A cloud-based intelligent carbon footprint assessment and management system and its assessment method

Abstract

The present invention discloses an intelligent carbon footprint assessment and management system based on the cloud and its assessment method. The system includes a front-end display unit and a back-end processing unit. The front-end display unit includes a smart visualization module designed based on DataV data visualization. The smart visualization module is used for visual presentation and analysis according to different dimensions. The back-end processing unit includes a data and application support module based on cloud computing and big data technologies and a basic resource support module based on cloud databases and network security. The present invention combines technical means such as cloud computing, mobile Internet, RFID, etc. to realize the carbon footprint evaluation, analysis and prediction of carbon emission units, thereby improving energy efficiency, reducing energy consumption, reducing operating costs, and promoting the healthy and sustainable development of enterprises.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission management, and particularly relates to an intelligent carbon footprint assessment and management system based on the cloud and an assessment method thereof. Background Art

[0002] With the progress of science and technology and the rapid development of modern artificial intelligence technology, new vitality has been injected into the traditional carbon footprint accounting method. It can be said that modern carbon footprint accounting and assessment methods have entered a new stage of automation, intelligence, and personalization, and are increasingly developing towards accurate and predictive effects. At the same time, cloud technology based on technologies such as 5G and the Internet of Things effectively saves computing resources and management costs, makes full use of big data resources and cloud computing technology, and provides a more convenient way to realize the high efficiency, accuracy, and intelligence of carbon footprint assessment and management.

[0003] Currently, the industry generally recognizes that the carbon footprint can be defined as the direct and indirect greenhouse gas (GHG) emissions caused by a product or activity during its life cycle. The carbon footprint is generally measured in units of carbon dioxide equivalent (CO2e), and the carbon dioxide equivalent is the equivalent of converting other greenhouse gases into carbon dioxide through the global warming potential (GWP). Internationally, it is required that carbon footprint disclosure or evaluation should cover all stages of the life cycle, that is, all information including raw materials (including transportation), manufacturing, distribution and retail, consumer use, and disposal or reuse stages. The main types of greenhouse gases are the 6 greenhouse gases specified in the Kyoto Protocol, namely carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6).

[0004] Current carbon footprint accounting methods vary: The analysis results of the life cycle assessment method are targeted and applicable to micro-systems. However, this method has system boundary problems and requires a large amount of human and material resources; The input and production analysis method takes the entire system as the boundary, requires less human and material resources during accounting, and is applicable to the carbon footprint accounting of macro-systems; The hybrid cycle carbon footprint assessment method integrates the advantages of the previous two methods and is the current research hotspot of carbon footprint accounting.

[0005] Problems existing in the current carbon footprint accounting and method practice:

[0006] (1) In terms of methods, there is no unified carbon footprint method accounting framework system, and there is no strong comparability between the results of different calculation methods. In addition, there are still disputes in dealing with the carbon footprints of capital goods and land use changes in existing methods.

[0007] (2) Current applied research mainly focuses on calculating the amount of carbon footprint, and there is less research on the temporal and spatial distribution trends and driving forces of carbon footprint.

[0008] (3) In terms of uncertainty, due to reasons related to methods and data sources, there are currently few studies on the uncertainty analysis of accounting results, and there is a lack of experience in the uncertainty analysis research of estimating and reducing carbon footprint results.

[0009] (4) For current carbon footprint accounting software, there is no strict unified standard for visualization reports, and the workload of managers is very large.

[0010] In response to the above problems, there is an urgent need for innovative design based on the original carbon footprint accounting system relying on the Internet and the Internet of Things. Summary of the Invention

[0011] Object of the Invention: In order to overcome the deficiencies in the prior art, the present invention provides an intelligent carbon footprint assessment and management system based on the cloud and its assessment method. By integrating technical means such as cloud computing, mobile Internet, and RFID, it realizes the carbon footprint evaluation, analysis, and prediction of carbon emission units, thereby improving energy efficiency, reducing energy consumption, reducing operating costs, and promoting the sound and sustainable development of enterprises.

[0012] Technical Solution: To achieve the above object, the present invention provides an intelligent carbon footprint assessment method based on the cloud, including the following steps:

[0013] S1: Collect and import carbon emission factor types, carbon emissions, and carbon emission factor data sources;

[0014] S2: Check and quantify carbon emission factors through a carbon emission factor database;

[0015] S3: Calculate the product carbon footprint according to the carbon footprint calculation formula;

[0016] S4: Conduct uncertainty analysis on the product carbon footprint calculation result based on the carbon footprint calculation result in step S3 and the carbon emission factor data source in step S1;

[0017] S5: Conduct potential analysis and contribution analysis on the current product plan based on the results obtained in steps S1 - S4;

[0018] S6: Generate alternative solutions based on the results obtained in steps S1 - S5;

[0019] S7: Evaluate the alternative solutions;

[0020] S8: Generate a product carbon footprint report based on the results obtained in steps S1 - S6;

[0021] S9: Present the product carbon footprint report with intelligent visualization.

