Product carbon footprint optimization method and device

Abstract

The invention discloses a product carbon footprint optimization method and device, and relates to the technical field of energy. The product carbon footprint optimization method comprises the steps of obtaining a target carbon footprint optimization model of a target product in a target batch, wherein the target carbon footprint optimization model comprises at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a use carbon footprint optimization model and a recovery carbon footprint optimization model; solving the target carbon footprint optimization model by using a stochastic optimization algorithm to obtain an optimization result; and optimizing the carbon footprint of the target product by using the optimization result. According to the embodiment of the invention, the method can improve the product carbon footprint optimization efficiency and accuracy.

Description

Technical Field

[0001] The present application belongs to the field of energy technology, and specifically relates to a method and device for optimizing the carbon footprint of a product. Background Art

[0002] Product carbon footprint is defined as the total greenhouse gas emissions of a product throughout its life cycle, from raw material acquisition, production, transportation, use to final disposal / recycling. Currently, the standards for carbon footprint include PAS2050:2008, GHG Protocol, and ISO 14067:2018, all of which use the life cycle assessment (LCA) method as a theoretical basis to determine the objectives and scope of carbon footprint assessment, inventory analysis, impact assessment, and explanation.

[0003] And with the advancement of computing power and data processing technology, many companies have developed models that can more accurately and efficiently evaluate product carbon footprint accounting, which has been widely used in various industries, including manufacturing, agriculture, food, etc. These models can not only calculate the carbon emissions of each product life cycle, but also identify and analyze the key factors affecting carbon emissions, thereby proposing optimization strategies to reduce emissions. Companies can use these models to identify and improve carbon management practices, reduce environmental impacts during production, and respond to the demands of consumers and regulators for sustainable development.

[0004] From the above, we can see that in terms of carbon footprint accounting, although there is no complete accounting standard for various industries, there are many theoretical studies and practical applications, and the technology is relatively mature. In terms of carbon footprint optimization methods for new energy products and other products, taking new energy products as an example, the current idea is to first calculate the carbon footprint of new energy products throughout their life cycle, and then manually analyze and extract emission reduction suggestions or strategies based on the relevant factors affecting the carbon footprint of new energy products in the carbon footprint accounting model of new energy products, so as to achieve the optimization of the carbon footprint of new energy products. Due to the time consumption and strong subjectivity of manually optimizing the carbon footprint of new energy products and other products, the above method has the problems of low efficiency and low accuracy in product carbon footprint optimization. Summary of the invention

[0005] The technical problem to be solved by the present application is to provide a product carbon footprint optimization method and device in view of the above-mentioned deficiencies in the prior art. By using the product carbon footprint optimization method, the efficiency and accuracy of product carbon footprint optimization can be improved.

[0006] In a first aspect, an embodiment of the present application provides a product carbon footprint optimization method, including: obtaining a target carbon footprint optimization model of a target product in a target batch, the target carbon footprint optimization model including at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model; solving the target carbon footprint optimization model using a random optimization algorithm to obtain an optimization result; and optimizing the carbon footprint of the target product using the optimization result.

[0007] Based on the same inventive concept, in the second aspect, an embodiment of the present application also provides a product carbon footprint optimization device, including: an acquisition module, used to obtain a target carbon footprint optimization model of a target product in a target batch, the target carbon footprint optimization model including at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model and a recycling carbon footprint optimization model; a solution module, connected to the acquisition module, used to solve the target carbon footprint optimization model using a random optimization algorithm to obtain an optimization result; an optimization module, connected to the solution module, used to optimize the carbon footprint of the target product using the optimization result.

[0008] According to the product carbon footprint optimization method and device provided in the embodiments of the present application, a target carbon footprint optimization model of a target product in a target batch is first obtained, and then a random algorithm is used to solve the target carbon footprint optimization model to obtain an optimization result, and then the optimization result is used to optimize the carbon footprint of the target product. The entire process does not require human intervention, and the optimization result can be automatically solved and used to optimize the carbon footprint of the target product, thereby improving the efficiency and accuracy of product carbon footprint optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram showing a process of a product carbon footprint optimization method provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram showing the carbon footprint accounting boundary of a product throughout its life cycle provided by an embodiment of the present application;

[0011] Figure 3 A schematic diagram showing the topology of an industrial park provided in an embodiment of the present application;

[0012] Figure 4 A detailed flow chart of a product carbon footprint optimization process provided by an embodiment of the present application is shown;

[0013] Figure 5 A schematic diagram showing the entire process of optimizing the carbon footprint of a product provided by an embodiment of the present application;

[0014] Figure 6 A schematic diagram of the structure of a product carbon footprint optimization device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the technical solution of the present application, the present application is further described in detail below with reference to the accompanying drawings and embodiments.

[0016] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present application and are not configured to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0017] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0018] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0019] Example 1

[0020] The product carbon footprint optimization method provided in the embodiment of the present application can be executed by a product carbon footprint optimization device and an electronic device, etc. The following takes the product carbon footprint optimization method executed by an electronic device as an example for description.

[0021] like Figure 1 As shown, the product carbon footprint optimization method provided in the embodiment of the present application may include steps S110 to S130.

[0022] S110. Obtain a target carbon footprint optimization model for a target product in a target batch, where the target carbon footprint optimization model includes at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model.

[0023] S120. Solve the target carbon footprint optimization model using a random optimization algorithm to obtain an optimization result.

[0024] S130. Optimize the carbon footprint of the target product using the optimization result.

[0025] According to the product carbon footprint optimization method provided in the embodiment of the present application, the target carbon footprint optimization model of the target product in the target batch is first obtained, and then the target carbon footprint optimization model is solved by using a random algorithm to obtain an optimization result, and then the optimization result is used to optimize the carbon footprint of the target product. The entire process does not require human intervention, and the optimization result can be automatically solved and used to optimize the carbon footprint of the target product, thereby improving the efficiency and accuracy of product carbon footprint optimization.

[0026] The specific implementation methods of the above steps are introduced below.

[0027] In step S110, the target batch may be any batch. The target product may be any industrial product such as a new energy product in the target batch. For example, the new energy product may include photovoltaic products such as solar panels, inverters, and photovoltaic modules. Of course, the target product may also be other industrial products other than new energy products, which is not limited here.

[0028] As an example, the target carbon footprint optimization model includes a raw material carbon footprint optimization model; as another example, the target carbon footprint optimization model includes a production carbon footprint optimization model and a transportation carbon footprint optimization model; as yet another example, the target carbon footprint optimization model includes a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model; as yet another example, the target carbon footprint optimization model includes a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model; as yet another example, the target carbon footprint optimization model includes a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model.

[0029] For example, the raw material carbon footprint optimization model may be a model that can optimize the carbon footprint of the raw materials of the target product; the production carbon footprint optimization model may be a model that can optimize the carbon footprint of the production process of the target product; the transportation carbon footprint optimization model may be a model that can optimize the carbon footprint of the transportation process of the target product; the use carbon footprint optimization model may be a model that can optimize the carbon footprint of the use process of the target product; and the recycling carbon footprint optimization model may be a model that can optimize the carbon footprint of the recycling process of the target product. As an example, the target carbon footprint optimization model may be pre-stored in the electronic device for subsequent call.

[0030] As another example, the target carbon footprint optimization model includes at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model; obtaining the target carbon footprint optimization model of the target product in the target batch includes:

[0031] When the target carbon footprint optimization model includes a raw material carbon footprint optimization model, a raw material carbon footprint optimization model is established according to the first type of optimization objectives, the first type of decision variables and the first type of constraints;

[0032] and / or, when the target carbon footprint optimization model includes a production carbon footprint optimization model, establishing a production carbon footprint optimization model according to the second type of optimization objective, the second type of decision variables and the second type of constraints;

[0033] and / or, when the target carbon footprint optimization model includes a transport carbon footprint optimization model, establishing a transport carbon footprint optimization model according to the third type of optimization objective, the third type of decision variables and the third type of constraints;

[0034] And / or, when the target carbon footprint optimization model includes a usage carbon footprint optimization model, a usage carbon footprint optimization model is established according to the fourth type of optimization objectives, the fourth type of decision variables and the fourth type of constraints; and / or, when the target carbon footprint optimization model includes a recycling carbon footprint optimization model, a recycling carbon footprint optimization model is established according to the fifth type of optimization objectives, the fifth type of decision variables and the fifth type of constraints.

[0035] According to this example, a target carbon footprint optimization model can be established, providing a basis for subsequently optimizing the carbon footprint of the target product according to the target carbon footprint optimization model.

[0036] Exemplarily, before obtaining the target carbon footprint optimization model of the target product in the target batch, the method may also include a product life cycle carbon footprint accounting boundary. For products in industrial scenarios (i.e., target products), the embodiment of the present application sets the functional unit as a batch (i.e., target batch), and divides the life cycle of the target product into five steps: raw materials, production, transportation, use, and recycling based on the life cycle assessment method (LCA model). The product life cycle carbon footprint accounting boundary provided in the embodiment of the present application is as follows: Figure 2 As shown. The carbon footprint of raw materials includes the recycling of raw materials (i.e. renewable raw materials), carbon emissions from the production and preparation process, and carbon emissions from the transportation process; the carbon footprint of product production mainly comes from the indirect greenhouse gas emissions generated by energy consumption and manpower consumption in the production and manufacturing process, as well as the direct greenhouse gas emissions generated by the product production process. Energy consumption includes natural gas consumption, electricity consumption, etc.; after the product is manufactured and transported to the buyer, the transportation carbon footprint can be calculated based on the product weight, transportation distance and transportation method (water and rail, etc.) to obtain the carbon footprint of this link; the carbon footprint of product use mainly comes from the carbon emissions generated by the energy consumed in the use and maintenance of the product; after the product reaches the end of its service life, it needs to be discarded and the raw materials need to be recycled. The carbon emissions of product discard and raw material recycling mainly include carbon emissions generated by energy consumption.

