A cloud computing-based carbon management method, terminal, and system for high-energy-consuming industries

Abstract

This invention discloses a cloud-based carbon management method, terminal, and system for high-energy-consuming industries. The method involves acquiring data tags from different dimensions of enterprises in a region to be analyzed; calculating the comprehensive energy consumption index of each enterprise; selecting enterprises whose comprehensive energy consumption index exceeds a set high-energy consumption threshold as high-energy-consuming and high-emission enterprises in the region; identifying the dimension of the data tag whose value is higher than the average value of the region as the improvement direction for these enterprises; and pushing the improvement direction and matching optimization scheme to the enterprise. By comprehensively considering the enterprise's energy consumption and carbon emissions from multiple perspectives, rather than solely focusing on the magnitude of energy consumption and carbon emissions, this method improves the objectivity of identifying high-energy-consuming enterprises, provides corresponding optimization schemes, and ultimately enhances the overall effectiveness of carbon management for enterprises.

Description

Technical Field

[0001] This invention relates to the field of cloud computing management technology, and in particular to a cloud computing-based carbon management method, terminal, and system for high-energy-consuming industries. Background Technology

[0002] With the rapid development of new energy power generation, when there is a large difference between the output characteristics and the load characteristics, large-scale new energy power generation has already encountered power curtailment problems in actual operation, which hinders the achievement of dual carbon targets. The current market environment tends to encourage and support the development of load-side energy storage, and explore the combination of energy storage and new energy power generation to realize the local consumption and utilization of new energy.

[0003] Meanwhile, as new energy technologies mature and are deployed, how to monitor and manage existing controlled emission enterprises on the grid, especially how to optimize their management, so as to further identify high-energy-consuming enterprises based on existing technologies and production scales and complete corresponding structural energy conservation and emission reduction, is a problem that must be faced in the current global wave of carbon neutrality.

[0004] However, in the existing technology, the calculation results for identifying high energy-consuming enterprises are not entirely objective, and there are no practical and effective technical means to reduce the energy consumption of enterprises. The existing technology does not provide a solution on how to shorten the carbon management transformation cycle as much as possible and reduce the carbon management transformation cost of enterprises. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a carbon management method, terminal and system for high-energy-consuming industries based on cloud computing, which can objectively identify high-energy-consuming enterprises and provide transformation and optimization solutions to improve the effectiveness of carbon management for enterprises.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A cloud computing-based carbon management method for high-energy-consuming industries includes the following steps:

[0008] S1. Obtain data labels for different dimensions of each enterprise in the region to be analyzed;

[0009] S2. Calculate the comprehensive energy consumption index of each enterprise to be analyzed based on the data tags of each enterprise to be analyzed;

[0010] S3. Select enterprises whose comprehensive energy consumption index exceeds the set high energy consumption threshold as high energy consumption and high emission enterprises in the region to be analyzed.

[0011] S4. For high-energy-consuming and high-emission enterprises, the dimension in which the value of their data label is higher than the average value of the region to be analyzed is taken as the direction for improvement of high-energy-consuming and high-emission enterprises.

[0012] S5. Match the data tags of high-energy-consuming and high-emission enterprises with the data tags of the stored optimization solutions, and push the improvement directions and matching optimization solutions to the enterprise.

[0013] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0014] A cloud-based carbon management terminal for high-energy-consuming industries includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a cloud-based carbon management method for high-energy-consuming industries as described above.

[0015] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0016] A cloud-based carbon management system for high-energy-consuming industries includes a cloud and multiple enterprise terminals. The cloud is communicatively connected to each enterprise terminal. The cloud includes a first memory, a first processor, and a first computer program stored in the first memory and capable of running on the processor.

[0017] The enterprise terminal includes a second memory, a second processor, and a second computer program stored in the second memory and capable of running on the processor.

[0018] The characteristic is that when the first processor executes the first computer program, it implements steps S1-S5 of the method described above;

[0019] When the second processor executes the second computer program, it implements the following steps:

[0020] A11. Obtain energy consumption and carbon emission data for each enterprise in the region to be analyzed;

[0021] A12. For the collected data from each company to be analyzed, set corresponding data tags according to different dimensions of the data. Then transmit the data tags of each company to be analyzed to the cloud.

[0022] The beneficial effects of this invention are as follows: A cloud-based carbon management method, terminal, and system for high-energy-consuming industries sets multiple data tags of different dimensions for enterprises, which facilitates a comprehensive consideration of the enterprise's energy consumption and carbon emissions from multiple perspectives, rather than simply considering the amount of energy consumption and carbon emissions. Multiple data tags of different dimensions help to comprehensively analyze the enterprise's actual energy consumption and carbon emissions, improve the objectivity of judging high-energy-consuming enterprises, and push optimization schemes with high data tag matching degree to enterprises whose annual energy consumption is not up to standard, so as to serve as a template or improvement basis for the next year's energy conservation and emission reduction optimization scheme, and provide corresponding optimization schemes, thereby improving the overall effectiveness of carbon management for enterprises. Attached Figure Description

[0023] Figure 1 This is a schematic flowchart of a cloud computing-based carbon management method for high-energy-consuming industries, according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the structure of a cloud computing-based carbon management terminal for high-energy-consuming industries, according to an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the topology of a cloud computing-based carbon management system for high-energy-consuming industries, according to an embodiment of the present invention.