[0022] Furthermore, the specific formula for calculating the carbon footprint in step S3 is as follows:

[0023] For grid enterprises:

[0024]

[0025] Among them, REC 容量，i refers to the sulfur hexafluoride capacity of the decommissioned equipment i, and REC 回收，i refers to the actual recovered amount of sulfur hexafluoride of the decommissioned equipment i. REP 容量，j refers to the sulfur hexafluoride capacity of the repaired equipment j, and REP 回收，j refers to the actual recovered amount of sulfur hexafluoride of the repaired equipment j. GWP SF6 refers to the greenhouse gas potential of sulfur hexafluoride. EL 上网 refers to the electricity generated by the power plant and fed into the grid, and EL 输出 refers to the electricity input from other provinces, and EL 售电 refers to the electricity output to other provinces. EF 电网 refers to the annual average power supply emission factor of the regional power grid;

[0026] For power generation enterprises:

[0027]

[0028]

[0029] Among them, AD 电力 refers to the net electricity purchased by the enterprise, and EF 电力 refers to the annual average power supply emission factor of the regional power grid. FC i refers to the consumption of fossil fuels, and EF i refers to the combustion emission factor. B k refers to the consumption of desulfurization agents, and I k refers to the carbonate content in the desulfurization agent, and EF k refers to the carbonate emission factor. TR refers to the conversion rate;

[0030] For electronic equipment manufacturing enterprises:

[0031]

[0032] Among them, AD i refers to the net consumption of fossil fuels, and EF i refers to the emission factor. h refers to the gas residue ratio of the raw material gas container. IB i refers to the initial inventory of the raw material gas i, and O i refers to the purchase volume of the raw material gas i, and IE i refers to the initial inventory of the raw material gas i, and S i The external sales or output volume of the raw material gas i, Ui Refers to the utilization rate of feed gas i, a i Refers to the collection efficiency of feed gas i, d i Refers to the removal efficiency of feed gas i, GWP i Refers to the global warming potential of feed gas i, B ij Refers to the conversion factor of by-product j generated from feed gas i, a i Refers to the collection efficiency of by-product j, d j Refers to the removal efficiency of by-product j, GWP i Refers to the global warming trend of by-product j, AD 电力 Refers to the net purchased electricity of the enterprise, EF 电力 Refers to the annual average power supply emission factor of the regional power grid, AD 热力 Refers to the net purchased heat quantity, EF 热力 Refers to the heat supply emission factor;

[0033] For the iron and steel industry:

[0034]

[0035] Among them, FC i Refers to the net consumption of fossil fuels, EF i Refers to the fuel emission factor, P 溶剂 Refers to the net consumption of solvents, EF 溶剂 Refers to the emission factor, P 电极 Refers to the amount of electrodes consumed in electric arc furnace steelmaking and refining, etc., EF 电极 Refers to the fuel emission factor, M 原料 Refers to the purchased quantity of carbon-containing raw materials, EF 原料 Refers to the emission factor, AD 固碳 Refers to the output of carbon sequestration products, EF 固碳 Refers to the emission factor, AD 电力 Refers to the net purchased electricity quantity, EF 电力 Refers to the annual average power supply emission factor of the regional power grid, AD 热力 Refers to the net purchased heat quantity, EF 热力 Refers to the heat supply emission factor;

[0036] For the chemical industry:

[0037]

[0038] Among them, AD 电力 Refers to the net purchased electricity, EF 电力 Refers to the annual average power supply emission factor of the regional power grid, AD 热力 Refers to the net purchased heat quantity, EF Thermal refers to the heat supply emission factor, AD refers to the consumption of fossil fuels, EF refers to the emission factor, AD rrefers to the input quantity of raw materials, CC refers to the carbon content, AD p refers to the output of carbon-containing products, AD w refers to the output of carbon-containing waste, AD i refers to the consumption of carbonates for raw materials, fluxes, and desulfurizers, EF i refers to the N 2 O generation factor of production technology type j, AD j refers to the adipic acid output of production process j, a k refers to the N 2 O removal efficiency of different tail gas treatment types k in adipic acid production, Q refers to the volume of Co 2 gas recovered and supplied externally, PUR CO2 refers to CO 2 purity of the gas supplied externally.

[0039] Furthermore, in step S4, the Monte Carlo method is used to achieve the uncertainty analysis of the carbon footprint. The specific analysis method is as follows:

[0040] A1: Construct or describe the probability process:

[0041] Generate random variable factors with a known probability distribution through the constructed product carbon footprint probability prediction model, and transform the product carbon footprint variable factors without random properties into product carbon footprint variable factors with random properties;

[0042] A2: Realize sampling from the known probability distribution:

[0043] Generate and sample the random variable factors of the product carbon footprint under different scenarios by collecting various variable factors of the product carbon footprint and then randomly generating various variable factors of the product for different scenarios by the computer;

[0044] A3: Establish various estimators and generate an uncertainty analysis report:

[0045] Determine a random variable factor as the solution to the required problem, which is called the unbiased estimate of the product carbon footprint; establish various estimators, examine and record the results of the simulation experiment, obtain the required product carbon footprint information from it, and generate a product carbon footprint uncertainty analysis report.