[0037] In some embodiments, the target carbon footprint optimization model includes a feedstock carbon footprint optimization model, and the feedstock carbon footprint optimization model includes a first type of optimization objective, a first type of decision variables, and a first type of constraints;

[0038] The first type of optimization objectives include the minimum total amount of carbon dioxide emissions generated by the first raw material acquisition and transportation links of the target product:

[0039]

[0040] Among them, minCF M represents the minimum value of the total amount of carbon dioxide emissions generated by the acquisition and transportation of the first raw material of the target product; P represents the number of target products in the target batch; n represents the total amount of the first raw material type required to produce the target product; Indicates the usage of the first raw material of the i-th category in the process of producing the target product; EF i M represents the carbon emission factor of the recovery and preparation process of the first raw material of the i-th category in the process of producing the target product; β represents the return empty driving rate; L i Indicates the transportation distance of the first raw material of the i-th category in the process of producing the target product; ET i represents the carbon emission factor of the transportation process of the first raw material of the i-th category;

[0041] The first type of decision variables includes: at least one of each first raw material supplier, each raw material transportation method, each raw material transportation distance and each first raw material fuel type;

[0042] The first type of constraint conditions includes at least one of a first raw material price constraint, a first transportation price constraint, and a first carbon emission constraint.

[0043] In some examples, when the first type of constraint includes a first raw material price constraint, the first raw material price constraint includes:

[0044]

[0045] in, Represents the first material price of the first raw material of the i-th category; RM M Indicates a first material price limit for a first raw material.

[0046] In some examples, when the first category of constraints includes a first transportation price constraint, the first transportation price constraint includes:

[0047]

[0048] in, represents the first transportation price of the first raw material of the i-th category; RM t Indicates the first transportation price limit for the first raw material.

[0049] In some examples, the first carbon emission constraint includes a first category of information belonging to a target carbon emission factor library, the first category of information including at least one of the carbon emission factors corresponding to each first raw material of each first raw material supplier, the transportation method of each supplier, the transportation distance of each raw material, the transportation type of each first raw material, and the carbon emission factor of each fuel; the first carbon emission constraint corresponds to the first category of decision variables.

[0050] Exemplarily, the first type of optimization objective may be the optimization objective of the raw material carbon footprint optimization model; the first type of decision variables may be the decision variables of the raw material carbon footprint optimization model; the first type of constraints may be the constraints of the raw material carbon footprint optimization model; the return empty rate refers to the proportion of vehicles that are empty when they return to the starting point or go to the next destination after completing the transportation task.

[0051] As an example, P, β, and M may be determined in response to a first input operation of the user based on the electronic device. i M ,RM M and RM t , the first input operation can be input P, ​​β, M i M ,RM M and RM tFor example, the first input operation may be inputting P, β, M in the input box displayed by the electronic device. i M ,RM M and RM t Operation; EF can be obtained from the target carbon emission factor library i M , E.T. i , L i ,RM i t and RM i M .

[0052] As another example, the electronic device pre-stores P, β, M i M ,RM M ,RM t EF i M , E.T. i , L i , and For subsequent direct calls.

[0053] It should be noted that P, β, M i M ,RM M and RM t The value of can be set according to the actual situation and is not limited here. The value of β can be determined according to the selected transportation unit, and its value range is [0, 100%].

[0054] As an example, when the first type of decision variables includes each first raw material supplier, the first carbon emission constraint includes that the carbon emission factor corresponding to each first raw material of each first raw material supplier belongs to the target carbon emission factor library; as another example, when the first type of decision variables includes each first raw material supplier and each raw material transportation mode, the first carbon emission constraint includes that the carbon emission factor corresponding to each first raw material of each first raw material supplier and each supplier transportation mode belong to the target carbon emission factor library; as another example, when the first type of decision variables includes each first raw material supplier, each first raw material fuel type and each raw material transportation distance, the first carbon emission constraint includes that the carbon emission factor corresponding to each first raw material of each first raw material supplier, each fuel carbon emission factor and each raw material transportation distance belong to the target carbon emission factor library; as another example, when the first type of decision variables includes each first raw material supplier, each raw material transportation mode, each raw material transportation distance and each first raw material fuel type, the first carbon emission constraint includes that the carbon emission factor corresponding to each first raw material of each first raw material supplier, each supplier transportation mode, each raw material transportation distance, each first raw material transportation type and each fuel carbon emission factor belong to the target carbon emission factor library.

[0055] As an example, the first category of constraints may include a first raw material price constraint; as another example, the first category of constraints may include a first transportation price constraint and a first carbon emission constraint; as yet another example, the first category of constraints may include a first raw material price constraint, a first transportation price constraint, and a first carbon emission constraint.

[0056] In some examples, the target carbon emission factor library includes at least one of a first carbon emission factor library, a second carbon emission factor library, a third carbon emission factor library, a fourth carbon emission factor library, and a fifth carbon emission factor library;

[0057] The process of establishing the target carbon emission factor library includes at least one of the following:

[0058] Establishing a first carbon emission factor library based on at least one of the raw material type, supplier, raw material address and raw material carbon emission factor;

[0059] A second carbon emission factor library is established based on the first information under different transportation modes, wherein the first information includes at least one of distance information, transportation unit price, fuel type, fuel usage, and fuel carbon emission factor;

[0060] Establish a third carbon emission factor library based on carbon emission factors of various types of energy;

[0061] According to various types of manpower carbon emission factors, the fourth carbon emission factor library is established;

[0062] Based on the global warming potential of each greenhouse gas, the fifth carbon emission factor library is established.

[0063] For example, the first carbon emission factor library may be a "raw material-supplier-carbon emission factor" library, the second carbon emission factor library may be a "transportation mode-carbon emission factor" library, the third carbon emission factor library may be an energy carbon emission factor library; the fourth carbon emission factor library may be a library of carbon emission factors for various types of manpower use; the fifth carbon emission factor library may be a library of global warming potentials for various greenhouse gases. Various types of energy carbon emission factors may include cold, heat, electricity, and compressed air.

[0064] For example, the carbon emission factor library of the Intergovernmental Panel on Climate Change (IPCC), the Ministry of Ecology and Environment of China, the Institute of Environmental Planning of the Ministry of Ecology and Environment, etc. can be searched, and the carbon emission factors involved in the entire life cycle of the target product can be put into the target carbon emission factor library to be established, and the actual data of the relevant processes of the target product can be used to correct them to obtain the target carbon emission factor library. Take the example of the search process from the IPCC carbon emission factor library: list the carbon emission factors involved in the entire life cycle of the target product, enter the IPCC carbon emission factor database website, select the IPCC Guidelines 2006, click "Energy", and you can check the three types of carbon emission factors: fuel combustion activities, fugitive emissions of fuels, and transportation and storage of carbon dioxide; click "Industrial Processes and Product Use" to check the carbon emission factors of industries such as mining, chemical industry, and metal industry; click "Waste" to check the carbon emission factors of solid waste disposal, biological treatment of solid waste, incineration of waste, and open-air burning. Record the relevant carbon emission factors found in the carbon emission factor list of the target product, and mark the maximum and minimum values ​​of the confidence interval of each carbon emission factor and the reference source. According to the different life stages of the target products, the target product carbon emission factor list is constructed into a target carbon emission factor library, where the target product carbon emission factor list includes raw material type, supplier, raw material address and raw material carbon emission factor, distance information, transportation unit price, fuel type, fuel usage and fuel carbon emission factor, various types of energy carbon emission factors, various types of manpower carbon emission factors and the global warming potential of each greenhouse gas.

[0065] Exemplarily, the first carbon emission factor library may include raw material type, supplier, raw material address and raw material carbon emission factor, and information such as raw material type, supplier, raw material address and raw material carbon emission factor can be found in the target carbon emission factor library; the second carbon emission factor library may include distance information, transportation unit price, fuel type, fuel usage and fuel carbon emission factor, and information such as distance information, transportation unit price, fuel type, fuel usage and fuel carbon emission factor can be found in the target carbon emission factor library; the third carbon emission factor library may include various types of energy carbon emission factors, and various types of energy carbon emission factors can be found in the target carbon emission factor library; the fourth carbon emission factor may include various types of manpower carbon emission factors, and various types of manpower carbon emission factors can be found in the target carbon emission factor library; the fifth carbon emission factor library may include the global warming potential of each greenhouse gas, and the global warming potential of each greenhouse gas can be found in the target carbon emission factor library.

[0066] It should be noted that the first type of information belongs to the first carbon emission factor library of the target carbon emission factor library.

[0067] In some examples, before obtaining the target carbon footprint optimization model of the target product in the target batch, the method may further include: establishing a general industrial park topology. The general industrial park topology established in the embodiment of the present application is as follows: Figure 3 As shown in the figure, the factory includes four loads: compressed air, electricity, cold and heat; three energy inputs: electricity purchased from the power grid, new energy electricity, and gas purchased from the heat network; six energy outputs: industrial gas load, industrial electricity load, charging pile, electric energy storage, cold load and heat load; four energy conversion equipment: gas boiler, air compressor, electric refrigeration, and heat pump; other adjustable equipment such as charging pile and electric energy storage. This topology can increase or decrease each device according to the actual situation of the target product production park.

[0068] The carbon footprint of the production process can include three parts: indirect greenhouse gas emissions caused by energy consumption, labor consumption, and direct greenhouse gas emissions. The calculation formula for the carbon footprint of the production process is shown in formula (8).

[0069]

[0070] Among them, CF P represents the carbon footprint of the production process; P represents the number of target products in the target batch; j represents the terminal energy type; E j represents the usage of the jth terminal energy; E k represents the usage of the kth type of manpower; G v Indicates the direct emission of the vth greenhouse gas; EL j represents the carbon emission factor of the jth terminal energy; EL k represents the carbon emission factor of the k-th type of labor use; GWP v represents the global warming potential of the vth greenhouse gas.

[0071] Since in the process of product production, optimizing equipment operation, improving energy conversion efficiency, and optimizing the overall operation of the park can reduce labor consumption and direct carbon emissions, the optimization of production carbon footprint can only consider the optimization of energy consumption in the production process. The main participants in the optimization are product manufacturing units.

