gasoline, diesel, etc. are collected, and non-energy inventory data supplemented by welding shielding gas, refrigerants, etc. are collected. The total annual carbon emissions are calculated using the emission factor method.

[0060] Most of the prediction methods for the total carbon emissions of enterprises or factories use the historical carbon emission intensity reduction method, which forces the carbon emission intensity of previous years to be reduced to predict the total annual carbon emissions. For discrete enterprises with obvious annual output fluctuations, the total carbon emissions predicted based on output value and carbon emission intensity have the disadvantages of low accuracy and large deviation in prediction results.

[0061] Most of the carbon emission accounting and forecasting methods of related technologies are concentrated on methods based on statistical models, economic models, energy models and machine learning models. For forecasting methods based on historical data, commonly used statistical models are highly dependent on historical data; forecasting models based on economic theories and economic relationships consider the impact of factors such as the decline in carbon emission intensity and the growth of output value on carbon emissions, which often oversimplifies the actual situation and ignores the role of non-economic factors such as technical factors.

[0062] A carbon accounting prediction method of related technology analyzes electricity carbon accounting parameters and conducts model verification based on historical parameters, but does not have the ability to predict carbon accounting for discrete manufacturing companies.

[0063] Another related technology is based on the statistical information of the main energy consumption of a region or enterprise. It calculates the final carbon emissions after detailed decomposition of historical energy data. It cannot predict the total carbon emissions in the future.

[0064] Through research, the inventors found that the relevant technologies for carbon emission accounting and carbon emission forecasting for provinces, cities, parks, factories and other regions have the following problems.

[0065] (1) The total carbon emissions forecast based on historical data is essentially to use the method of analyzing historical energy consumption data to calculate the total carbon emissions for the accounting year based on the emissions in the past few years, or to convert the average emission level in the past few years based on the company's own output, output value and other information, and then calculate the total carbon emissions for the accounting year. Related technologies Such methods are based on years of accounting experience and accounting data. The carbon emissions forecast for a period of time in the future through the total carbon emissions or carbon emission intensity are greatly affected by production capacity fluctuations, the prediction results are low in accuracy, and the guidance ability for energy conservation and carbon reduction is weak.

[0066] (2) Establish a multi-attribute influencing factor model and a carbon emission prediction method based on a data-driven machine learning model. By analyzing carbon emissions over the past years, a big data model is trained to predict future carbon emissions. This type of method in related technologies overly relies on a large amount of accurate historical carbon emission data. Carbon emissions are affected by many factors, some of which have complex nonlinear relationships, and big data machine learning models cannot capture such complex relationships. At the same time, the carbon emission results predicted by black box models lack reasonable interpretability, which will limit the understanding and trust of the prediction results.

[0067] In view of at least one of the above technical problems, the present disclosure provides a method and device for predicting carbon emissions of an enterprise, a computer program product and a storage medium. The present disclosure is described below through specific embodiments.

[0068] Figure 1 Schematic diagram of some embodiments of the enterprise carbon emission prediction method disclosed in the present invention. Preferably, this embodiment can be executed by the enterprise carbon emission prediction device disclosed in the present invention. Figure 1 As shown, Figure 1 The method of the embodiment may include at least one of steps 1 to 3.

[0069] Step 1: Pre-establish a data relationship model between the carbon emissions of the enterprise's production process and the total carbon emissions of the enterprise.

[0070] In some embodiments of the present disclosure, the carbon emissions from the enterprise's production process may be the total carbon emissions from the product manufacturing process, or the carbon emissions caused by the main energy consumption in the production process.

[0071] In some embodiments of the present disclosure, the total carbon emissions of an enterprise may be the total carbon dioxide emissions within the enterprise's operating boundary, or a total carbon emissions relationship of the enterprise.

[0072] In some embodiments of the present disclosure, the carbon dioxide emissions are carbon dioxide emission equivalents, which include common greenhouse gases such as carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride and nitrogen trifluoride. The carbon dioxide equivalent is obtained by multiplying the greenhouse gas with the corresponding global warming potential.

[0073] In some embodiments of the present disclosure, step 1 may include: constructing a carbon emission conversion model, establishing a data relationship model between the total carbon emissions of the product production and manufacturing process and the total carbon dioxide emissions within the enterprise's operating boundaries, that is, a relationship model between the carbon emissions caused by the main energy consumption in the production process and the total carbon emissions of the enterprise, which is used to form a functional relationship between the total carbon emissions and the carbon emissions of the main energy.

[0074] In some embodiments of the present disclosure, step 1 may include: performing data fitting based on historical data of carbon emissions from the enterprise's production process and the enterprise's total carbon emissions to determine the data relationship model.

[0075] In some embodiments of the present disclosure, the data relationship model is a power function model.

[0076] In some embodiments of the present disclosure, step 1 may include at least one of steps 11 to 13.

[0077] Step 11: input historical data, wherein the historical data may include annual carbon emission source inventory data and carbon emission factor data.

[0078] Step 12: Calculate carbon emissions.

[0079] In some embodiments of the present disclosure, carbon emissions include carbon emissions from the product manufacturing process and the total carbon dioxide emissions within the enterprise's operating boundaries. The carbon emissions from the production process are the direct or indirect carbon emissions caused by the consumption and use of energy such as electricity, natural gas, steam, acetylene, propane, diesel, gasoline, etc. required for product manufacturing, and the total carbon dioxide emissions within the discrete factory operating boundary include all energy, non-energy consumption and direct carbon emissions required for activities. Input carbon emission sources and data for no less than five years, and calculate the carbon emissions from the product manufacturing process and the total carbon emissions of the enterprise on an annual basis in combination with carbon emission factors.

[0080] Step 13, fitting function.