[0046] Furthermore, the construction method of the product carbon footprint probability prediction model in step A1 is as follows:

[0047] B1: Conduct principal component analysis on the carbon emission factors to obtain the variance percentage;

[0048] B2: Use the extracted principal components as new variables to replace the original variables for multiple linear regression analysis to obtain a regression equation. Substitute the principal components with the loadings of their respective independent variables to obtain the regression equation for each explanatory variable.

[0049] B3: Use the regression residuals of the regression equation to plot a histogram, and plot the standardized residuals and standardized predicted values as a scatter plot to test the homoscedasticity. Thus, the construction of the product carbon footprint probability prediction model is completed.

[0050] Furthermore, the generation of the alternative plan in step S6 is as follows:

[0051] Based on the results obtained in steps S1 - S5, generate an alternative plan for the product operation mode according to the existing product operation mode, referring to the China EDP and ISO standards.

[0052] Furthermore, the evaluation of the alternative plan in step S7 is as follows:

[0053] C1: Rely on the basic resource service module to achieve the virtual operation and carbon emission analysis of the alternative plan.

[0054] C2: Analyze and compare the carbon reduction indices of the alternative plan and the original plan, and give an evaluation of the alternative plan.

[0055] Furthermore, for the analysis of the carbon emission reduction potential of the existing operation mode of the current product in step S5, for the chemical industry, the specific analysis method is as follows:

[0056] D1: Under the operation mode, the absolute carbon dioxide emission reduction potential of the product is:

[0057]

[0058] Where, refers to the total carbon dioxide emissions in a specific scenario of the chemical industry within the y time period, is the total carbon dioxide emissions in scenario l of the chemical industry within the y time period;

[0059] D2: The carbon dioxide emission intensity of the chemical industry is:

[0060]

[0061] Where V is the industrial added value of the chemical industry;

[0062] D3: Thus, it can be obtained that within the y time period, the relative carbon dioxide emission potential of the chemical industry is:

[0063]

[0064] Where, is the carbon dioxide emission intensity of the chemical industry during the reference time period. is the carbon dioxide emission intensity of the chemical industry under scenario l during the y time period.

[0065] Furthermore, the analysis of the contribution of influencing factors of the current product in step S5 is as follows:

[0066]

[0067] Among them, C s is the cumulative carbon dioxide emission reduction during this time period, that is, the contribution degree ce j represents the cumulative emission reduction intensity during the j time period, ce 0 is the carbon emission intensity during the reference time period, E j is the total energy consumption during the j time period.

[0068] The present invention also provides an intelligent carbon footprint assessment and management system based on the cloud, including a front-end display unit and a back-end processing unit. The front-end display unit is used to present a front-end interface to the user to realize the user interface interaction of Internet products; the back-end processing unit uses database technology, connects to the cloud server at the same time to complete the liberation of the user's own resource consumption management, designs and develops cross-platform external API interface experience and capabilities, and at the same time calls external APIs for independent design and realizes system modularization by packing functions.

[0069] The front-end display unit includes a smart visualization module designed based on DataV data visualization. The smart visualization module is used for visual presentation and analysis according to different dimensions;

[0070] The back-end processing unit includes a data and application support module based on cloud computing and big data technology and a basic resource support module based on cloud database and network security;

[0071] The data and application support module is used for carbon footprint calculation, uncertainty analysis, and generation of product carbon footprint reports;

[0072] The basic resource support module is used to provide a platform foundation and data resources for functions such as carbon footprint calculation, uncertainty analysis, and generation of product carbon footprint reports, and at the same time ensure the network security of the platform. The cloud server provides computer resources such as databases, and remote calls are made to realize computer control and configuration, which is the basis of cloud technology. In terms of resources, the storage space of the cloud server, various functions of the database and its management provide a basis for the storage and calculation of the collected data. Updating the database and its connections and data transmission, as well as network security management, ensure the security of the platform.

[0073] The integration of carbon assessment processes, including the connection and transmission of data, as well as the analysis, evaluation, and presentation of reports, is completed by a cloud-based intelligent carbon footprint assessment management system.

[0074] In the present invention, the intelligent visualization module is designed based on DataV data visualization, supporting the display of multiple chart components and data types, and supporting the access of multiple data sources; it can access data sources including Alibaba Cloud analytical databases, relational databases, local CSV uploads, and online APIs, and supports dynamic requests; it supports the display on screens with multiple resolutions and can be optimized for special screens; it supports graphical building tools, allowing non-professional programmers to operate.

[0075] The data and application support module is a high-performance real-time in-memory database supported by big data computing: it is built based on technologies such as distributed storage, distributed computing, interactive query, full-text retrieval, data encryption, and system disaster recovery, and has functions such as big data collection, cleaning, transformation, storage, analysis, and display, and provides a standardized access interface externally to achieve the collection, review, and unified management and distribution of carbon emission factor data. Relying on virtual services, all data storage will be uniformly quantified and uploaded to the cloud to achieve the standardized storage and management of carbon emission factors; the carbon footprint calculation relies on the data and application support module to achieve the access of multi-source data, which is processed by the middle platform to achieve data quantification, and then the data is extracted by the carbon footprint calculator, and the product carbon footprint is calculated relying on the carbon footprint calculation formula.