[0072] In other embodiments, the target carbon footprint optimization model includes a production carbon footprint optimization model, and the production carbon footprint optimization model includes a second type of optimization objective, a second type of decision variables, and a second type of constraints;

[0073] The second type of optimization objectives includes the minimum total greenhouse gas emissions generated by the target product during the production process:

[0074]

[0075] Among them, minCF Prepresents the minimum value of the total greenhouse gas emissions generated by the target product during the production process; P represents the number of target products in the target batch; EL1 represents the carbon emission factor of the first terminal energy in the production process of the target product; P load (t) represents the park electricity load at time t during the production process of the target product; EL2 represents the carbon emission factor of the second terminal energy during the production process of the target product; H load (t) represents the heat load of the park at time t during the production process of the target product; EL3 represents the carbon emission factor of the third terminal energy during the production process of the target product; C load (t) represents the cooling load of the park at time t during the production of the target product; EL4 represents the carbon emission factor of the fourth terminal energy during the production of the target product; A load (t) represents the compressed air load of the park at time t during the production process of the target product;

[0076] The second type of decision variables includes: at least one of the first gas purchase amount, the first power grid purchase amount, the power of each first device, the start / stop state of each first device, the first electric energy storage, and the charging / discharging state of the first charging pile;

[0077] The second type of constraint conditions include at least one of the first equipment capacity constraints, the first climbing rate constraints, the first power balance constraints, the first energy storage equipment constraints, the first charging pile constraints and the first movable load constraints; the first power balance constraints include at least one of the first electric power balance constraints, the first cold power balance constraints, the first air compressor power balance constraints, the first heat power balance constraints and the first natural gas power balance constraints.

[0078] In some examples, when the second type of constraint includes each first device capacity constraint, each first device capacity constraint includes:

[0079]

[0080] Among them, X ED,t Indicates the first start-stop state of the energy assembly unit ED at time t; Indicates the first minimum operating output of the energy component unit ED; P ED (t) represents the first operating output of the energy assembly unit ED at time t; Indicates the first maximum operating output of the energy component unit ED.

[0081] In some examples, when the second type of constraint includes a first climbing rate constraint, the first climbing rate constraint includes:

[0082] DP ED ≤P ED (t)-P ED(t-Δt)≤UP ED

[0083] Among them, DP ED Indicates the first down ramp rate of the energy component unit ED, UP ED represents the first climbing rate of the energy assembly unit ED; Δt is the first time interval.

[0084] In some examples, when the second type of constraint includes a first power balance constraint, and the first power balance constraint includes a first electric power balance constraint, the first electric power balance constraint includes:

[0085] P NE (t)+P out (t)+P e GB (t) = P EC (t)+P AC (t)+P HP (t)+P es (t)+P cp (t)+P tra (t)+P qt (t)

[0086] Among them, P NE (t) represents the power generated by renewable energy at time t; P out (t) represents the electric energy interaction power between the power system and the power grid at time t during the production process of the target product; P represents the power generation of the gas boiler at time t during the production process of the target product; EC (t) represents the electric refrigeration power of the target product at time t during the production process; P AC (t) represents the air compressor power of the target product at time t during the production process; P HP (t) represents the heat pump power of the target product at time t during the production process; P es (t) represents the electric energy storage power of the target product at time t during the production process; P cp (t) represents the charging pile power of the target product at time t during the production process; P tra (t) represents the transferable load power of the target product at time t during the production process; P qt (t) represents the electric power of other electrical loads of the target product at time t during the production process.

[0087] In some examples, when the second type of constraint includes a first power balance constraint, and the first power balance constraint includes a first cold power balance constraint, the first cold power balance constraint includes:

[0088] η EC,t PEC (t) = C load (t)

[0089] Among them, η EC,t represents the electric refrigeration energy conversion efficiency of the target product at time t during the production process; P EC (t) represents the power consumption of the compression refrigerator at time t during the production process of the target product.

[0090] In some examples, when the second type of constraint includes a first power balance constraint, and the first power balance constraint includes a first air compressor power balance constraint, the first air compressor power balance constraint includes:

[0091] η AC,t P AC (t) = A load (t)

[0092] Among them, η AC,t It represents the energy conversion efficiency of the air compressor at time t during the production process of the target product;

[0093] A load (t) represents the compressed air rate generated by the air compressor at time t during the production process of the target product, that is, the compressed air load of the park at time t during the production process of the target product.

[0094] In some examples, when the second type of constraint includes a first power balance constraint, and the first power balance constraint includes a first thermal power balance constraint, the first thermal power balance constraint includes:

[0095]

[0096] Among them, η HP,t represents the heat pump energy conversion efficiency of the target product at time t during the production process; P HP (t) represents the heat pump power of the target product at time t during the production process; P h GB (t) represents the thermal power of the gas boiler at time t during the production process of the target product; H load (t) represents the heat load of the park at time t during the production process of the target product.

[0097] In some examples, when the second type of constraint includes a first power balance constraint, and the first power balance constraint includes a first natural gas power balance constraint, the first natural gas power balance constraint includes:

[0098]

[0099] Among them, P h GB(t) represents the heat generation power of the gas boiler at time t during the production process of the target product; represents the heat generation efficiency of the gas boiler at time t during the production process of the target product; P e GB (t) represents the power generation of the gas boiler at time t during the production process of the target product; G represents the power generation efficiency of the gas boiler at time t during the production process of the target product; tot (t) represents the total rate of natural gas consumption at time t during the production process of the target product.

[0100] Exemplarily, the second type of optimization objective may be the optimization objective of the production carbon footprint optimization model, the second type of decision variables may be the decision variables of the production carbon footprint optimization model, and the second type of constraints may be the constraints of the production carbon footprint optimization model. The first gas purchase volume may be the gas purchase volume during the target product generation process; the first power grid purchase volume may be the power grid purchase volume during the target product generation process; the first equipment power may be the equipment power during the target product generation process; the first equipment start-stop state may be the equipment start-stop state during the target product production process; the first electric energy storage may be the electric energy storage during the target product production process; the first charging pile charge and discharge state may be the charging pile charge and discharge state during the target product production process; the first equipment capacity constraint may be the equipment capacity constraint corresponding to the target product production process; the first ramp rate constraint may be the ramp rate constraint corresponding to the target product production process; the first power balance constraint may be the function balance constraint corresponding to the target product production process; the first energy storage equipment constraint may be the energy storage equipment corresponding to the target product production process; the first charging pile constraint may be the charging pile constraint corresponding to the target product production process; the first translatable load constraint may be the translatable load constraint corresponding to the target product production process.

[0101] As an example, the second type of decision variables may include the first gas purchase amount; as another example, the second type of decision variables may include the first power grid purchase amount and the power of each first device; as yet another example, the second type of decision variables may include the power of each first device, the start / stop status of each first device, and the first electrical energy storage; as yet another example, the second type of decision variables include: the first gas purchase amount, the first power grid purchase amount, the power of each first device, the start / stop status of each first device, the first electrical energy storage, and the charging and discharging status of the first charging pile.

[0102] As an example, the second type of constraints include each first device capacity constraint; as another example, the second type of constraints include a first climbing rate constraint and a first power balance constraint; as yet another example, the second type of constraints include a first energy storage device constraint, a first charging pile constraint, and a first movable load constraint; as yet another example, the second type of constraints include each first device capacity constraint, a first climbing rate constraint, a first power balance constraint, a first energy storage device constraint, a first charging pile constraint, and a first movable load constraint.

[0103] As an example, the first power balance constraint includes a first electric power balance constraint; as another example, the first power balance constraint includes a first cooling power balance constraint and a first air compressor power balance constraint; as yet another example, the first power balance constraint includes a first air compressor power balance constraint, a first thermal power balance constraint, and a first natural gas power balance constraint; as yet another example, the first power balance constraint includes a first electric power balance constraint, a first cooling power balance constraint, a first air compressor power balance constraint, a first thermal power balance constraint, and a first natural gas power balance constraint.

[0104] For example, when the energy assembly unit ED is started at time t, X ED,t is 1; when the energy component unit ED stops at time t, X ED,t is 0.

[0105] For example, H load (t) is the total rate of natural gas consumption in the park. The energy component unit ED may include gas boiler GB, photovoltaic PV, electric refrigeration EC, air compressor AC, heat pump HP, electric energy storage es, charging pile cp, transferable load tra and other electrical loads qt.

[0106] For example, P load , H load , C load , A load , P es , P cp , P tra , P qt , P HP , P h GB , P EC and P AC Can be actually measured; η HP,t , η EC,t , η AC,t It is an inherent parameter of the device and can be stored in the electronic device in advance for subsequent direct call; EL1, EL2, EL3, EL4 can be stored in the electronic device in advance for subsequent direct call, or can be obtained from the target carbon emission factor library; PED (t) and X ED,t is a variable, and is a constant, and It can be set according to the actual situation and is not limited here; the electronic device can store Multiple P ED (t), X ED,t , DP ED UP ED and Δt for subsequent direct calls.

[0107] It should be noted that the first energy storage device constraint, the first charging pile constraint, the first movable load constraint, and the second energy storage device constraint, the second charging pile constraint, and the second movable load constraint mentioned below may be the energy storage device constraint, charging pile constraint, and movable load constraint in the prior art, and will not be repeated here.

[0108] In some examples, the park power load P at time t during the production process of the target product load (t) is calculated by formula (1);

[0109] Formula (1) includes:

[0110] P load (t) = P es (t)+P cp (t)+P tra (t)+P qt (t) (1)

[0111] Among them, P es (t) represents the electric energy storage power of the target product at time t during the production process; P cp (t) represents the charging pile power of the target product at time t during the production process; P tra (t) represents the transferable load power of the target product at time t during the production process; P qt (t) represents the power of other electrical loads of the target product at time t during the production process;

[0112] The heat load H of the park at time t during the production process of the target product load (t) is calculated by formula (2);

[0113] Formula (2) includes:

[0114]

[0115] The cooling load C of the park at time t during the production process of the target product load (t) is calculated by formula (3);

[0116] Among them, η HP,t represents the heat pump energy conversion efficiency of the target product at time t during the production process; P HP (t) represents the heat pump power of the target product at time t during the production process; P h GB (t) represents the thermal power of the gas boiler at time t during the production process of the target product;

[0117] Formula (3) includes:

[0118] C load (t) = η EC,t P EC (t) (3)

[0119] Among them, η EC,t represents the electric refrigeration energy conversion efficiency of the target product at time t during the production process; P EC (t) represents the power consumption of the compression refrigerator at time t during the production process of the target product;

[0120] The compressed air load A of the park at time t during the production process of the target product load (t) is calculated by formula (4);

[0121] Formula (4) includes:

[0122] A load (t) = η AC,t P AC (t) (4).