[0026] Label Explanation:

[0027] 1. A cloud computing-based carbon management terminal for high-energy-consuming industries; 2. Processor; 3. Memory. Detailed Implementation

[0028] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0029] Please refer to Figure 1 A cloud computing-based carbon management method for high-energy-consuming industries includes the following steps:

[0030] S1. Obtain data labels for different dimensions of each enterprise in the region to be analyzed;

[0031] S2. Calculate the comprehensive energy consumption index of each enterprise to be analyzed based on the data tags of each enterprise to be analyzed;

[0032] S3. Select enterprises whose comprehensive energy consumption index exceeds the set high energy consumption threshold as high energy consumption and high emission enterprises in the region to be analyzed.

[0033] S4. For high-energy-consuming and high-emission enterprises, the dimension in which the value of their data label is higher than the average value of the region to be analyzed is taken as the direction for improvement of high-energy-consuming and high-emission enterprises.

[0034] S5. Match the data tags of high-energy-consuming and high-emission enterprises with the data tags of the stored optimization solutions, and push the improvement directions and matching optimization solutions to the enterprise.

[0035] As described above, the beneficial effects of this invention are as follows: A cloud-based carbon management method, terminal, and system for high-energy-consuming industries sets multiple data tags of different dimensions for enterprises, which facilitates a comprehensive consideration of the enterprise's energy consumption and carbon emissions from multiple perspectives, rather than simply considering the amount of energy consumption and carbon emissions. Multiple data tags of different dimensions help to comprehensively analyze the enterprise's actual energy consumption and carbon emissions, improve the objectivity of judging high-energy-consuming enterprises, and push optimization schemes with high data tag matching to enterprises whose annual energy consumption is not up to standard, so as to serve as a template or improvement basis for the next year's energy conservation and emission reduction optimization scheme, and provide corresponding optimization schemes, thereby improving the overall effectiveness of carbon management for enterprises.

[0036] Furthermore, the data labels of different dimensions include one or more of the following:

[0037] Energy efficiency label:

[0038]

[0039] In the formula, a i1 To analyze the current carbon emission data of company i for this year, a i2 The data to be analyzed is the current electricity consumption data of company i for this year;

[0040] Energy efficiency label:

[0041]

[0042] In the formula, b i1 This refers to the electricity consumption of enterprise i during the current off-peak period of the power grid this year, b. i2 This refers to the electricity consumption of the enterprise i to be analyzed during the current peak period of the power grid this year;

[0043] District carbon emission impact indicators:

[0044]

[0045] In the formula, c i This refers to the carbon emissions of the enterprise i to be analyzed in its region in the current year, and c is the total carbon emissions of the region to be analyzed in the current carbon management cycle.

[0046] Energy consumption industry label d i :

[0047] For the industry to which company i belongs, set an energy consumption industry label d based on whether company i is a high-energy-consuming or low-energy-consuming enterprise. i High-energy-consuming industries, energy consumption industry label d i The energy consumption rating is 3, and the energy consumption industry label for low-energy consumption industries is d. i The energy consumption industry label for other types of enterprises is d (2). i =1;

[0048] Carbon neutrality and management history label e i :

[0049] For the industry to which company i belongs, if company i has carbon management data from the previous year, then the carbon neutrality and management history label e is used. i The carbon emission data for the previous year is divided by the company's carbon allowance data for the previous year; otherwise, the carbon neutrality and historical labeling are used. i The value is 1.

[0050] As described above, setting multiple data labels of different dimensions for a company's energy consumption, carbon emissions, and other data facilitates a comprehensive assessment of the company's energy consumption and carbon emissions from multiple perspectives, rather than simply evaluating the company based on the amount of energy consumption and carbon emissions. Multiple data labels of different dimensions help to comprehensively analyze the company's true energy consumption and carbon emissions.

[0051] Further, step S2 specifically includes:

[0052] The data tags of each enterprise to be analyzed are weighted to obtain the comprehensive energy consumption index of each enterprise.

[0053] As can be seen from the above description, the weight values ​​effectively reflect the effectiveness of each data label.

[0054] Furthermore, the weight values ​​for data labels in different dimensions are set according to the importance of the data label dimensions.

[0055] As can be seen from the above description, taking into full account the differentiated management needs of economic development levels, energy consumption and carbon emission management levels in various regions, enterprises with high current energy consumption and carbon emission levels in the region to be analyzed are identified according to these differentiated management needs, so as to achieve more targeted energy conservation and emission reduction.

[0056] Furthermore, the matching of data tags of high-energy-consuming and high-emission enterprises with data tags of stored optimization schemes specifically involves matching data tags of improvement directions with data tags of optimization schemes, or performing weighted combination matching of all data tags with data tags of optimization schemes.