[0076] The present invention will use Monte Carlo simulation to calculate uncertainty, simulate 100,000 times, with a = 0.05 and a 95% confidence interval. The Monte Carlo Method is characterized by the ability to obtain approximate results through random sampling. As the number of samples increases, the probability that the obtained result is the correct result gradually increases. Due to the multi-dimensionality and complexity of carbon footprint influencing factors, it is difficult and inaccurate to use other methods for carbon footprint uncertainty analysis, while the Monte Carlo method for carbon footprint calculation is relatively simple. Moreover, it has the following advantages that other methods do not have: directly tracking the product carbon footprint, with a clear physical idea and easy to understand; using the method of random sampling to more truly simulate the trajectory of the product carbon footprint, reflecting the law of statistical fluctuations; not being restricted by the complexity of the system such as multi-dimensionality and multiple factors, and being a good method for solving the carbon footprint of complex systems.

[0077] The present invention uses the MC method to write a program to solve the carbon footprint uncertainty analysis and obtain the intermediate results that users want.

[0078] In terms of result analysis, first, a quantitative model of the database and production and consumption activities is established to calculate various environmental quantities and the carbon emissions throughout the whole process of a product. Secondly, after obtaining the total amount, the system can identify the product with the highest energy consumption in a certain link as the key point for improvement. Furthermore, for these key improvement points, different alternative solutions can be carried out. Through the comparative analysis of multiple solutions, the system finally presents the optimal solution for the user, providing a methodological basis for continuous improvement. In fact, the improvement solutions mainly include energy conservation and energy substitution, as well as material conservation and raw material substitution, providing accurate data after emission reduction. LCA provides optimization solutions for carbon reduction in all industries by measuring the total emissions of energy consumption and the upstream production process of raw materials.

[0079] The model of the present invention can also predict future changes. In the power grid application scenario, with the change of the power grid itself, the carbon emissions per kilowatt-hour are decreasing, and the system is simultaneously predicting the future change trend of 1.7 tons of high-density polyethylene production. Therefore, the carbon footprint of a product is not constant, and the acquisition of data depends on the data collection of the technical route.

[0080] The present invention will combine China EDP and ISO international standards to generate a product carbon footprint report according to the types of product carbon footprints. In the report, the product will be analyzed, evaluated, and compared in terms of comprehensive carbon emissions, uncertainty analysis, and the entire process of the product life cycle. Based on the current carbon footprint of the product, check aspects such as the collection of raw materials, R & D design, production process, product flow, and product recycling of the product, analyze the advantages and disadvantages of the current product life cycle, generate multiple alternative solutions, and provide one-stop services for enterprise carbon footprint accounting.

[0081] The present invention provides intelligent visual display of the entire life cycle of the product carbon footprint. The final report will be presented to the enterprise in a dynamic visual form, helping the enterprise to more easily understand the advantages and disadvantages of the product carbon footprint and better adapt to the global low-carbon advocacy.

[0082] Advantageous effects: Compared with the prior art, the present invention combines technical means such as cloud computing, mobile Internet, and RFID to realize the evaluation, analysis, and prediction of the carbon footprint of carbon emission units, thereby improving energy efficiency, reducing energy consumption, and lowering operating costs, and can promote the healthy and sustainable development of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 is the architecture diagram of the system of the present invention;

[0084] Figure 2 is the intelligent carbon footprint assessment flow chart of the system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0085] The present invention will be further clarified below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, those skilled in the art's various equivalent modifications of the present invention all fall within the scope defined by the appended claims of this application.

[0086] The present invention provides an intelligent carbon footprint assessment and management system based on the cloud. As Figure 1 shown, the system includes a front-end display unit and a back-end processing unit. The front-end display unit is used to present a front-end interface to the user and realize the user interface interaction of Internet products; the back-end processing unit uses database technology, connects to the cloud server at the same time to complete the liberation of the user's own resource consumption management, designs and develops cross-platform external API interface experience and capabilities, and at the same time calls external APIs for independent design, and packages functions to realize system modularization.

[0087] The front-end display unit includes a smart visualization module designed based on DataV data visualization. The smart visualization module is used for visual presentation and analysis according to different dimensions;

[0088] The back-end processing unit includes a data and application support module based on cloud computing and big data technology and a basic resource support module based on cloud database and network security;

[0089] The data and application support module is used for carbon footprint calculation, uncertainty analysis, and generation of product carbon footprint reports;

[0090] The basic resource support module is used to provide a platform foundation and data resources for functions such as carbon footprint calculation, uncertainty analysis, and generation of product carbon footprint reports, and at the same time ensure the network security of the platform. The cloud server provides computer resources such as databases, and remotely calls to achieve computer control and configuration, as the basis of cloud technology. In terms of resources, the storage space of the cloud server, all aspects of the functions of the database and its management provide a basis for the storage and calculation of the collected data. Updating the database and its connections and data transmission, as well as network security management, ensure the security of the platform.