[0081] In some embodiments of the present disclosure, step 13 may include: the total carbon emissions generated by the enterprise's operating scope have a nonlinear relationship with the total carbon emissions generated by the energy consumed in the product production process. Based on the generated data on carbon emissions from the product production process over the years and the total carbon emissions of the enterprise, a data relationship model between the total carbon emissions data of the product production process and the total carbon dioxide emissions within the enterprise's operating boundary is established using the least squares method, as shown in formula (1).

[0082] A GHG =aM GHG b +c (1)

[0083] In formula (1), A GHG M is the total amount of carbon dioxide emissions within the enterprise’s operating boundary; GHG is the carbon emission in the product manufacturing process, a, b, c are power function constants.

[0084] Step 2: Calculate the carbon emissions of the enterprise’s production process within the target period based on order information.

[0085] In some embodiments of the present disclosure, the target period may be a forecast month, quarter, or year.

[0086] In some embodiments of the present disclosure, step 2 may include: constructing a main model of production energy consumption and carbon emissions, wherein the main model of production energy consumption and carbon emissions is composed of a product production order model (matrix set A), a production manufacturing process model (matrix set B), a product manufacturing time model (matrix set C) and an energy carbon emission factor coefficient library (matrix set D); performing operations on matrices such as the product production order model, the product manufacturing process model, the product manufacturing time model and the energy carbon emission factor coefficient library to calculate the total estimated carbon emissions of all current orders to be produced.

[0087] In some embodiments of the present disclosure, step 2 may include at least one of steps 21 to 23. The above embodiments of the present disclosure can gradually and accurately calculate the carbon emissions of the enterprise production process through the dimensions of a single product (step 21), a single order (step 22), and the sum of all orders (step 23).

[0088] Step 21, for each model of product in the enterprise's production process within the target period, calculate the total carbon emissions of a single product of that model in the production process.

[0089] In some embodiments of the present disclosure, step 21 may include at least one of steps 211 to 212. The above embodiments of the present disclosure accurately determine the carbon emissions of a single product production process through the dimensions of a single process (step 211) and a single product (step 212).

[0090] Step 211, for each process of the enterprise's production process within the target period, determine the carbon emissions per working hour of the process based on the usage of each energy source per working hour of the process and the energy carbon emission factor coefficient of each energy source.

[0091] In some embodiments of the present disclosure, step 211 may include at least one step from step 2111 to step 2113 .

[0092] Step 2111, determine the process matrix based on the usage of each energy source in each process of the enterprise's production process within the target period.

[0093] In some embodiments of the present disclosure, step 2111 may include: determining the process matrix based on the process information, energy information and the usage of various energy corresponding to different processes within the target cycle, wherein the row information of the process matrix is ​​the process information, the column information of the process matrix is ​​the energy information, and the elements of the process matrix are the usage of the energy in the column in a single working period in the current row process.

[0094] In some embodiments of the present disclosure, step 2111 may include: constructing a product manufacturing process model.

[0095] In some embodiments of the present disclosure, the manufacturing of products by discrete enterprises includes multiple process steps, and the product manufacturing process model includes process information, energy information, and various energy usage corresponding to different processes, wherein the manufacturing process information can be represented in the form of a serial number or by a specific process name. The energy information includes all energy used by the product in the production process of the production process, including but not limited to electricity, natural gas, acetylene, propane, heat, gasoline, diesel, etc. The energy usage is the total amount of various energy used under a single process in a single working hour.

[0096] In some embodiments of the present disclosure, step 2111 may also include: obtaining annual energy consumption, carbon emission source inventory data and process working hour data in recent years, establishing a product manufacturing process information database, and forming a determinant matrix with manufacturing process as row information and energy type as column information. The "element" (element) of the matrix represents the energy usage of the column in a single working hour in the current row manufacturing process, wherein the energy usage of one or more manufacturing process processes is greater than or equal to zero, as shown in formula (2).

[0097]

[0098] In formula (2): B mn b is the product manufacturing process matrix collection entity; b mn is the consumption of n energy types in m processes per single working hour; m is the serial number of the product manufacturing process in the matrix; n is the serial number of the energy type in the matrix.

[0099] In some embodiments of the present disclosure, for the process numbers, different process categories include multiple process numbers, such as multiple cutting processes for different plate thicknesses or multiple assembly processes for different component installation processes.

[0100] Figure 2 This is a process breakdown diagram of discrete enterprise carbon emission prediction based on product orders in some embodiments of the present disclosure. Figure 2 As shown, order A can be divided into multiple (n) process categories B1, B2, ..., B n Each process category can be divided into multiple process links. For example, process category B1 can be divided into multiple (m) process links B1C1, B1C2, ..., B1C m .

[0101] Step 2112, determining the energy carbon emission factor coefficient matrix according to the energy carbon emission factor coefficient of each energy source in the enterprise production process within the target period.

[0102] In some embodiments of the present disclosure, the energy carbon emission factor coefficient matrix is ​​a column matrix, the row information of the energy carbon emission factor coefficient matrix is ​​the energy carbon emission factor coefficient, and the elements of the energy carbon emission factor coefficient matrix are the energy carbon emission factor coefficients of the current row energy type number.

[0103] In some embodiments of the present disclosure, step 2112 may include: constructing an energy carbon emission factor coefficient library. Various energy sources used by discrete enterprises in the product manufacturing process are converted into carbon emission equivalents through carbon emission coefficients, and the product of energy consumption and carbon emission coefficients is carbon dioxide equivalent.

[0104] In some embodiments of the present disclosure, step 2112 may also include: establishing an energy carbon emission factor coefficient model to form a column matrix with the energy carbon emission factor coefficient as row information, and the "element" of the matrix (matrix set D) represents the energy carbon emission coefficient corresponding to the unit energy of the current row, as shown in formula (3).