[0123] Among them, η AC,t It represents the energy conversion efficiency of the air compressor at time t during the production process of the target product;

[0124] P AC (t) represents the electric power of the air compressor at time t during the production process of the target product.

[0125] In this embodiment, the above formula (1) can be used to quickly and accurately determine the park power load P of the target product at time t during the production process. load (t); The above formula (2) can quickly and accurately determine the park heat load H of the target product at time t during the production process load (t); The above formula (3) can quickly and accurately determine the park cooling load C of the target product at time t during the production process load (t); The above formula (4) can quickly and accurately determine the park compressed air load A of the target product at time t during the production process load (t).

[0126] For example, P es(t)=50kW,P cp (t)=30kW,P tra (t)=20kW,P qt (t) = 10kW, then P load (t) = 110kW; P HP (t) = 100 kW, η HP,t =60%, Then H load (t) = 210kW; P EC (t) = 200 kW, η EC,t =70%, then C load (t) = 140 kW; η AC,t =80%, P AC (t) = 300kW, then A load (t) = 240kW.

[0127] In still other embodiments, the target carbon footprint optimization model includes a transportation carbon footprint optimization model, and the transportation carbon footprint optimization model includes a third type of optimization objective, a third type of decision variables, and a third type of constraints;

[0128] The third type of optimization goal includes the minimum total amount of carbon dioxide emissions generated by the transportation of target products:

[0129]

[0130] Among them, minCF T represents the minimum value of the total amount of carbon dioxide emissions generated in the transportation process of the target product; P represents the number of target products in the target batch; M' represents the total weight of a batch of target products; β represents the return empty trip rate; L' represents the transportation distance in the process of transporting the target product; ET represents the carbon emission factor in the transportation process of the target product;

[0131] The third type of decision variables includes: at least one of the transportation mode of each target product and the transportation distance of each target product;

[0132] The third type of constraint condition includes at least one of the second transportation price constraint and the second carbon emission constraint;

[0133] When the third type of constraint condition includes a second transportation price constraint, the second transportation price constraint includes:

[0134] M'×RT t '×L'≤RT t

[0135] Among them, RT t ' represents the unit transportation price of the target product; RT t Indicates a second transportation price limit for a second target product;

[0136] When the third type of constraint includes the second carbon emission constraint, the second carbon emission constraint includes that the second type of information belongs to the target carbon emission factor, and the second type of information includes at least one of the transportation mode of each supplier and the transportation distance of each target product; the second carbon emission constraint corresponds to the third type of decision variable.

[0137] Exemplarily, the third type of optimization objective may be the optimization objective of the transportation carbon footprint optimization model; the third type of decision variables may be the decision variables of the transportation carbon footprint optimization model; the third type of constraints may be the constraints of the transportation carbon footprint optimization model; the second transportation price constraint may be the transportation price constraint of the target product; the second carbon emission constraint may be the carbon emission constraint of the target product; the second transportation price may be the transportation price of the target product, and the second transportation price limit may be the price limit of the target product.

[0138] As an example, when the third type of decision variables includes the transportation mode of each target product, the second carbon emission constraint includes that the transportation mode of each supplier belongs to the target carbon emission factor library; as another example, when the third type of decision variables includes the transportation distance of each target product, the second carbon emission constraint includes that the transportation distance of each target product belongs to the target carbon emission factor library; as yet another example, when the third type of decision variables includes the transportation mode of each target product and the transportation distance of each target product, the second carbon emission constraint may include that both the transportation mode of each supplier and the transportation distance of each target product belong to the target carbon emission factor library.

[0139] As an example, the third category of constraints includes a second transportation price constraint; as another example, the third category of constraints includes a second carbon emission constraint; as yet another example, the third category of constraints includes a second transportation price constraint and a second carbon emission constraint.

[0140] It should be noted that the second type of information belongs to the second carbon emission factor library in the target carbon emission factor library.

[0141] It should be noted that the transportation carbon footprint optimization model is similar to the raw material carbon footprint transportation in the raw material carbon footprint optimization model. The main participants in the optimization are transportation companies, which will not be repeated here.

[0142] After extensive research, the inventor found that the carbon footprint of use mainly comes from the carbon emissions generated by the energy consumed when the product is used and repaired, and the optimization subjects are users and maintenance companies. Carbon emissions generated by maintenance are not considered for the time being. Since the use is more biased towards the user side, the process is accompanied by different user behavior patterns and has a large degree of randomness. Therefore, optimizing its carbon footprint can change the way users behave through energy price incentives, enhance users' energy-saving awareness, thereby reducing energy consumption and reducing carbon emissions.

[0143] In some further embodiments, the target carbon footprint optimization model includes using the carbon footprint optimization model, using the fourth type of optimization objective, the fourth type of decision variables and the fourth type of constraints of the carbon footprint optimization model,

[0144] The fourth type of optimization goal includes the minimum carbon footprint generated by the target product during use:

[0145]

[0146] Among them, minCF U It represents the minimum value of the carbon footprint generated by the target product during use; P represents the number of target products in the target batch; g represents the type of energy consumed by the target product during use and maintenance; Indicates the final amount of energy used during the use of the target product; EF i U Indicates the energy carbon emission factor of the i-th energy source during the use of the target product;

[0147] The fourth type of decision variables includes: the final use of the i-th energy

[0148] The fourth type of constraints include:

[0149]

[0150] in, It represents the original usage of the i-th energy before price incentives during the use of the target product; It represents the final usage rate of the i-th energy during the use of the target product; represents the original usage rate of the i-th energy source before price incentives during the use of the target product; C1 is the energy-saving incentive price; C 2,t represents the peak load incentive price at time t; k represents the consumption The number of hours used for the amount of energy.

[0151] It should be noted that by introducing two incentive measures, namely energy-saving price incentive and peak-shaving price incentive, taking a batch of products as a unit, the final energy usage can meet the fourth type of constraint conditions.

[0152] Understandably, E. i UO , P i,t UO It can be a value obtained according to the actual situation, E i U and P i,t U can be the values ​​that need to be solved for the fourth type of constraints mentioned above, C1, C 2,tIt is a value based on local policies, EFi U Can be obtained from the target carbon emission factor library.

[0153] In still other embodiments, the target carbon footprint optimization model includes a recycling carbon footprint optimization model, and the recycling carbon footprint optimization model includes a fifth type of optimization objective, a fifth type of decision variables, and a fifth type of constraint conditions;

[0154] The fifth type of optimization objectives includes the minimum total greenhouse gas emissions generated by the target product during the recycling process:

[0155]

[0156] Among them, minCF R represents the minimum value of the total greenhouse gas emissions generated by the target product during the recycling process; P represents the number of target products in the target batch; EL'1 represents the carbon emission factor of the first terminal energy during the recycling process of the target product; P' load (t') represents the park electricity load at time t' during the recycling process of the target product; EL'2 represents the carbon emission factor of the second terminal energy during the recycling process of the target product; H load '(t') represents the heat load of the park at time t' during the recycling process of the target product; EL'3 represents the carbon emission factor of the third terminal energy during the recycling process of the target product; C load '(t') represents the cooling load of the park at time t' during the recycling process of the target product; EL'4 represents the carbon emission factor of the fourth terminal energy during the recycling process of the target product; A load '(t') represents the compressed air load of the park at time t' during the recycling process of the target product;

[0157] The fifth category of decision variables includes: at least one of the second gas purchase amount, the second power grid purchase amount, the power of each second device, the start / stop state of each second device, the second electric energy storage, and the charging / discharging state of the second charging pile;

[0158] The fifth category of constraints includes at least one of the second equipment capacity constraints, the second climbing rate constraints, the second power balance constraints, the second energy storage equipment constraints, the second charging pile constraints and the second movable load constraints; the second power balance constraints include at least one of the second electric power balance constraints, the second cold power balance constraints, the second air compressor power balance constraints, the second heat power balance constraints and the second natural gas power balance constraints.

[0159] In some examples, when the fifth category of constraints includes each second device capacity constraint, each second device capacity constraint includes:

[0160]

[0161] Among them, X′ ED,t Indicates the second start-stop state of the energy assembly unit ED at time t' during the recycling process of the target product; Represents the second minimum operating output of the energy component unit ED; P′ ED (t′) represents the second operating output of the energy assembly unit ED at time t′ during the recovery process of the target product; Indicates the second maximum operating output of the energy component unit ED.

[0162] In some examples, when the fifth category of constraints includes a second climbing rate constraint, the second climbing rate constraint includes:

[0163] DP′ ED ≤P′ ED (t′)-P′ ED (t′-Δt′)≤UP′ ED

[0164] Among them, DP′ ED Indicates the second downward slope rate of the energy component unit ED, UP′ ED represents the second ramp-up rate of the energy assembly unit ED; Δt′ is the second time interval.

[0165] In some examples, when the fifth type of constraint includes a second power balance constraint, and the second power balance constraint includes a second electric power balance constraint, the second electric power balance constraint includes:

[0166]

[0167] Among them, P' NE (t') represents the renewable energy power generation of the target product at time t' during the recycling process; P' out (t') represents the electric energy interaction power between the power system and the power grid at time t' during the recycling process of the target product; represents the power generation of the gas boiler at time t' during the recycling process of the target product; P' EC (t') represents the electric refrigeration power of the target product at time t' during the recycling process; P' AC (t') represents the air compressor power of the target product at time t' during the recycling process; P' HP (t') represents the heat pump power of the target product at time t' during the recovery process; P' es (t') represents the electric energy storage power of the target product at time t' during the recycling process; P' cp (t') represents the charging pile power of the target product at time t' during the recycling process; P' tra(t') represents the transferable load power of the target product at time t' during the recycling process;

[0168] P' qt (t') represents the electric power of other electrical loads of the target product at time t' during the recycling process.