[0091] Based on the above system, in this embodiment, the system is applied as an example to provide an intelligent carbon footprint assessment method based on the cloud, as Figure 2 shown, which includes the following steps:

[0092] S1: Collect and import carbon emission factor types, carbon emissions, and carbon emission factor data sources through the system's source data collection devices for carbon emission sources and carbon emission reduction sources;

[0093] S2: Check and quantify carbon emission factors through the carbon emission factor database;

[0094] S3: Calculate the product carbon footprint according to the carbon footprint calculation formula;

[0095] S4: Conduct uncertainty analysis on the product carbon footprint calculation result based on the carbon footprint calculation result in step S3 and the carbon emission factor data source in step S1;

[0096] S5: Conduct potential analysis and contribution analysis on the current product plan according to the results obtained in steps S1 - S4;

[0097] S6: Generate alternative solutions according to the results obtained in steps S1 - S5;

[0098] S7: Evaluate the alternative solutions;

[0099] S8: Generate a product carbon footprint report according to the results obtained in steps S1 - S6;

[0100] S9: Present the product carbon footprint report in an intelligent and visual way.

[0101] The above steps S2 - S8 are jointly implemented by the data and application support module and the basic resource support module. The data and application support module is used for carbon footprint calculation, uncertainty analysis, and generation of the product carbon footprint report. The basic resource support module is used to provide the platform foundation and data resources for the data and application support module.

[0102] Furthermore, the specific carbon footprint calculation formula in step S3 is as follows:

[0103] For power grid enterprises:

[0104]

[0105] where REC 容量，i refers to the sulfur hexafluoride capacity of retired equipment i, and REC 回收，i refers to the actual sulfur hexafluoride recovery amount of retired equipment i. REP 容量，j refers to the sulfur hexafluoride capacity of repaired equipment j, and REP 回收，j refers to the actual sulfur hexafluoride recovery amount of repaired equipment j. GWP SF6 refers to the greenhouse gas potential of sulfur hexafluoride. EL 上网 refers to the electricity generated by the power plant and fed into the grid, and EL 输出 refers to the electricity input from other provinces, and EL 售电 refers to the electricity output to other provinces. EF 电网 refers to the annual average power supply emission factor of the regional power grid;

[0106] For power generation enterprises:

[0107]

[0108] Among them, AD 电力 refers to the net electricity purchased by the enterprise, EF 电力 refers to the annual average power supply emission factor of the regional power grid, FC i refers to the consumption of fossil fuels, EF i refers to the combustion emission factor, B k refers to the consumption of desulfurizer, I k refers to the carbonate content in the desulfurizer, EF k refers to the carbonate emission factor, and TR refers to the conversion rate;

[0109] For electronic equipment manufacturing enterprises:

[0110]

[0111] Among them, AD i refers to the net consumption of fossil fuels, EF i refers to the emission factor, h refers to the gas residue ratio of the raw material gas container, IB i refers to the initial inventory of raw material gas i, P i refers to the purchased quantity of raw material gas i, IE i refers to the initial inventory of raw material gas i, S i The external sales or output quantity of raw material gas i, U i refers to the utilization rate of raw material gas i, a i refers to the collection efficiency of raw material gas i, di refers to the removal efficiency of raw material gas i, GWP i refers to the global warming potential of raw material gas i, B ij refers to the conversion factor of by-product j generated by raw material gas i, a i refers to the collection efficiency of by-product j, d j refers to the removal efficiency of by-product j, GWP i refers to the global warming trend of by-product j, AD electricity refers to the net electricity purchased by the enterprise, EF 电力 refers to the annual average power supply emission factor of the regional power grid, AD 热力 refers to the net purchased heat quantity, EF 热力 refers to the heat supply emission factor;

[0112] For the iron and steel industry:

[0113]

[0114] Among them, FC i refers to the net consumption of fossil fuels, EF i refers to the fuel emission factor, P 溶剂 refers to the net consumption of solvents, EF 溶剂 refers to the emission factor, P 电极Refers to the amount of electrodes consumed in electric furnace steelmaking and refining, etc., EF 电极 Refers to the fuel emission factor, M 原料 Refers to the purchased quantity of carbon-containing raw materials, EF 原料 Refers to the emission factor, AD 固碳 Refers to the output of carbon sequestration products, EF 固碳 Refers to the emission factor, AD 电力 Refers to the net purchased electricity quantity, EF 电力 Refers to the annual average power supply emission factor of the regional power grid, AD 热力 Refers to the net purchased heat quantity, EF 热力 Refers to the heat supply emission factor;

[0115] For the chemical industry:

[0116]

[0117] Among them, AD 电力 Refers to the net purchased electricity quantity, EF 电力 Refers to the annual average power supply emission factor of the regional power grid, AD 热力 Refers to the net purchased heat quantity, EF 热力 Refers to the heat supply emission factor, AD refers to the consumption of fossil fuels, EF refers to the emission factor, AD r Refers to the input quantity of raw materials, CC refers to the carbon content, AD p Refers to the output of carbon-containing products, AD w Refers to the output of carbon-containing waste, AD i Refers to the consumption of carbonates for raw materials, fluxes and desulfurizers, EF i Refers to the N of production technology type j 2 O generation factor, AD j Refers to the adipic acid output of production process j, a k Refers to the N of different tail gas treatment types k in adipic acid production 2 O removal efficiency, Q refers to the recovered and externally supplied CO 2 Gas volume, PUR CO2 Refers to CO 2 External supply gas purity.