[0105]

[0106] In formula (3), D is the energy carbon emission factor coefficient matrix set; d n is the energy carbon emission factor coefficient of energy type n; n is the sequence number of the energy type in the matrix.

[0107] In some embodiments of the present disclosure, the product of the energy carbon emission factor coefficient and the energy is the carbon dioxide equivalent emissions caused by the current energy use.

[0108] Step 2113, determine the carbon emission matrix per working hour for each process based on the process matrix and the energy carbon emission factor coefficient matrix, wherein the carbon emission matrix per working hour for each process is a column matrix, and the elements of the carbon emission matrix per working hour for each process in the enterprise's production process within the target period.

[0109] In some embodiments of the present disclosure, step 2113 may include: calculating the carbon emissions per working hour of each process.

[0110] In some embodiments of the present disclosure, step 2113 may include: multiplying the product manufacturing process model with the energy carbon emission factor coefficient library to obtain the carbon emissions generated by the processes involved in product production per unit working hour, and the carbon emission matrix per unit working hour of each process is shown in formulas (4) and (5).

[0111]

[0112] In formula (4), E is the aggregate matrix of carbon emissions per working hour of each process; e mis the total carbon emissions generated by energy use in process m per working hour; m is the product manufacturing process number in the matrix.

[0113] Step 212, for each model of product in the enterprise's production process within the target period, determine the total carbon emissions of a single product of this model in the production process based on the number of working hours used in each process during the production process of a single product of this model and the carbon emissions per working hour of each process.

[0114] In some embodiments of the present disclosure, step 212 may include at least one of steps 2121 to 2122 .

[0115] Step 2121, determining the product manufacturing man-hour matrix according to the man-hours used in each process during the production of a single product of this model.

[0116] In some embodiments of the present disclosure, step 2121 may include: determining the product manufacturing work time matrix based on the process information, product model information and the total number of working hours required for a single product of the current model in the process within the target cycle, wherein the row information of the product manufacturing work time matrix is ​​the product model information, the column information of the product manufacturing work time matrix is ​​the process information, and the elements of the product manufacturing work time matrix are the number of working hours used in the current column process during the production process of a single product of the current row product model.

[0117] In some embodiments of the present disclosure, step 2121 may include: constructing a product manufacturing time model. The manufacturing process of each series of products of a discrete enterprise includes accurate processes and time. The product manufacturing time model includes process information, product model series information, and the total number of time required for a single product of the current model in the process.

[0118] In some embodiments of the present disclosure, step 2121 may include: establishing a product manufacturing time model to form a determinant matrix with product model as row information and manufacturing process as column information, and the "element" of the matrix represents the total number of hours in the manufacturing process for producing a single product in the current row product series, as shown in formula (6).

[0119]

[0120] In formula (6), C is the main body of the product manufacturing time matrix; c jm is the number of working hours used in process m for product model j to be produced; j is the product model number in the matrix; m is the product manufacturing process number in the matrix.

[0121] Step 2122, determine the production carbon emission matrix of each product model based on the product manufacturing time matrix and the single process time carbon emission matrix, wherein the production carbon emission matrix of each product model is a column matrix, and the elements of the production carbon emission matrix of each product model are the total carbon emissions of a single product of this model in the production process during the target period.

[0122] In some embodiments of the present disclosure, step 2122 may include: multiplying the product manufacturing time model with the carbon emission matrix of each process single time to obtain the carbon emissions generated by each product series model in the production process, and the production carbon emission matrix of each product model is shown in formulas (7) and (8).

[0123]

[0124] In formulas (7) and (8): F is the aggregate entity of the production carbon emission matrix of each product model; f j is the total carbon emissions generated during the manufacturing process of a single unit of product series model j; j is the product model number in the matrix.

[0125] Step 22, for each order in the enterprise's production process within the target period, determine the total carbon emissions of all products in the order during the production process based on the total carbon emissions of each model of a single product during the production process and the number of products of each model to be produced in the order.

[0126] In some embodiments of the present disclosure, in some embodiments of the present disclosure, step 22 may include at least one step from step 221 to step 222.

[0127] Step 221, determining a product production order matrix according to the quantity to be produced of each model of product in each order of the enterprise production process within the target period.

[0128] In some embodiments of the present disclosure, step 221 may include: determining the product production order matrix based on the order number information within the target period, the product model information in the order, and the quantity information of products of different models in the order, wherein the row information of the product production order matrix is ​​the order number information, the column information of the product production order matrix is ​​the product model, and the elements of the product production order matrix are the quantity to be produced of products of the product model in this column in the current order.

[0129] In some embodiments of the present disclosure, step 221 may include: constructing a product production order model. A discrete enterprise has one or more product production orders within a certain period of time, and the product production order model includes order number information, product model series information within the order, and quantity information of products of different models within the order.

[0130] In some embodiments of the present disclosure, step 221 may also include: establishing a product production order information database, forming a determinant matrix with order serial number as row information and product model as column information, the "element" of the matrix represents the number of products to be produced of the column model in the current order, one order or multiple orders, in which the number of products to be produced of a single model is greater than or equal to zero. The introduced single time period product production order matrix is ​​as shown in formula (9).

[0131]

[0132] In formula (9): A ij is the product production order matrix collection entity; a ij is the number of products to be produced for product model j in order i; i is the order number in the matrix; j is the product model number in the matrix.

[0133] Step 222, based on the product production order matrix and the production carbon emission matrix of each product model, determine the carbon emission matrix of each product production order, wherein the carbon emission matrix of each product production order is a column matrix, and the elements of the carbon emission matrix of each product production order are the total carbon emissions of all products in each order during the target period during the production process.