[0169] In some examples, when the fifth type of constraint includes a second power balance constraint, and the second power balance constraint includes a second cold power balance constraint, the second cold power balance constraint includes:

[0170] η' EC,t P' EC (t') = C' load (t')

[0171] Among them, η' EC,t represents the electric refrigeration energy conversion efficiency of the target product at time t' during the recycling process; P' EC (t) represents the power consumption of the compression refrigerator at time t' during the recycling process of the target product.

[0172] In some examples, when the fifth category of constraints includes a second power balance constraint, and the second power balance constraint includes a second air compressor power balance constraint, the second air compressor power balance constraint includes:

[0173] η' AC,t' P' AC (t') = A' load (t')

[0174] Among them, η' AC,t' A' represents the energy conversion efficiency of the air compressor at time t' during the recycling process of the target product; load (t') represents the compressed air rate generated by the air compressor at time t' during the recycling process of the target product, that is, the compressed air load of the park at time t' during the recycling process of the target product.

[0175] In some examples, when the fifth type of constraint includes a second power balance constraint, and the second power balance constraint includes a second thermal power balance constraint, the second thermal power balance constraint includes:

[0176]

[0177] Among them, η' HP,t' represents the heat pump energy conversion efficiency of the target product at time t' during the recycling process; P' HP (t') represents the heat pump power of the target product at time t' during the recycling process; Indicates the thermal power of the gas boiler at time t' during the recovery process of the target product; H'load (t') represents the heat load of the park at time t' during the recycling process of the target product.

[0178] In some examples, when the fifth type of constraint includes a second power balance constraint, and the second power balance constraint includes a second natural gas power balance constraint, the second natural gas power balance constraint includes:

[0179]

[0180] in, represents the heat generation power of the gas boiler at time t' during the recovery process of the target product; represents the heat generation efficiency of the gas boiler at time t' during the recovery process of the target product; represents the power generation of the gas boiler at time t' during the recycling process of the target product; G' represents the power generation efficiency of the gas boiler at time t' during the recycling process of the target product; tot (t') represents the total rate of natural gas consumption at time t' during the recovery process of the target product.

[0181] As an example, the fifth category of decision variables includes the second gas purchase amount; as another example, the fifth category of decision variables includes the second power grid purchase amount and the power of each second device; as yet another example, the fifth category of decision variables includes the start and stop status of each second device, the second electrical energy storage and the charging and discharging status of the second charging pile; as yet another example, the fifth category of decision variables includes the second gas purchase amount, the second power grid purchase amount, the power of each second device, the start and stop status of each second device, the second electrical energy storage and the charging and discharging status of the second charging pile.

[0182] As an example, the fifth category of constraints includes each second device capacity constraint; as another example, the fifth category of constraints includes a second climbing rate constraint and a second power balance constraint; as yet another example, the fifth category of constraints includes a second energy storage device constraint, a second charging pile constraint, and a second movable load constraint; as yet another example, the fifth category of constraints includes each second device capacity constraint, a second climbing rate constraint, a second power balance constraint, a second energy storage device constraint, a second charging pile constraint, and a second movable load constraint.

[0183] As an example, the second power balance constraint includes a second electric power balance constraint; as another example, the second power balance constraint includes a second cooling power balance constraint and a second air compressor power balance constraint; as yet another example, the second power balance constraint includes a second air compressor power balance constraint, a second thermal power balance constraint, and a second natural gas power balance constraint; as yet another example, the second power balance constraint includes a second electric power balance constraint, a second cooling power balance constraint, a second air compressor power balance constraint, a second thermal power balance constraint, and a second natural gas power balance constraint.

[0184] It should be noted that the recycling carbon footprint includes the carbon emissions generated by the energy consumption in the waste process of the non-recyclable part of the product. The participants are related waste treatment companies, and their main process is the industrial production process. Therefore, the focus can be on optimizing the factory production process. Since it is similar to the production carbon footprint optimization model, this part will not be repeated.

[0185] In some embodiments, the target carbon footprint optimization model includes a feedstock carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model;

[0186] The raw material carbon footprint optimization model includes the first type of decision variables, the production carbon footprint optimization model includes the second type of decision variables, the transportation carbon footprint optimization model includes the third type of decision variables, the use carbon footprint optimization model includes the fourth type of decision variables; the recycling carbon footprint optimization model includes the fifth type of decision variables;

[0187] The target decision variable includes at least one of the first type of decision variable, the second type of decision variable, the third type of decision variable, the fourth type of decision variable and the fifth type of decision variable;

[0188] The process of obtaining the target decision variable includes:

[0189] Obtain information on factors affecting the carbon footprint of target products;

[0190] The principal component analysis method is used to analyze the influencing factor information and obtain the target decision variables.

[0191] Exemplarily, the influencing factor information may include at least one of raw material influencing factors, production influencing factors, transportation influencing factors, usage influencing factors, and recycling influencing factors. For example, the influencing factor information may include raw material influencing factors; for another example, the influencing factor information may include raw material influencing factors and production influencing factors; for another example, the influencing factor information may include raw material influencing factors, production influencing factors, transportation influencing factors, usage influencing factors, and recycling influencing factors.

[0192] Exemplarily, taking the production process of the target product as an example, obtaining the production influencing factors of the target product's carbon footprint may include sorting out relevant influencing factors based on manual experience, including sunshine hours, wind speed, grid electricity usage, natural gas usage, manpower usage in each link of product processing, carbon dioxide and other greenhouse gas emissions, etc., and then deriving data information of relevant factors (i.e., production influencing factors) from the historical order data of this industrial product.

[0193] For example, the electronic device may pre-store information on factors affecting the carbon footprint of the target product for subsequent direct retrieval.

[0194] In some examples, the influencing factor information includes p first influencing factors, and each of the p first influencing factors includes q samples;

[0195] The principal component analysis method is used to analyze the influencing factor information and obtain the target decision variables, including:

[0196] Construct a sample matrix based on p*q samples;

[0197] Standardize the sample matrix to obtain a standardized sample matrix;

[0198] For two different first influencing factors in the standardized sample matrix, according to formula (5), the correlation coefficient between the two different first influencing factors is determined to determine the covariance matrix of the standardized matrix;

[0199] Formula (5) includes:

[0200]

[0201] Among them, r ij represents the correlation coefficient between the i-th first influencing factor and the j-th first influencing factor; X ki represents the kth sample of the ith first influencing factor; X kj represents the kth sample of the jth first influencing factor;

[0202] According to the covariance matrix, determine the corresponding p eigenvalues;

[0203] According to the p eigenvalues ​​and formula (6), determine the p information contribution rates corresponding to the p eigenvalues ​​one by one;

[0204] Formula (6) includes:

[0205]

[0206] Among them, b i represents the information contribution rate of the i-th eigenvalue; i represents the i-th eigenvalue; λ krepresents the kth eigenvalue;

[0207] According to the information contribution rate and formula (7), the cumulative contribution rate is determined;

[0208] Formula (7) includes:

[0209]

[0210] Among them, a i represents the cumulative contribution rate;

[0211] When the cumulative contribution rate is greater than the cumulative contribution rate threshold, c samples with larger information contribution rates are selected from p samples as c principal components;

[0212] Comprehensively analyze the c principal components to obtain the target decision variables.

[0213] Illustratively, the first influencing factor may be an influencing factor corresponding to at least one of a raw material influencing factor, a production influencing factor, a transportation influencing factor, a usage influencing factor, and a recycling influencing factor.

[0214] Exemplarily, the sample matrix of the principal component analysis model (i.e., principal component analysis method) is determined as the factors affecting the carbon footprint (i.e., influencing factor information). Since the carbon footprint is divided into five parts: raw materials, production, transportation, use, and recycling, it is assumed that each of the five carbon footprints has p influencing factors (i.e., the first influencing factors), and each indicator (i.e., the first influencing factor) has q samples, and the sample matrix g of each category is obtained. i (i=1,2,3,4,5), and perform standardization on it to obtain the standardized sample matrix G i (i=1,2,3,4,5).

[0215]

[0216] Among them, X 11 represents a sample of the first influencing factor; X 12 represents a sample of the second influencing factor; and so on, X qp represents the q samples of the pth influencing factor; X1 = [X 11 , X 21 , …, X q1 ],X2=[X 12 , X 22 , …, X q2 ], and so on, X p =[X 1p , X 2p , …, X qp ].

[0217] The covariance matrix of the standardized sample is calculated using the above formula (6) R = (r ij ) m×m , where r ij It is the correlation coefficient between the ith indicator (i.e. the ith first influencing factor) and the jth indicator (i.e. the jth first influencing factor).

[0218] Calculate the p eigenvalues ​​λ1, λ2, ...λ of the covariance matrix R p and the corresponding eigenvectors.

[0219]

[0220] Among them, λ 11 Represents the characteristic value of a standardized sample of the first influencing factor; 21 represents the characteristic value of the two standardized samples of the first influencing factor; and so on, λ qp Represents the eigenvalues ​​of the q standardized samples of the pth influencing factor.

[0221] According to formula (7) and formula (8), the eigenvalue λ is calculated respectively: j The information contribution rate and cumulative contribution rate of (j=1,2,...,p).

[0222] When the cumulative contribution rate is close to 1 (a p =0.85,0.90,0.95), the first c indicator variables are selected as c principal components to replace the original p indicator variables, so that the c principal components can be comprehensively analyzed. The i-th principal component is:

[0223]

[0224] It should be noted that since each stage of the target product life cycle can be approximated as an independent system, the target carbon footprint optimization model can be as follows:

[0225] minCF=min(CF M +CF P +CF T +CF U +CF R )

[0226] =minCF M +minCF P +minCF T +minCF U +minCF R

[0227] Among them, minCF is the minimum value of the total carbon footprint of the target product; CF Mis the carbon footprint of raw materials, that is, the total amount of carbon dioxide emissions generated by the acquisition and transportation of the first raw materials of the target product; CF P is the transportation carbon footprint, which is the total amount of greenhouse gas emissions generated during the production process of the target product; CF T The carbon footprint of production is the total amount of carbon dioxide emissions generated by the transportation of the target product; CF U The carbon footprint is the carbon footprint generated by the target product during use; CF R The recycling carbon footprint is the total amount of greenhouse gas emissions generated by the target product during the recycling process.