[0118] Furthermore, in step S4, the Monte Carlo method is used to implement the uncertainty analysis of the carbon footprint. The specific analysis method is as follows:

[0119] A1: Construct or describe the probability process:

[0120] Generate random variable factors with a known probability distribution through the constructed product carbon footprint probability prediction model, and convert the product carbon footprint variable factors without random properties into product carbon footprint variable factors with random properties;

[0121] A2: Implement sampling from a known probability distribution:

[0122] By collecting various variable factors of the product carbon footprint and then randomly generating various variable factors of the product for different scenarios through a computer, the generation and sampling of random variable factors of the product carbon footprint under different scenarios are realized;

[0123] A3: Establish various estimators and generate an uncertainty analysis report:

[0124] Determine a random variable factor as the solution to the required problem, which is called an unbiased estimate of the product carbon footprint; establish various estimators, examine and record the results of the simulation experiment, obtain the required product carbon footprint information therefrom, and generate a product carbon footprint uncertainty analysis report.

[0125] Furthermore, the construction method of the product carbon footprint probability prediction model in step A1 is as follows:

[0126] B1: Conduct a principal component analysis on the carbon emission factors to obtain the variance percentage;

[0127] B2: Use the extracted principal components as new variables to replace the original variables for multiple linear regression analysis to obtain a regression equation. Substitute the loadings of the principal components into their respective independent variables to obtain a regression equation for each explanatory variable;

[0128] B3: Use the regression residuals of the regression equation to draw a histogram, and draw a scatter plot of the standardized residuals and standardized predicted values to test the homoscedasticity; thus, the construction of the product carbon footprint probability prediction model is completed.

[0129] In this embodiment, the generation of the alternative solution for step S6 is as follows:

[0130] Based on the results obtained from steps S1 - S5, according to the existing operation mode of the product, referring to the China EDP and ISO standards, generate an alternative solution for the product operation mode.

[0131] In this embodiment, the evaluation of the alternative solution for step S7 is as follows:

[0132] C1: Rely on the basic resource service module to realize the virtual operation and carbon emission analysis of the alternative solution;

[0133] C2: Analyze and compare the carbon reduction indexes of the alternative solution and the original solution, and give an evaluation of the alternative solution.

[0134] In this embodiment, for the analysis of the carbon emission reduction potential of the existing operation mode of the current product in step S5, for the chemical industry in this embodiment, the specific analysis method is as follows:

[0135] D1: Under the operation mode, the absolute carbon dioxide emission reduction potential of the product is:

[0136]

[0137] Among them, refers to the total carbon dioxide emissions of the chemical industry in a specific scenario within the y time period, is the total carbon dioxide emissions of the chemical industry under Scenario 1 within the y time period;

[0138] D2: The carbon dioxide emission intensity of the chemical industry is:

[0139]

[0140] Among them, V is the industrial added value of the chemical industry;

[0141] D3: From this, it can be obtained that within the y time period, the relative carbon dioxide emission potential of the chemical industry is:

[0142]

[0143] Among them, is the carbon dioxide emission intensity of the chemical industry in the baseline time period, is the carbon dioxide emission intensity of the chemical industry under Scenario l within the y time period.

[0144] In this embodiment, the analysis of the contribution of the influencing factors of the existing product in step S5 is as follows:

[0145]

[0146] Among them, C s is the cumulative carbon dioxide emission reduction amount during this time period, that is, the contribution degree ce j represents the cumulative emission reduction intensity within the j time period, ce 0 is the carbon emission intensity in the baseline time period, E j is the total energy consumption within the j time period.

[0147] In order to verify the effect of the intelligent carbon footprint assessment and management system provided by the present invention, in this embodiment, the intelligent carbon footprint assessment and management system provided by the present invention is compared with the existing carbon footprint accounting system. The advantages of the system of the present invention compared with the existing carbon footprint accounting system and method are:

[0148] 1. The present invention has established a complete set of evaluation systems. Compared with the existing systems, the evaluation system is more complete, the evaluation method is more advanced, and the evaluation efficiency is higher.

[0149] 2. Based on China EDP and ISO international standards, the present invention establishes a unified carbon footprint accounting framework system, increasing the comparability between the results of different calculation methods and solving the controversial issues in dealing with the carbon footprints of capital goods and land use changes.