[0134] In some embodiments of the present disclosure, step 222 may include: calculating the carbon emissions of product production orders within the target period according to formulas (10) and (11).

[0135]

[0136] In formulas (10) and (11), G is the aggregate entity of the carbon emission matrix of production orders of each product in the period; g i is the total carbon emissions generated by all products in order i during the manufacturing process; i is the order number in the matrix.

[0137] Step 23, determining the carbon emissions of the enterprise's production process within the target period according to the total carbon emissions of all products in the production process of each order of the enterprise's production process within the target period.

[0138] In some embodiments of the present disclosure, the carbon emissions from the enterprise's production process may be carbon emissions from the product manufacturing process.

[0139] In some embodiments of the present disclosure, step 23 may include: calculating the carbon emissions of product production orders within the target period.

[0140] In some embodiments of the present disclosure, step 23 may include: adding the total number of orders to be produced within the target period to obtain a prediction result of the total carbon emissions of the production process.

[0141] In some embodiments of the present disclosure, the carbon emissions from the product manufacturing process during the target period M GHG It can be expressed as shown in formula (12).

[0142]

[0143] In some embodiments of the present disclosure, formula (10) can be converted into formula (13).

[0144]

[0145] In some embodiments of the present disclosure, step 23 may include: according to formulas (13) and (12), by performing operations on matrices such as product production order model, product manufacturing process model, product manufacturing time model and energy carbon emission factor coefficient library, the total carbon emission amount M of all current pending production orders is calculated. GHG .

[0146] Step 3: Determine the total carbon emissions of the enterprise within the target period according to the carbon emissions of the enterprise's production process within the target period and the data relationship model.

[0147] In some embodiments of the present disclosure, the total carbon emissions of an enterprise may be the total carbon dioxide emissions within the operating boundary of the enterprise.

[0148] In some embodiments of the present disclosure, step 3 may include: substituting the carbon emissions of the product manufacturing process within the cycle into formula (1) to obtain the total carbon dioxide emission forecast value A within the enterprise operation boundary: GHG .

[0149] In some embodiments of the present disclosure, step 3 may include: using a carbon emission conversion model to calculate the total carbon emissions A within the discrete enterprise operation boundary GHG .

[0150] The above-mentioned embodiments of the present disclosure disclose a method and device for predicting carbon emissions of discrete manufacturing enterprises. The technical solutions of the above-mentioned embodiments of the present disclosure include modules such as a carbon emission conversion function model, a main model of production energy consumption and carbon emissions, and a calculation system, which are mainly used for predicting the total carbon emissions of discrete manufacturing enterprises within an annual period.

[0151] The above embodiment of the present disclosure discloses a method and device for predicting enterprise carbon emissions for discrete manufacturing. First, the above embodiment of the present disclosure establishes a data relationship model between the total carbon emissions of the product production and manufacturing process and the total carbon dioxide emissions within the operating boundary of the enterprise or factory, and decomposes the production orders of multiple time periods in the enterprise according to the product sequence through the production model, and obtains product orders and product model series information; at the same time, the above embodiment of the present disclosure builds a main production energy consumption model, covering product production order information, product manufacturing process information, product manufacturing working hours information and energy carbon emission information, splits the production order into independent model product production information, and decomposes the production process of the same model product; at the same time, the above embodiment of the present disclosure can obtain each granularity production link step by step, construct single working hour energy consumption information, and classify and count the energy and non-energy consumption information used in the production and manufacturing of electricity, natural gas, steam, protective gas, etc. involved in the production link and store it, and combine the emission factors of each carbon emission source to calculate the expected carbon emissions generated by the production order to be produced.

[0152] By calculating carbon emissions, the above embodiments of the present disclosure can understand carbon emissions, assess corporate environmental impacts, and stimulate and encourage companies to adopt environmentally friendly and sustainable business models, take energy-saving and emission-reduction measures, and promote low-carbon transformation and sustainable development through carbon quota compliance. Through the annual accounting of the above embodiments of the present disclosure, the energy and non-energy carbon emission sources generated by factory production activities and other production auxiliary activities can be counted, and various greenhouse gases can be accounted for using the carbon emission factor method.

[0153] The above-mentioned embodiments of the present disclosure provide a method for predicting carbon emissions of discrete manufacturing enterprises. By analyzing the characteristics and quantity of product production orders, the carbon emissions of discrete enterprises can be more accurately evaluated and predicted, which is helpful to formulate more effective carbon emission reduction measures and effectively avoids the problem of inaccurate predictions of conventional methods caused by fluctuations in the production capacity of discrete enterprises.

[0154] Figure 3 Schematic diagram of other embodiments of the enterprise carbon emission prediction method disclosed in the present invention. Preferably, this embodiment can be executed by the enterprise carbon emission prediction device disclosed in the present invention. Figure 3 As shown, Figure 3 The method of the embodiment may include at least one of steps 301 to 305 .

[0155] Step 301, inputting energy consumption and carbon emission data of recent years into the carbon emission conversion model, product manufacturing process model and product manufacturing man-hour model.

[0156] In some embodiments of the present disclosure, the carbon emission conversion model may be a data relationship model between the carbon emissions of an enterprise's production process and the total carbon emissions of the enterprise.

[0157] Step 302, pre-establishing a data relationship model between the carbon emissions of the enterprise's production process and the total carbon emissions of the enterprise.

[0158] In some embodiments of the present disclosure, step 302 may be implemented as follows: Figure 1 Step 1 of the embodiment.

[0159] In some embodiments of the present disclosure, step 302 may include: establishing a data relationship model between the total carbon emissions of a product manufacturing process and the total carbon emissions of discrete factories.

[0160] Step 303, constructing a main model of carbon emissions from energy consumption in product production, wherein the main model of carbon emissions from energy consumption in product production is composed of a product production order model, a production manufacturing process model, a product manufacturing man-hour model and an energy carbon emission coefficient library.