[0228] In step S120, after obtaining the target carbon footprint optimization model of the target product in the target batch, the electronic device may also use a stochastic optimization algorithm to solve the target carbon footprint optimization model to obtain an optimization result.

[0229] Exemplarily, the random optimization algorithm may include a simulated annealing algorithm (SA), which reduces the probability of accepting a poor solution by gradually lowering the “temperature” (randomness), thereby allowing the search process to gradually converge from a global search to a local optimal solution.

[0230] In some embodiments, the target carbon footprint optimization model includes an optimization target;

[0231] The target carbon footprint optimization model is solved using a stochastic optimization algorithm to obtain the optimization results, including:

[0232] S1, initialization of the initial solution and the initial value of the optimization target;

[0233] S2, perturb to generate a new solution, and substitute the new solution into the optimization target to obtain a new value of the optimization target;

[0234] S3. If the new optimization target value is less than or equal to the initial optimization target value, the new solution is accepted; otherwise, the new solution is accepted according to the preset probability criterion;

[0235] S4. Reduce the initial value of the optimization target according to the preset cooling rate, and repeat steps S2 to S3 at the reduced initial value of the optimization target until the termination condition is met, and take the new solution after the final iteration as the optimization result.

[0236] Exemplarily, the termination condition may include that the temperature is lower than a set temperature value, the number of iterations reaches a preset number of iterations, or the new value of the optimization target after iteration is greater than a preset value (ie, meets the accuracy requirement).

[0237] It should be noted that the preset probability, set temperature value, preset number of iterations and preset value can be set according to actual conditions and are not limited here. For example, the preset probability can be 50%, the preset number of iterations can be 1000 times, etc.

[0238] That is to say, solving the target carbon footprint optimization model may include: ① Initialization: initialization of the initial solution and the objective function, setting the cooling rate. ② Generate a new solution: generate a new solution by perturbation, and calculate the objective function value of the current solution. ③ Decide whether to accept the new solution: calculate the objective function difference between the new solution and the current solution. If the new solution is better, accept the new solution. If the new solution is worse, accept the new solution with a certain probability criterion. ④ Cooling: lower the temperature according to the preset cooling function. ⑤ Termination condition: check whether the termination condition is met, such as the temperature is lower than the set value, the maximum number of iterations is reached, or the objective function value meets the accuracy requirements. ⑥ Output result: return the current solution as the final optimization result.

[0239] In step S130, after the electronic device solves the target carbon footprint optimization model using the stochastic optimization algorithm and obtains the optimization result, it can also optimize the carbon footprint of the target product using the optimization result.

[0240] In order to better understand the product carbon footprint optimization method provided in the embodiments of the present application, in some more specific implementations, such as Figure 4 As shown in FIG. 1 , the product carbon footprint optimization method is used to optimize the carbon footprint of industrial products. The product carbon footprint optimization method includes optimization subjects, segmented optimization models, and optimization model solutions.

[0241] The entities involved in the optimization include raw material suppliers, transportation companies (i.e. the raw material transportation formula), product manufacturing units, transportation units (i.e. the target product transportation units), users, maintenance companies and waste disposal companies.

[0242] The phased optimization model may include raw material carbon footprint (i.e. raw material carbon footprint model), production carbon footprint (i.e. production carbon footprint model), transportation carbon footprint (i.e. transportation carbon footprint model), use carbon footprint (i.e. use carbon footprint model) and recycling carbon footprint (i.e. recycling carbon footprint model).

[0243] The objective function of the raw material carbon footprint (i.e. the first type of optimization objective) is minCF M The constraints of raw material carbon footprint (i.e., the first type of constraints) include raw material price constraints (i.e., the first raw material price constraints) and transportation price constraints (i.e., the first transportation price constraints), etc.; the decision variables of raw material carbon footprint (i.e., the first type of decision variables) include suppliers (i.e., each first raw material supplier) and transportation methods (i.e., each raw material transportation method), etc.

[0244] The objective function of production carbon footprint (i.e. the second type of optimization objective) is minCF PThe constraints of production carbon footprint (i.e., the second type of constraints) include equipment capacity (i.e., each first equipment capacity constraint) and power balance constraint (i.e., the first power balance constraint), etc.; the decision variables of production carbon footprint (i.e., the second type of decision variables) include gas purchase volume (i.e., the first gas purchase volume), power grid purchase volume (i.e., the first power grid purchase volume), and the power of each equipment (i.e., each first equipment power), etc.

[0245] The objective function of the transport carbon footprint (i.e. the third type of optimization objective) is minCF T ; The constraints of the transport carbon footprint (i.e. the third type of constraints) include transport price constraints (i.e. the second transport price constraints) and carbon emission constraints (i.e. the second carbon emission constraints); the decision variables of the transport carbon footprint (i.e. the third type of decision variables) include transport mode (i.e. the transport mode of each target product) and transport distance (i.e. the transport distance of each target product), etc.

[0246] The objective function of carbon footprint (i.e. the fourth type of optimization objective) is minCF U ;Optimization methods for using carbon footprint include energy-saving price incentives and peak-shaving price incentives.

[0247] The objective function of recycling carbon footprint (i.e. the fifth type of optimization objective) is minCF R ; The constraints for recovering carbon footprint (i.e., the fifth type of constraints) include equipment capacity (i.e., the second equipment capacity constraints) and power balance constraints (i.e., the second power balance constraints), etc.; the decision variables for recovering carbon footprint (i.e., the fourth type of decision variables) include gas purchase volume (i.e., the second gas purchase volume), power grid purchase volume (i.e., the second power grid purchase volume), and the power of each device (i.e., the power of each second device).

[0248] Solving the optimization model may include generating an initial solution w and calculating an objective function CFn(w); perturbing to generate a new solution w′ and calculating an objective function CFn(w′); calculating ΔCF=CFn(w′)-CFn(w); determining whether ΔCF is less than or equal to 0, and if so, accepting the new solution, w=w′, CFn(w)-=CFn(w′); if not, accepting the new solution according to the Metropolis criterion; determining whether the number of iterations has been reached; if not, jumping to the step of perturbing to generate a new solution w′ and calculating an objective function CFn(w′); if yes, determining whether a termination condition is met; if yes, the operation ends and the optimal solution is returned; if not, slowly lowering the temperature, repeating the number of iterations, and jumping to the step of perturbing to generate a new solution w′ and calculating an objective function CFn(w′).

[0249] It should be noted that the embodiments of the present application are applicable to the carbon footprint optimization of multiple parties involved in the entire life cycle of industrial enterprise products with multi-energy coupling characteristics. The embodiments of the present application select the influencing factors with strong practicability as decision variables for the subjects participating in the optimization of each link in the entire life cycle (raw material suppliers, transportation companies, manufacturing enterprises, product users, waste treatment companies), and establish a detailed carbon footprint optimization model (i.e., establish a carbon footprint optimization model with clear optimization goals, decision variables and constraints), which can maximize the potential for carbon reduction and improve the standardization and practicability of carbon footprint optimization.

[0250] In order to better understand the product carbon footprint optimization method provided in the embodiment of the present application, a more specific implementation method is described below.

[0251] like Figure 5 As shown, the product carbon footprint optimization method provided in the embodiment of the present application may include constructing a full life cycle carbon emission factor library; determining the full life cycle carbon footprint accounting boundary; principal component analysis of factors affecting the full life cycle carbon footprint; and multi-link and multi-subject carbon footprint optimization throughout the life cycle.

[0252] Example 2

[0253] like Figure 6 As shown, the product carbon footprint optimization device provided in the embodiment of the present application may include an acquisition module 610, a solution module 620 and an optimization module 630.

[0254] The acquisition module 610 is used to acquire a target carbon footprint optimization model of a target product in a target batch, wherein the target carbon footprint optimization model includes at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model;

[0255] The solving module 620 is connected to the obtaining module 610 and is used to solve the target carbon footprint optimization model using a random optimization algorithm to obtain an optimization result;

[0256] The optimization module 630 is connected to the solution module 620 and is used to optimize the carbon footprint of the target product using the optimization result.

[0257] In some embodiments, the target carbon footprint optimization model includes a feedstock carbon footprint optimization model, and the feedstock carbon footprint optimization model includes a first type of optimization objective, a first type of decision variables, and a first type of constraints;

[0258] The first type of optimization objectives include the minimum total amount of carbon dioxide emissions generated by the first raw material acquisition and transportation links of the target product:

[0259]

[0260] Among them, minCF M represents the minimum value of the total amount of carbon dioxide emissions generated by the acquisition and transportation of the first raw material of the target product; P represents the number of target products in the target batch; n represents the total amount of the first raw material type required to produce the target product; Indicates the usage of the first raw material of the i-th category in the process of producing the target product; EF i M represents the carbon emission factor of the recovery and preparation process of the first raw material of the i-th category in the process of producing the target product; β represents the return empty driving rate; L i Indicates the transportation distance of the first raw material of the i-th category in the process of producing the target product; ET i represents the carbon emission factor of the transportation process of the first raw material of the i-th category;

[0261] The first type of decision variables includes: at least one of each first raw material supplier, each raw material transportation method, each raw material transportation distance and each first raw material fuel type;

[0262] The first type of constraint condition includes at least one of a first raw material price constraint, a first transportation price constraint, and a first carbon emission constraint;

[0263] The first carbon emission constraint includes the first type of information belonging to the target carbon emission factor library, and the first type of information includes at least one of the carbon emission factors corresponding to each first raw material of each first raw material supplier, the transportation method of each supplier, the transportation distance of each raw material, the transportation type of each first raw material, and the carbon emission factor of each fuel; the first carbon emission constraint corresponds to the first type of decision variable.