[0150] 3. The present invention innovatively adds the analysis of the temporal and spatial distribution trends and driving forces of product carbon footprints to the carbon footprint evaluation method, solving the problem of incomplete carbon footprint evaluation in the existing evaluation system.

[0151] 4. The present invention innovatively adds the analysis of the uncertainty of the results of estimating and reducing carbon footprints to the carbon footprint evaluation method, solving the problems of inaccurate evaluation results and weak guidance in the existing evaluation system.

[0152] 5. The present invention innovatively establishes a unified presentation standard for visual reports in the visual presentation of carbon footprint evaluation results, solving the problem of heavy workload for managers in the existing evaluation system.

Claims

1. A cloud-based intelligent carbon footprint assessment method, characterized in that, it includes the following steps: S1: Collect and import carbon emission factor types, carbon emissions, and carbon emission factor data sources; S2: Check and quantify carbon emission factors through a carbon emission factor database; S3: Calculate the product carbon footprint according to the carbon footprint calculation formula; S4: Conduct uncertainty analysis on the product carbon footprint calculation result based on the carbon footprint calculation result in step S3 and the carbon emission factor data source in step S1; S5: Conduct potential analysis and contribution analysis on the current product plan according to the results obtained in steps S1 - S4; S6: Generate alternative solutions according to the results obtained in steps S1 - S5; S7: Evaluate the alternative solutions; S8: Generate a product carbon footprint report according to the results obtained in steps S1 - S6; S9: Present the product carbon footprint report in an intelligent visual manner; In step S4, the Monte Carlo method is used to achieve the uncertainty analysis of the carbon footprint. The specific analysis method is as follows: A1: Construct or describe the probability process: Generate random variable factors with known probability distributions through the constructed product carbon footprint probability prediction model, and convert the product carbon footprint variable factors without random properties into product carbon footprint variable factors with random properties; A2: Realize sampling from the known probability distribution: Generate and sample the random variable factors of the product carbon footprint under different scenarios by collecting various variable factors of the product carbon footprint and then randomly generating various variable factors of the product for different scenarios by the computer; A3: Establish various estimators and generate an uncertainty analysis report: Determine a random variable factor as the solution to the required problem, which is called the unbiased estimate of the product carbon footprint; establish various estimators, examine and record the results of the simulation experiment, obtain the required product carbon footprint information from it, and generate a product carbon footprint uncertainty analysis report; The construction method of the product carbon footprint probability prediction model in step A1 is as follows: B1: Conduct principal component analysis on carbon emission factors to obtain the variance percentage; B2: Use the extracted principal components as new variables to replace the original variables for multiple linear regression analysis to obtain a regression equation. Substitute the loadings of the principal components into their respective independent variables to obtain the regression equation for each explanatory variable; B3: Draw a histogram using the regression residuals of the regression equation, and draw a scatter plot of the standardized residuals and standardized predicted values to test the homoscedasticity; So far, the product carbon footprint probability prediction model is constructed.

2. The cloud-based intelligent carbon footprint assessment method according to claim 1, characterized in that, the carbon footprint calculation formula in step S3 is specifically as follows: For power grid enterprises: Among them, REC 容量，i refers to the sulfur hexafluoride capacity of the decommissioned equipment i, REC 回收，i refers to the actual sulfur hexafluoride recovery amount of the decommissioned equipment i, REP 容量，j refers to the sulfur hexafluoride capacity of the repaired equipment j, REP 回收，j refers to the actual sulfur hexafluoride recovery amount of the repaired equipment j, GEP SF6 refers to the greenhouse gas potential of sulfur hexafluoride, EL 上网 refers to the electricity generated by the power plant and fed into the grid, EL 输出 refers to the electricity input from other provinces, EL 售电 refers to the electricity output to other provinces, EF 电网 refers to the annual average power supply emission factor of the regional power grid; For power generation enterprises: Among them, AD 电力 refers to the net electricity purchased by the enterprise, EF 电力 refers to the annual average power supply emission factor of the regional power grid, FC i refers to the consumption of fossil fuels, EF i refers to the combustion emission factor, B k refers to the consumption of desulfurizer, I k refers to the carbonate content in the desulfurizer, EF k refers to the carbonate emission factor, and TR refers to the conversion rate; For electronic equipment manufacturing enterprises: Among them, AD i refers to the net consumption of fossil fuels, EF i refers to the emission factor, h refers to the gas residue ratio of the raw material gas container, IB i refers to the initial inventory of raw material gas i, P i refers to the purchase quantity of raw material gas i, IE i refers to the initial inventory of raw material gas i, S i The external sales or output quantity of raw material gas i, U i refers to the utilization rate of raw material gas i, a i refers to the collection efficiency of raw material gas i, d i refers to the removal efficiency of raw material gas i, GWP i refers to the global warming potential of raw material gas i, B ij refers to the conversion factor of by-product j generated by raw material gas i, a i refers to the collection efficiency of by-product j, d j refers to the removal efficiency of by-product j, GWP i refers to the global warming trend of by-product j, AD 电力 refers to the net purchased electricity quantity of the enterprise, EF 电力 refers to the annual average power supply emission factor of the regional power grid, AD 热力 refers to the net purchased heat quantity, EF 热力 refers to the heat supply emission factor; For the steel industry: Among them, FC i refers to the net consumption of fossil fuels, EF i refers to the fuel emission factor, P 溶剂 refers to the net consumption of solvents, EF 溶剂 refers to the emission factor, P 电极 refers to the amount of electrodes consumed in electric arc furnace steelmaking and refining, etc., EF 电极 refers to the fuel emission factor, M 原料 refers to the purchased quantity of carbon-containing raw materials, EF 原料 refers to the emission factor, AD 固碳 refers to the output of carbon sequestration products, EF 固碳 refers to the emission factor, AD 电力 refers to the net purchased electricity quantity, EF 电力 refers to the annual average power supply emission factor of the regional power grid, AD 热力 refers to the net purchased heat quantity, EF 热力 refers to the heat supply emission factor; For the chemical industry: Among them, AD 电力 refers to the net purchased electricity quantity, EF 电力 refers to the annual average power supply emission factor of the regional power grid, AD 热力 refers to the net purchased heat quantity, EF 热力 refers to the heat supply emission factor, AD refers to the fossil fuel consumption, EF refers to the emission factor, AD r refers to the raw material input quantity, CC refers to the carbon content, AD p refers to the carbon-containing product output, AD w refers to the carbon-containing waste output quantity, AD i refers to the consumption of carbonates for raw materials, fluxes and desulfurizers, EF i refers to the NO generation factor of production technology type j, N 2 O generation factor, AD j refers to the adipic acid output of production process j, a k refers to the NO removal efficiency of different tail gas treatment types k in adipic acid production, N 2 O removal efficiency, Q refers to the volume of recycled and externally supplied CO 2 gas volume, PUR CO2 refers to CO 2 external supply gas purity.