[0161] Step 304, perform data calculation and storage.

[0162] In some embodiments of the present disclosure, step 304 may include: performing operations through matrices such as product production order model, product manufacturing process model, product manufacturing time model and energy carbon emission factor coefficient library to calculate the total estimated carbon emissions of all current orders to be produced.

[0163] In some embodiments of the present disclosure, steps 303 and 304 may be implemented as follows: Figure 1 Step 2 of the embodiment.

[0164] Step 305, predicting the carbon emissions of the factory in future time periods.

[0165] In some embodiments of the present disclosure, step 305 may be implemented as follows: Figure 1 Step 3 of the embodiment.

[0166] The above-mentioned embodiments of the present disclosure propose a method and device for predicting carbon emissions of enterprises for discrete manufacturing, which can accurately calculate the carbon emissions generated by the enterprise's operating activities, make detailed predictions on the annual carbon emissions of the enterprise, formulate emission reduction measures in a timely manner, adjust and optimize the energy structure, provide data support for over-quota carbon emission defense measures and make forward-looking decisions, and ultimately achieve smooth compliance with carbon quotas.

[0167] The above embodiments of the present disclosure can predict carbon emissions based on the enterprise's product capacity and order volume, combined with carbon-related emission sources such as electricity, natural gas, diesel, gasoline, steam, etc. The above embodiments of the present disclosure can be applied to the annual carbon quota compliance and dual-carbon path planning of enterprises.

[0168] The present disclosure is described below through specific embodiments.

[0169] Taking the quarterly production orders of a machinery manufacturing enterprise as an example, the total quarterly carbon emissions of the discrete factory are analyzed and predicted. Combined with the carbon emissions and energy consumption data of the past two years, the total carbon emissions of the enterprise in the next quarter are predicted.

[0170] (1) Fitting function.

[0171] The total carbon emissions generated by the company's operations in the past two years and the total carbon emissions generated by energy consumption in the product production process are shown in Table 1.

[0172] Table 1

[0173]

[0174] The total carbon emissions data of the product production process and the function model of the total carbon dioxide emissions within the enterprise's operating boundary are as follows.

[0175] A GHG =4.48939M GHG 0.84106 -562.529

[0176] (2) Product order information is shown in Table 2.

[0177] Table 2

[0178]

[0179] Establish product production order model A.

[0180]

[0181] (3) The energy consumption information of the product process is shown in Table 3.

[0182] Table 3

[0183]

[0184]

[0185] Establish product manufacturing process model B.

[0186]

[0187] (4) Product manufacturing time information is shown in Table 4.

[0188] Table 4

[0189]

[0190] Establish product manufacturing time model C.

[0191]

[0192] (5) The energy carbon emission factor coefficient library is shown in Table 5.

[0193] Table 5

[0194]

[0195] Establish energy carbon emission factor coefficient library D.

[0196]

[0197] The energy carbon emission factor coefficient is calculated using the latest carbon emission factor and greenhouse gas global warming potential.

[0198] (6) Total carbon emissions from product production orders during the period.

[0199]

[0200] (7) Substitute the carbon emissions MGHG of the product production and manufacturing process within the cycle into the fitting function to obtain the predicted value of the total carbon dioxide emissions within the enterprise's operating boundary.

[0201] A GHG =4.48939M GHG 0.84106 -562.529 = 3632 kgCO2e

[0202] The use of the prediction model and method of the above-mentioned embodiments of the present disclosure can significantly shorten the evaluation time, discover potential high-carbon emission hotspot processes, help discrete enterprises to achieve accurate management and control of carbon emissions, improve production efficiency and resource utilization, and reduce energy consumption and carbon emissions.

[0203] In the above-mentioned embodiments of the present disclosure, the prediction time is advanced to avoid the cost increase caused by the inability to fulfill the requirements such as carbon quota.

[0204] Figure 4 Schematic diagram of some embodiments of the enterprise carbon emission prediction device disclosed in the present invention. Figure 4 As shown, the enterprise carbon emission prediction device disclosed in the present invention may include a relationship model building module 41 , a first emission amount calculation module 42 and a second emission amount determination module 43 .

[0205] The relationship model building module 41 is configured to pre-build a data relationship model between the carbon emissions of the enterprise's production process and the total carbon emissions of the enterprise.

[0206] In some embodiments of the present disclosure, the relationship model building module 41 may be configured to perform data fitting based on historical data of carbon emissions from the enterprise's production process and the enterprise's total carbon emissions to determine the data relationship model.

[0207] In some embodiments of the present disclosure, the data relationship model may be a power function model.

[0208] The first emission calculation module 42 is configured to calculate the carbon emissions of the enterprise's production process within the target period based on the order information.

[0209] In some embodiments of the present disclosure, the first emission calculation module 42 can be configured to calculate the total carbon emissions of a single product of each model in the production process of the enterprise within the target period; for each order in the enterprise's production process within the target period, determine the total carbon emissions of all products in the order during the production process based on the total carbon emissions of a single product of each model in the production process and the number of products of each model to be produced in the order; determine the carbon emissions of the enterprise's production process within the target period based on the total carbon emissions of all products in each order in the enterprise's production process within the target period.

[0210] In some embodiments of the present disclosure, the first emission calculation module 42 can be configured to determine the carbon emissions per working hour of each process in the enterprise production process within the target period based on the usage of each energy per working hour of the process and the energy carbon emission factor coefficient of each energy, when calculating the total carbon emissions of a single product of this model in the production process for each product model in the enterprise production process within the target period; for each product model in the enterprise production process within the target period, determine the total carbon emissions of a single product of this model in the production process based on the number of working hours used for each process of the single product of this model in the production process and the carbon emissions per working hour of each process.