[0264] In some embodiments, the target carbon footprint optimization model includes an optimization target;

[0265] The solution module 620 is specifically used for:

[0266] S1, initialization of the initial solution and the initial value of the optimization target;

[0267] S2, perturb to generate a new solution, and substitute the new solution into the optimization target to obtain a new value of the optimization target;

[0268] S3. If the new optimization target value is less than or equal to the initial optimization target value, the new solution is accepted; otherwise, the new solution is accepted according to the preset probability criterion;

[0269] S4. Reduce the initial value of the optimization target according to the preset cooling rate, and repeat steps S2 to S3 at the reduced initial value of the optimization target until the termination condition is met, and take the new solution after the final iteration as the optimization result.

[0270] In some embodiments, the target carbon footprint optimization model includes at least one of a feedstock carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model;

[0271] The acquisition module 610 includes:

[0272] A first establishing submodule is used to establish a raw material carbon footprint optimization model according to a first type of optimization objective, a first type of decision variable and a first type of constraint condition when the target carbon footprint optimization model includes a raw material carbon footprint optimization model;

[0273] and / or, a second establishing submodule, for establishing a production carbon footprint optimization model according to a second type of optimization objective, a second type of decision variable, and a second type of constraint condition when the target carbon footprint optimization model includes a production carbon footprint optimization model;

[0274] and / or, a third establishing submodule, for establishing a transport carbon footprint optimization model according to a third type of optimization objective, a third type of decision variables and a third type of constraint conditions when the target carbon footprint optimization model includes a transport carbon footprint optimization model;

[0275] and / or, a fourth establishing submodule, for establishing the use carbon footprint optimization model according to a fourth type of optimization objective, a fourth type of decision variable and a fourth type of constraint condition when the target carbon footprint optimization model includes the use carbon footprint optimization model;

[0276] And / or, a fifth establishing submodule, for establishing a recycling carbon footprint optimization model according to a fifth type of optimization objective, a fifth type of decision variables and a fifth type of constraint conditions when the target carbon footprint optimization model includes a recycling carbon footprint optimization model.

[0277] The product carbon footprint optimization device provided in the embodiment of the present application can be used to execute the product carbon footprint optimization method, that is, it has the beneficial effects and implementation methods of the product carbon footprint optimization method provided in Example 1 of the present application. For details, please refer to the specific description of the product carbon footprint optimization method in the above Example 1, which will not be repeated in this embodiment.

[0278] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present application, but the present application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of the present application, and these modifications and improvements are also considered to be within the scope of protection of the present application.

Claims

1. A method for optimizing product carbon footprint, characterized in that: include: Obtaining a target carbon footprint optimization model for a target product in a target batch, wherein the target carbon footprint optimization model includes at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model; Solving the target carbon footprint optimization model using a random optimization algorithm to obtain an optimization result; The optimization result is used to optimize the carbon footprint of the target product.

2. The method according to claim 1, characterized in that The target carbon footprint optimization model includes a raw material carbon footprint optimization model, and the raw material carbon footprint optimization model includes a first type of optimization target, a first type of decision variables and a first type of constraint conditions; The first type of optimization objectives include the minimum total amount of carbon dioxide emissions generated by the first raw material acquisition and transportation links of the target product: Among them, minCF M represents the minimum value of the total amount of carbon dioxide emissions generated by the acquisition and transportation of the first raw material of the target product; P represents the number of target products in the target batch; n represents the total amount of the first raw material type required to produce the target product; Indicates the usage of the first raw material of the i-th category in the process of producing the target product; EF i M represents the carbon emission factor of the recovery and preparation process of the first raw material of the i-th category in the process of producing the target product; β represents the return empty driving rate; L i Indicates the transportation distance of the first raw material of the i-th category in the process of producing the target product; ET i represents the carbon emission factor of the transportation process of the first raw material of the i-th category; The first type of decision variables includes: at least one of each first raw material supplier, each raw material transportation method, each raw material transportation distance and each first raw material fuel type; The first type of constraint condition includes at least one of a first raw material price constraint, a first transportation price constraint, and a first carbon emission constraint; The first carbon emission constraint includes a first type of information belonging to a target carbon emission factor library, and the first type of information includes at least one of the carbon emission factors corresponding to each first raw material of each first raw material supplier, the transportation method of each supplier, the transportation distance of each raw material, the transportation type of each first raw material, and the carbon emission factor of each fuel; the first carbon emission constraint corresponds to the first type of decision variables.

3. The method according to claim 1, characterized in that: The target carbon footprint optimization model includes a production carbon footprint optimization model, and the production carbon footprint optimization model includes a second type of optimization target, a second type of decision variables, and a second type of constraint conditions; The second type of optimization objectives include the minimum total greenhouse gas emissions generated by the target product during the production process: Among them, min CF P represents the minimum value of the total greenhouse gas emissions generated by the target product during the production process; P represents the number of target products in the target batch; EL1 represents the carbon emission factor of the first terminal energy in the production process of the target product; P load (t) represents the park electricity load at time t during the production process of the target product; EL2 represents the carbon emission factor of the second terminal energy during the production process of the target product; H load (t) represents the heat load of the park at time t during the production process of the target product; EL3 represents the carbon emission factor of the third terminal energy during the production process of the target product; C load (t) represents the cooling load of the park at time t during the production of the target product; EL4 represents the carbon emission factor of the fourth terminal energy during the production of the target product; A load (t) represents the compressed air load of the park at time t during the production process of the target product; The second type of decision variables includes: at least one of the first gas purchase amount, the first power grid purchase amount, the power of each first device, the start / stop state of each first device, the first electric energy storage, and the charging / discharging state of the first charging pile; The second type of constraint conditions include at least one of the first equipment capacity constraints, the first climbing rate constraints, the first power balance constraints, the first energy storage equipment constraints, the first charging pile constraints and the first movable load constraints; the first power balance constraints include at least one of the first electric power balance constraints, the first cold power balance constraints, the first air compressor power balance constraints, the first heat power balance constraints and the first natural gas power balance constraints.

4. The method according to claim 3, characterized in that The park electrical load P at time t during the production process of the target product load (t) is calculated by formula (1); The formula (1) include: P load (t)=P es (t)+P cp (t)+P tra (t)+P qt (t) (1) Among them, P es (t) represents the electric energy storage power of the target product at time t during the production process; P cp (t) represents the charging pile power of the target product at time t during the production process; P tra (t) represents the transferable load power of the target product at time t during the production process; P qt (t) represents the power of other electrical loads of the target product at time t during the production process; The heat load H of the park at time t during the production process of the target product load (t) is calculated by formula (2); The formula (2) includes: Among them, η HP，t represents the heat pump energy conversion efficiency of the target product at time t during the production process; P HP (t) represents the heat pump power of the target product at time t during the production process; Indicates the thermal power of the gas boiler at time t during the production process of the target product; The cooling load C of the park at time t during the production process of the target product load (t) is calculated by formula (3); The formula (3) includes: C load (t)=η EC，t P EC (t) (3) Among them, η EC，t represents the electric refrigeration energy conversion efficiency of the target product at time t during the production process; P EC (t) represents the power consumption of the compression refrigerator at time t during the production process of the target product; The compressed air load A of the park at time t during the production process of the target product load (t) is calculated by formula (4): The formula (4) includes: A looad (t)=η AC，t P AC (t) (4); Among them, η AC，t P represents the energy conversion efficiency of the air compressor at time t during the production process of the target product; AC (t) represents the electric power of the air compressor at time t during the production process of the target product.

5. The method according to claim 1, characterized in that The target carbon footprint optimization model includes a transportation carbon footprint optimization model, and the transportation carbon footprint optimization model includes a third type of optimization target, a third type of decision variables and a third type of constraint conditions; The third type of optimization objectives include the minimum total amount of carbon dioxide emissions generated by the transportation of target products: Among them, min CF T represents the minimum value of the total amount of carbon dioxide emissions generated by the transportation of the target product; P represents the number of target products in the target batch; M′ represents the total weight of a batch of target products; β represents the return empty trip rate; L′ i Indicates the transportation distance of the process of transporting the target product; ET i Indicates the carbon emission factor of the target product transportation process; The third type of decision variables includes: at least one of the transportation mode of each target product and the transportation distance of each target product: The third type of constraint condition includes at least one of the second transportation price constraint and the second carbon emission constraint; When the third type of constraint includes the second carbon emission constraint, the second carbon emission constraint includes second type of information belonging to the target carbon emission factor library, and the second type of information includes at least one of the transportation methods of each supplier and the transportation distance of each target product; the second carbon emission constraint corresponds to the third type of decision variable.

6. The method according to claim 2 or 5, characterized in that: The target carbon emission factor library includes at least one of the first carbon emission factor library, the second carbon emission factor library, the third carbon emission factor library, the fourth carbon emission factor library and the fifth carbon emission factor library; The process of establishing the target carbon emission factor library includes at least one of the following: Establishing a first carbon emission factor library based on at least one of the raw material type, supplier, raw material address and raw material carbon emission factor; Establishing a second carbon emission factor library according to first information under different modes of transportation, wherein the first information includes at least one of distance information, transportation unit price, fuel type, fuel usage, and fuel carbon emission factor; Establish a third carbon emission factor library based on carbon emission factors of various types of energy; According to various types of manpower carbon emission factors, the fourth carbon emission factor library is established; Based on the global warming potential of each greenhouse gas, the fifth carbon emission factor library is established.

7. The method according to claim 1, characterized in that The target carbon footprint optimization model includes a use carbon footprint optimization model, and the use carbon footprint optimization model includes a fourth type of optimization target, a fourth type of decision variables and a fourth type of constraint conditions, The fourth type of optimization objectives include the minimum carbon footprint generated by the target product during use: Among them, minCF U It represents the minimum value of the carbon footprint generated by the target product during use; P represents the number of target products in the target batch; g represents the type of energy consumed by the target product during use and maintenance; It represents the final usage of the i-th energy during the use of the target product; Represents the energy carbon emission factor of the i-th energy during the use of the target product. The fourth type of decision variables includes: the final use of the i-th energy The fourth type of constraints include: in, It represents the original usage of the i-th energy before price incentives during the use of the target product; It represents the final usage rate of the i-th energy during the use of the target product; represents the original usage rate of the i-th energy source before price incentives during the use of the target product; C1 is the energy-saving incentive price; C 2,t represents the peak load incentive price at time t; k represents the consumption The number of hours used for the amount of energy.