3. The cloud-based intelligent carbon footprint assessment method according to claim 1, characterized in that, the generation of alternative solutions in step S6 is specifically as follows: Based on the results obtained in steps S1 - S5, according to the existing operation mode of the product, referring to the China EDP and ISO standards, generate alternative solutions for the product operation mode.

4. A cloud-based intelligent carbon footprint assessment method according to claim 1, characterized in that the evaluation of alternative solutions in step S7 is carried out as follows: C1: Relying on the basic resource service module, realize the virtual operation and carbon emission analysis of alternative solutions; C2: Analyze and compare the carbon reduction indexes of alternative solutions and the original solutions, and give evaluations to alternative solutions.

5. A cloud-based intelligent carbon footprint assessment method according to claim 1, characterized in that the analysis of the carbon emission reduction potential of the existing operation mode of the existing product in step S5, for the chemical industry, is carried out as follows: D1: Under the operation mode, the absolute carbon dioxide emission reduction potential of the product is: Among them, refers to the total carbon dioxide emissions of a specific scenario in the chemical industry during the y time period, is the total carbon dioxide emissions under Scenario l in the chemical industry during the y time period; D2: The carbon dioxide emission intensity of the chemical industry is: where V is the industrial added value of the chemical industry; D3: Thus, the relative carbon dioxide emission potential of the chemical industry within the y time period can be obtained as: Among them, is the carbon dioxide emission intensity of the chemical industry during the baseline time period, is the carbon dioxide emission intensity of the chemical industry under Scenario 1 during the y time period.

6. A cloud-based intelligent carbon footprint assessment method according to claim 1, characterized in that the analysis of the contribution of influencing factors of the existing product in step S5 is carried out as follows: Among them, C s is the cumulative carbon dioxide emission reduction during this time period, that is, the contribution degree, ce j represents the cumulative emission reduction intensity in the j-th time period, ce 0 is the carbon emission intensity in the baseline time period, E j is the total energy consumption in the j-th time period.

7. A cloud-based intelligent carbon footprint assessment management system according to the method of claim 1, characterized in that it includes a front-end display unit and a back-end processing unit. The front-end display unit is used to present a front-end interface to the user to realize the user interface interaction of Internet products; the back-end processing unit uses database technology, connects to the cloud server at the same time to complete the liberation of the user's own resource consumption management, designs and develops cross-platform external API interface experience and capabilities, and at the same time calls external APIs for independent design and realizes system modularization by packaging functions.

8. A cloud-based intelligent carbon footprint assessment management system according to claim 7, characterized in that the front-end display unit includes a smart visualization module designed based on DataV data visualization. The smart visualization module is used for visual presentation and analysis according to different dimensions; the back-end processing unit includes a data and application support module based on cloud computing and big data technology and a basic resource support module based on cloud database and network security; the data and application support module is used for carbon footprint calculation, uncertainty analysis, and generation of product carbon footprint reports; the basic resource support module is used to provide a platform foundation and data resources for the functions of carbon footprint calculation, uncertainty analysis, and generation of product carbon footprint reports, and at the same time ensure the network security of the platform.

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