[0211] In some embodiments of the present disclosure, the first emission calculation module 42 can be configured to determine a process matrix based on the amount of each energy used by each process in the enterprise production process within the target period; determine an energy carbon emission factor coefficient matrix based on the energy carbon emission factor coefficient of each energy in the enterprise production process within the target period; and determine a carbon emission matrix for each process per working hour based on the process matrix and the energy carbon emission factor coefficient matrix, wherein the carbon emission matrix for each process per working hour is a column matrix, and the elements of the carbon emission matrix for each process per working hour are the carbon emissions for each process in the enterprise production process within the target period.

[0212] In some embodiments of the present disclosure, when the first emission calculation module 42 determines the process matrix based on the usage of each energy in each process of the enterprise's production process within a target period, it can be configured to determine the process matrix based on the process information, energy information and the usage of various energy corresponding to different processes within the target period, wherein the row information of the process matrix is ​​the process information, the column information of the process matrix is ​​the energy information, and the elements of the process matrix are the usage of the energy in the column during a single working period in the current row process.

[0213] In some embodiments of the present disclosure, the energy carbon emission factor coefficient matrix is ​​a column matrix, the row information of the energy carbon emission factor coefficient matrix is ​​the energy carbon emission factor coefficient, and the elements of the energy carbon emission factor coefficient matrix are the energy carbon emission factor coefficients of the current row energy type number.

[0214] In some embodiments of the present disclosure, the first emission calculation module 42 determines the total carbon emissions of a single product of this model in the production process according to the number of working hours used in each process of a single product of this model in the production process and the carbon emissions per working hour of each process for each product model in the enterprise's production process within the target period. The module can be configured to determine the product manufacturing working hour matrix according to the number of working hours used in each process of a single product of this model in the production process; and determine the production carbon emission matrix of each product model according to the product manufacturing working hour matrix and the carbon emission matrix of each process per working hour, wherein the production carbon emission matrix of each product model is a column matrix, and the elements of the production carbon emission matrix of each product model are the total carbon emissions of a single product of this model in the production process within the target period.

[0215] In some embodiments of the present disclosure, when the first emission calculation module 42 determines the product manufacturing time matrix according to the number of working hours used in each process during the production of a single product of the model, it can be configured to determine the product manufacturing time matrix according to the process information, product model information and the total number of working hours required for a single product of the current model in the process during the target period, wherein the row information of the product manufacturing time matrix is ​​the product model information, the column information of the product manufacturing time matrix is ​​the process information, and the elements of the product manufacturing time matrix are the number of working hours used in the current column process during the production of a single product of the current row product model.

[0216] In some embodiments of the present disclosure, the first emission calculation module 42 determines the total carbon emissions of all products in the production process of an order according to the total carbon emissions of each model of a single product in the production process and the number of products of each model in the order to be produced for each order in the enterprise production process within the target period. The module can be configured to determine a product production order matrix based on the number of products of each model to be produced in each order in the enterprise production process within the target period; determine a carbon emission matrix for each product production order based on the product production order matrix and the carbon emission matrix for each product model, wherein the carbon emission matrix for each product production order is a column matrix, and the elements of the carbon emission matrix for each product production order are the total carbon emissions of all products in each order in the target period.

[0217] In some embodiments of the present disclosure, when the first emission calculation module 42 determines the product production order matrix according to the quantity to be produced of each model product in each order of the enterprise's production process within a target period, it can be configured to determine the product production order matrix according to the order number information within the target period, the product model information in the order, and the quantity information of products of different models in the order, wherein the row information of the product production order matrix is ​​the order number information, the column information of the product production order matrix is ​​the product model, and the elements of the product production order matrix are the quantity to be produced of products of the product model in this column in the current order.

[0218] The second emission determination module 43 is configured to determine the total carbon emissions of the enterprise within the target period according to the carbon emissions of the enterprise's production process within the target period and the data relationship model.

[0219] In some embodiments of the present disclosure, the enterprise carbon emission prediction device of the present disclosure may also be configured to execute the enterprise carbon emission prediction method described in any of the above embodiments of the present disclosure.

[0220] The above-mentioned embodiments of the present disclosure provide a method and device for predicting carbon emissions of enterprises for discrete manufacturing, which solves the problem that discrete manufacturing enterprises and factories cannot accurately predict the total amount of carbon emissions due to fluctuations in product output, and at the same time solves the problem that the granularity of enterprise carbon emission accounting is insufficient, it is difficult to find carbon emission hotspots, and it is impossible to accurately implement emission reduction measures. The method of the above-mentioned embodiments of the present disclosure first uses the least squares method to fit the nonlinear relationship curve between the total amount of carbon emissions generated by the energy consumption in the product production and manufacturing process and the total amount of carbon emissions generated by the enterprise's operating scope, and then establishes a production energy consumption carbon emission main model through a combination of a product production order model, a production manufacturing process model, a product manufacturing time model, and an energy carbon emission coefficient model, and obtains energy carbon emission inventory data based on the smallest particle production process link. The above-mentioned embodiments of the present disclosure obtain carbon emission data integrating energy type and process type through a multi-dimensional summary method. The above-mentioned embodiments of the present disclosure can quickly, efficiently and accurately predict carbon emissions within a monthly, quarterly or annual range.

[0221] Figure 5 Schematic diagram of the structure of some other embodiments of the enterprise carbon emission prediction device disclosed in the present invention. Figure 5 As shown, the enterprise carbon emission prediction device includes a memory 51 and a processor 52.

[0222] The memory 51 is used to store instructions, the processor 52 is coupled to the memory 51, and the processor 52 is configured to execute the enterprise carbon emission prediction method described in any of the above embodiments of the present disclosure based on the instructions stored in the memory.