8. The method according to claim 1, characterized in that The target carbon footprint optimization model includes a recycling carbon footprint optimization model, and the recycling carbon footprint optimization model includes a fifth type of target, a fifth type of decision variables and a fifth type of constraint conditions; The fifth type of optimization objectives include the minimum total greenhouse gas emissions generated by the target product during the recycling process: Among them, minCF R represents the minimum value of the total greenhouse gas emissions generated by the target product during the recycling process; P represents the number of target products in the target batch; EL'1 represents the carbon emission factor of the first terminal energy during the recycling process of the target product; P' load (t') represents the park electricity load at time t' during the recycling process of the target product; EL'2 represents the carbon emission factor of the second terminal energy during the recycling process of the target product; H load '(t') represents the heat load of the park at time t' during the recycling process of the target product; EL'3 represents the carbon emission factor of the third terminal energy during the recycling process of the target product; C load '(t') represents the cooling load of the park at time t' during the recycling process of the target product; EL'4 represents the carbon emission factor of the fourth terminal energy during the recycling process of the target product; A load '(t') represents the compressed air load of the park at time t' during the recycling process of the target product; The fifth category of decision variables includes: at least one of the second gas purchase amount, the second power grid purchase amount, the power of each second device, the start / stop state of each second device, the second electric energy storage, and the charging / discharging state of the second charging pile; The fifth category of constraints includes at least one of the second equipment capacity constraints, the second climbing rate constraints, the second power balance constraints, the second energy storage equipment constraints, the second charging pile constraints and the second movable load constraints; the second power balance constraints include at least one of the second electric power balance constraints, the second cold power balance constraints, the second air compressor power balance constraints, the second heat power balance constraints and the second natural gas power balance constraints.

9. The method according to any one of claims 1 to 5, 7 to 8, characterized in that: The target carbon footprint optimization model includes a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model and a recycling carbon footprint optimization model; The raw material carbon footprint optimization model includes a first type of decision variables, the production carbon footprint optimization model includes a second type of decision variables, the transportation carbon footprint optimization model includes a third type of decision variables, the use carbon footprint optimization model includes a fourth type of decision variables, and the recycling carbon footprint optimization model includes a fifth type of decision variables; The target decision variable includes at least one of the first type of decision variable, the second type of decision variable, the third type of decision variable, the fourth type of decision variable and the fifth type of decision variable; The process of obtaining the target decision variable includes: Obtain information on factors affecting the carbon footprint of target products; The influencing factor information is analyzed using the principal component analysis method to obtain the target decision variable.

10. The method according to claim 9, characterized in that The influencing factor information includes p first influencing factors, and each of the p first influencing factors includes q samples; The principal component analysis method is used to analyze the influencing factor information and obtain the target decision variables, including: Construct a sample matrix based on p*q samples; Standardize the sample matrix to obtain a standardized sample matrix; For two different first influencing factors in the standardized sample matrix, according to formula (5), determine the correlation coefficient between the two different first influencing factors to determine the covariance matrix of the standardized matrix; The formula (5) includes: Among them, r ij represents the correlation coefficient between the i-th first influencing factor and the j-th first influencing factor; X ki represents the kth sample of the ith first influencing factor; X kj represents the kth sample of the jth first influencing factor; According to the covariance matrix, determining its corresponding p eigenvalues; According to the p eigenvalues ​​and formula (6), determine the p information contribution rates corresponding to the p eigenvalues ​​one by one; The formula (6) includes: Among them, b i represents the information contribution rate of the i-th eigenvalue; i represents the i-th eigenvalue; λ k represents the kth eigenvalue; According to the information contribution rate and formula (7), the cumulative contribution rate is determined; The formula (7) includes: Among them, a i represents the cumulative contribution rate; When the cumulative contribution rate is greater than the cumulative contribution rate threshold, c samples with larger information contribution rates are selected from the p samples as c principal components; Comprehensively analyze the c principal components to obtain the target decision variables.

11. The method according to any one of claims 1 to 5, 7 to 8, characterized in that: The target carbon footprint optimization model includes an optimization target; The target carbon footprint optimization model is solved by using a random optimization algorithm to obtain optimization results, including: S1, initialization of the initial solution and the initial value of the optimization target; S2, perturb to generate a new solution, and substitute the new solution into the optimization target to obtain a new value of the optimization target; S3. If the new optimization target value is less than or equal to the initial optimization target value, the new solution is accepted; otherwise, the new solution is accepted according to the preset probability criterion; S4. Reduce the initial value of the optimization target according to the preset cooling rate, and repeat steps S2 to S3 at the reduced initial value of the optimization target until the termination condition is met, and take the new solution after the final iteration as the optimization result.

12. The method according to any one of claims 1 to 5, 7 to 8, characterized in that The target carbon footprint optimization model includes at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model; The step of obtaining a target carbon footprint optimization model for a target product in a target batch includes: In the case where the target carbon footprint optimization model includes a raw material carbon footprint optimization model, establishing the raw material carbon footprint optimization model according to the first type of optimization objectives, the first type of decision variables and the first type of constraints; and / or, when the target carbon footprint optimization model includes a production carbon footprint optimization model, establishing a production carbon footprint optimization model according to the second type of optimization objectives, the second type of decision variables and the second type of constraints; and / or, when the target carbon footprint optimization model includes a transport carbon footprint optimization model, establishing a transport carbon footprint optimization model according to the third type of optimization objectives, the third type of decision variables and the third type of constraints; And / or, when the target carbon footprint optimization model includes a use carbon footprint optimization model, a use carbon footprint optimization model is established according to the fourth type of optimization objectives, the fourth type of decision variables and the fourth type of constraints; and / or, when the target carbon footprint optimization model includes a recycling carbon footprint optimization model, a recycling carbon footprint optimization model is established according to the fifth type of optimization objectives, the fifth type of decision variables and the fifth type of constraints.

13. A product carbon footprint optimization device, characterized in that: include: An acquisition module, used to acquire a target carbon footprint optimization model of a target product in a target batch, wherein the target carbon footprint optimization model includes at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model; A solution module, connected to the acquisition module, for solving the target carbon footprint optimization model using a random optimization algorithm to obtain an optimization result; The optimization module is connected to the solution module and is used to optimize the carbon footprint of the target product using the optimization result.

14. The device according to claim 13, characterized in that The target carbon footprint optimization model includes a raw material carbon footprint optimization model, and the raw material carbon footprint optimization model includes a first type of optimization target, a first type of decision variables and a first type of constraint conditions; The first type of optimization objectives include the minimum total amount of carbon dioxide emissions generated by the first raw material acquisition and transportation links of the target product: Among them, minCF M represents the minimum value of the total amount of carbon dioxide emissions generated by the acquisition and transportation of the first raw material of the target product; P represents the number of target products in the target batch; n represents the total amount of the first raw material type required to produce the target product; Indicates the usage of the first raw material of the i-th category in the process of producing the target product; EF i M represents the carbon emission factor of the recovery and preparation process of the first raw material of the i-th category in the process of producing the target product; β represents the return empty driving rate; L i Indicates the transportation distance of the first raw material of the i-th category in the process of producing the target product; ET i represents the carbon emission factor of the transportation process of the first raw material of the i-th category; The first type of decision variables includes: at least one of each first raw material supplier, each raw material transportation method, each raw material transportation distance and each first raw material fuel type; The first type of constraint condition includes at least one of a first raw material price constraint, a first transportation price constraint, and a first carbon emission constraint; The first carbon emission constraint includes a first type of information belonging to a target carbon emission factor library, and the first type of information includes at least one of the carbon emission factors corresponding to each first raw material of each first raw material supplier, the transportation method of each supplier, the transportation distance of each raw material, the transportation type of each first raw material, and the carbon emission factor of each fuel; the first carbon emission constraint corresponds to the first type of decision variables.

15. The device according to claim 13, characterized in that The target carbon footprint optimization model includes an optimization target; Solving module, specifically used for: S1, initialization of the initial solution and the initial value of the optimization target; S2, perturb to generate a new solution, and substitute the new solution into the optimization target to obtain a new value of the optimization target; S3. If the new optimization target value is less than or equal to the initial optimization target value, the new solution is accepted; otherwise, the new solution is accepted according to the preset probability criterion; S4. Reduce the initial value of the optimization target according to the preset cooling rate, and repeat steps S2 to S3 at the reduced initial value of the optimization target until the termination condition is met, and take the new solution after the final iteration as the optimization result.

16. The device according to claim 13, characterized in that The target carbon footprint optimization model includes at least one of a raw material carbon footprint optimization model, a production carbon footprint optimization model, a transportation carbon footprint optimization model, a usage carbon footprint optimization model, and a recycling carbon footprint optimization model; The acquisition module comprises: A first establishing submodule is used to establish a raw material carbon footprint optimization model according to a first type of optimization objective, a first type of decision variable and a first type of constraint condition when the target carbon footprint optimization model includes a raw material carbon footprint optimization model; and / or, a second establishing submodule, for establishing a production carbon footprint optimization model according to a second type of optimization objective, a second type of decision variable, and a second type of constraint condition, when the target carbon footprint optimization model includes a production carbon footprint optimization model; and / or, a third establishing submodule, for establishing a transport carbon footprint optimization model according to a third type of optimization objective, a third type of decision variables and a third type of constraint conditions, when the target carbon footprint optimization model includes a transport carbon footprint optimization model; and / or, a fourth establishing submodule, for establishing the use carbon footprint optimization model according to a fourth type of optimization objective, a fourth type of decision variable and a fourth type of constraint condition, when the target carbon footprint optimization model includes the use carbon footprint optimization model; And / or, a fifth establishing submodule, used to establish a recycling carbon footprint optimization model according to a fifth type of optimization objective, a fifth type of decision variable and a fifth type of constraint condition when the target carbon footprint optimization model includes a recycling carbon footprint optimization model.

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