[0223] like Figure 5 As shown, the enterprise carbon emission prediction device also includes a communication interface 53 for information exchange with other devices. At the same time, the enterprise carbon emission prediction device also includes a bus 54, through which the processor 52, the communication interface 53, and the memory 51 communicate with each other.

[0224] The memory 51 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory. The memory 51 may also be a memory array. The memory 51 may also be divided into blocks, and the blocks may be combined into virtual volumes according to certain rules.

[0225] In addition, the processor 52 may be a central processing unit (CPU), or may be an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present disclosure.

[0226] The above-mentioned embodiments of the present disclosure provide a method and system for predicting enterprise carbon emissions for discrete manufacturing. By establishing a carbon emission conversion model and a main model of carbon emissions from production energy consumption, the production process is split into a production process link tree containing single-hour energy consumption information, and carbon-related energy and carbon-related non-energy are counted in each link. Each granular production link adopts standardized processing of working hours, and finally obtains the total amount of all energy and non-energy data under the current order, and then converts the total carbon emissions of the production process and the total carbon emissions within the scope of enterprise operations. In addition to the technical solutions provided by the above-mentioned embodiments of the present disclosure, the present disclosure can also incorporate carbon emission data generated by non-production behaviors such as public facilities and office areas of discrete enterprises into the accounting model to further improve the accuracy of carbon emission prediction.

[0227] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the enterprise carbon emission prediction method as described in any of the above embodiments is implemented.

[0228] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the enterprise carbon emission prediction method as described in any of the above embodiments of the present disclosure is implemented.

[0229] In some embodiments of the present disclosure, the computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0230] The storage medium of the above-mentioned embodiment of the present disclosure can conveniently store and manage carbon emission data, facilitate enterprises to analyze and evaluate carbon emissions, and provide a reference for formulating carbon emission reduction plans.

[0231] Through research, the inventors found that the carbon emission accounting methods of related technologies are mainly concentrated on traditional mathematical models such as statistical models, economic models and energy models, and prediction methods based on historical data. The related technical methods have technical problems such as single influencing factors, rough predictions, and low accuracy. They are predictions that deviate from the facts in order to meet policy measures. They are forced to reduce the annual intensity and cannot fully consider other important influencing factors that may be strongly correlated.

[0232] Compared with the related technology, the above-mentioned embodiments of the present disclosure relate to a method and device for predicting enterprise carbon emissions for discrete manufacturing. The above-mentioned embodiments of the present disclosure bring corresponding technical improvements and corresponding technical effects in terms of accounting methods, accounting processes, application scenarios, etc.

[0233] (1) The above embodiments of the present disclosure establish a carbon emission conversion model and a main model of production energy consumption carbon emissions, modularize the carbon accounting process of discrete enterprises, make the accounting process clearer, and make the accounting more accurate.

[0234] (2) The carbon emission conversion model of the above-mentioned embodiment of the present disclosure is a carbon emission accounting method that relates the carbon emissions of the production and manufacturing process and the total carbon emissions of the enterprise and is highly consistent with the operating characteristics of the enterprise. It organically combines the carbon emissions contributed by production and manufacturing that are easy to statistically calculate with the total carbon emissions within the operating boundary, while taking into account the necessary carbon emissions generated by the basic operations of the enterprise.

[0235] (3) The production energy consumption model of the above-mentioned embodiment of the present invention includes a product production order model, a product manufacturing process model, a product manufacturing working hour model and an energy carbon emission coefficient library. According to the minimum granularity single working hour energy consumption of each production process, the carbon emissions of the factory production and manufacturing process are classified and counted, so as to achieve the ability to quickly predict the total monthly, quarterly or annual carbon emissions based on the production order situation, and can accurately predict and warn the carbon emissions of discrete enterprises.

[0236] (4) The above-mentioned embodiments of the present disclosure utilize computer technology to form an enterprise carbon emission accounting and prediction system. The above-mentioned embodiments of the present disclosure system stores all production models, establishes an energy consumption database that can be automatically updated, and inputs product order information to automatically calculate the contribution value of the production order to the total carbon emissions of the discrete enterprise.

[0237] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, devices, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0238] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0239] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0240] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0241] The enterprise carbon emission prediction device, relationship model building module, first emission calculation module and second emission determination module described above can be implemented as a general-purpose processor, programmable logic controller (PLC), digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component or any appropriate combination thereof for performing the functions described in this application.

[0242] So far, the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0243] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by instructing the relevant hardware through a program, and the program may be stored in a non-transitory computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0244] The description of the present disclosure is given for the purpose of illustration and description, and is not intended to be exhaustive or to limit the present disclosure to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present disclosure, and to enable those of ordinary skill in the art to understand the present disclosure and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A method for predicting corporate carbon emissions, comprising: Establish in advance a data relationship model between the carbon emissions of the enterprise's production process and the total carbon emissions of the enterprise; Based on order information, calculate the carbon emissions of the enterprise's production process within the target period; The total carbon emissions of the enterprise within the target period are determined according to the carbon emissions of the enterprise's production process within the target period and the data relationship model.

2. The method for predicting corporate carbon emissions according to claim 1, wherein: The calculation of the carbon emissions of the enterprise production process within the target period based on the order information includes: For each model of product in the enterprise's production process within the target period, calculate the total carbon emissions of a single product of that model in the production process; For each order in the enterprise's production process within the target period, the total carbon emissions of all products in the order during the production process are determined based on the total carbon emissions of each model of the product during the production process and the number of products of each model to be produced in the order; The carbon emissions of the enterprise's production process within the target period are determined based on the total carbon emissions of all products in the production process of each order in the enterprise's production process within the target period.

3. The method for predicting corporate carbon emissions according to claim 2, wherein: For each model of product in the enterprise's production process within the target period, the total carbon emissions of a single product of this model in the production process are calculated including: For each process in the enterprise's production process within the target period, the carbon emissions per working hour of the process shall be determined based on the usage of each energy source per working hour of the process and the energy carbon emission factor coefficient of each energy source; For each model of product in the enterprise's production process within the target period, the total carbon emissions of a single product of this model in the production process are determined based on the number of working hours used in each process during the production of a single product of this model and the carbon emissions per working hour of each process.

4. The method for predicting corporate carbon emissions according to claim 3, wherein: For each process of the enterprise production process within the target period, the carbon emissions per working hour of the process are determined according to the usage of each energy source per working hour of the process and the energy carbon emission factor coefficient of each energy source, including: Determine the process matrix based on the usage of each energy source in each process of the enterprise's production process within the target period; Determine the energy carbon emission factor coefficient matrix based on the energy carbon emission factor coefficient of each energy source in the enterprise production process within the target period; According to the process matrix and the energy carbon emission factor coefficient matrix, the carbon emission matrix for each process per working hour is determined, wherein the carbon emission matrix for each process per working hour is a column matrix, and the elements of the carbon emission matrix for each process per working hour are the carbon emission for each process in the enterprise's production process within the target period.

5. The method for predicting corporate carbon emissions according to claim 4, wherein: Determining the process matrix based on the usage of each energy source in each process of the enterprise's production process within the target period includes: The process matrix is ​​determined based on the process information, energy information and the usage of various energy sources corresponding to different processes within the target cycle, wherein the row information of the process matrix is ​​the process information, the column information of the process matrix is ​​the energy information, and the elements of the process matrix are the usage of the energy in the column during a single operation in the current row process.

6. The method for predicting corporate carbon emissions according to claim 4, wherein: The energy carbon emission factor coefficient matrix is ​​a column matrix, the row information of the energy carbon emission factor coefficient matrix is ​​the energy carbon emission factor coefficient, and the elements of the energy carbon emission factor coefficient matrix are the energy carbon emission factor coefficients of the energy type number of the current row.

7. The method for predicting corporate carbon emissions according to any one of claims 4 to 6, wherein: For each model of product in the enterprise's production process within the target period, the total carbon emissions of a single product of this model in the production process are determined based on the number of working hours used in each process during the production process of the single product of this model and the carbon emissions per working hour of each process, including: Determine the manufacturing time matrix of the product according to the number of working hours used in each process during the production of a single product of the model; According to the product manufacturing working hour matrix and the carbon emission matrix of each process single working hour, the production carbon emission matrix of each product model is determined, wherein the production carbon emission matrix of each product model is a column matrix, and the elements of the production carbon emission matrix of each product model are the total carbon emissions of a single product of this model in the production process during the target period.

8. The method for predicting corporate carbon emissions according to claim 7, wherein: Determining the product manufacturing time matrix according to the number of working hours used in each process during the production process of a single product of this model includes: The product manufacturing time matrix is ​​determined based on the process information, product model information and the total number of working hours required for a single product of the current model in the process within the target cycle, wherein the row information of the product manufacturing time matrix is ​​the product model information, the column information of the product manufacturing time matrix is ​​the process information, and the elements of the product manufacturing time matrix are the number of working hours used in the current column process during the production process of a single product of the current row product model.

9. The method for predicting corporate carbon emissions according to claim 7, wherein: For each order in the enterprise production process within the target period, the total carbon emissions of all products in the order during the production process are determined based on the total carbon emissions of each model of a single product during the production process and the number of products of each model to be produced in the order, including: Determine the product production order matrix based on the quantity of each model of product to be produced in each order of the enterprise's production process within the target period; According to the product production order matrix and the production carbon emission matrix of each product model, the carbon emission matrix of each product production order is determined, wherein the carbon emission matrix of each product production order is a column matrix, and the elements of the carbon emission matrix of each product production order are the total carbon emissions of all products in each order during the target period in the production process.

10. The method for predicting corporate carbon emissions according to claim 9, wherein: Determining the product production order matrix according to the quantity of each model of product to be produced in each order of the enterprise production process within the target period includes: The product production order matrix is ​​determined based on the order number information within the target period, the product model information in the order, and the quantity information of products of different models in the order, wherein the row information of the product production order matrix is ​​the order number information, the column information of the product production order matrix is ​​the product model information, and the elements of the product production order matrix are the quantity to be produced of products of the product model in this column in the current order.

11. The method for predicting corporate carbon emissions according to any one of claims 1 to 6, wherein: The data relationship model of the carbon emissions of the enterprise production process and the total carbon emissions of the enterprise established in advance includes: According to the historical data of carbon emissions in the production process of the enterprise and the total carbon emissions of the enterprise, data fitting is performed to determine the data relationship model.

12. The method for predicting corporate carbon emissions according to claim 11, wherein: The data relationship model is a power function model.

13. An enterprise carbon emission prediction device, comprising: A relational model building module is configured to pre-build a data relational model of carbon emissions during the enterprise's production process and the enterprise's total carbon emissions; A first emission calculation module is configured to calculate the carbon emissions of the enterprise production process within a target period based on the order information; The second emission determination module is configured to determine the total carbon emissions of the enterprise within the target period according to the carbon emissions of the enterprise's production process within the target period and the data relationship model.

14. An enterprise carbon emission prediction device, comprising: a memory configured to store instructions; The processor is configured to execute the instructions so that the enterprise carbon emission prediction device implements the enterprise carbon emission prediction method as described in any one of claims 1-12.

15. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the enterprise carbon emission prediction method according to any one of claims 1 to 12 is implemented.

16. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the enterprise carbon emission prediction method according to any one of claims 1 to 12 is implemented.

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