[0057] As can be seen from the above description, the matching of high-energy-consuming enterprises with optimization solutions has been achieved.

[0058] Furthermore, the matching of data labels for improvement directions with data labels for optimization schemes is specifically performed according to the following formula:

[0059]

[0060] In the formula, is the value of the data label for the improvement direction of high energy-consuming and high-emission enterprises, and is the value of the data label for the improvement direction of the corresponding high energy-consuming and high-emission enterprises in the optimization scheme. If T is greater than the set matching threshold, the optimization scheme passes the matching.

[0061] As can be seen from the above description, a specific scheme for single-label matching has been given.

[0062] Furthermore, the weighted combination matching of all data labels with the data labels of the optimization scheme is specifically performed according to the following formula:

[0063]

[0064] In the formula, is the weight value of the i-th data label, Z i Y represents the value of the i-th data label for high-energy-consuming and high-emission enterprises. i Let i be the value of the i-th data label in the optimization scheme.

[0065] As can be seen from the above description, a specific scheme for multi-label combination matching has been given.

[0066] Furthermore, the data labels for each enterprise in the region to be analyzed, based on different dimensions, are obtained according to the following steps:

[0067] A11. Obtain energy consumption and carbon emission data for each enterprise in the region to be analyzed;

[0068] A12. For the data collected from each enterprise to be analyzed, set corresponding data labels according to different dimensions of the data.

[0069] As described above, the acquisition of data tags has been achieved.

[0070] A cloud-based carbon management terminal for high-energy-consuming industries includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method described above.

[0071] A cloud-based carbon management system for high-energy-consuming industries includes a cloud and multiple enterprise terminals. The cloud is communicatively connected to each enterprise terminal. The cloud includes a first memory, a first processor, and a first computer program stored in the first memory and capable of running on the processor.

[0072] The enterprise terminal includes a second memory, a second processor, and a second computer program stored in the second memory and capable of running on the processor.

[0073] The characteristic is that when the first processor executes the first computer program, it implements steps S1-S5 of the method described above;

[0074] When the second processor executes the second computer program, it implements steps A11-A12 in the method described above and transmits the data tags of each enterprise to be analyzed to the cloud.

[0075] This invention is used for carbon management in enterprises.

[0076] Please refer to Figure 1 Embodiment 1 of the present invention is as follows:

[0077] A cloud computing-based carbon management method for energy-intensive industries includes the following steps:

[0078] S1. Obtain data labels of different dimensions for each enterprise in the region to be analyzed.

[0079] The data labels for each enterprise in the region to be analyzed, based on different dimensions, are obtained according to the following steps:

[0080] A11. Obtain energy consumption data and carbon emission data of each enterprise in the region to be analyzed.

[0081] Specifically, energy consumption and carbon emission data are collected for each enterprise i() in region A to be analyzed.

[0082] A12. For the data collected from each enterprise to be analyzed, set corresponding data labels according to different dimensions of the data.

[0083] Specifically, data tags include energy efficiency tags:

[0084]

[0085] In the formula, a i1 To analyze the current carbon emission data of company i for this year, a i2 The data to be analyzed is the current electricity consumption data of company i for this year;

[0086] Energy efficiency label:

[0087]

[0088] Among them, b i1 This refers to the electricity consumption of enterprise i during the current off-peak period of the power grid this year, b. i2This refers to the electricity consumption of the enterprise i to be analyzed during the current peak period of the power grid this year;

[0089] District carbon emission impact indicators:

[0090]

[0091] Among them, c i This refers to the carbon emissions of the enterprise i to be analyzed in its region in the current year, and c is the total carbon emissions of the region to be analyzed in the current carbon management cycle.

[0092] Energy consumption industry label d i :

[0093] For the industry to which company i belongs, set an energy consumption industry label d based on whether company i is a high-energy-consuming or low-energy-consuming enterprise. i For example, companies in industries such as metallurgy and chemicals are considered high-energy-consuming industries, as indicated by their energy consumption industry label d. i The energy consumption rating is 3. Companies in the new energy (such as photovoltaic power generation) industry belong to the low-energy consumption industry, and their energy consumption industry label is d. i The energy consumption industry label for other types of enterprises is d (2). i It can be set to 1;

[0094] Carbon neutrality and management history label e i :

[0095] For the industry to which company i belongs, a carbon neutrality and management history label e is set based on its carbon emissions in the previous year. i The carbon neutrality and management history label e i The initial value is 1. If the company i to be analyzed has carbon management data from the previous year, then the carbon neutrality and management history label e is added. i The value is the company's actual carbon emissions data for the previous year divided by its carbon allowance data for the previous year; if company i does not have carbon management data for the previous year, the initial value is set to 1.

[0096] It should be noted that in this embodiment, the data labels of each dimension are calculated on an annual basis. In other optional embodiments, the analysis and calculation can also be performed according to other period lengths, such as monthly, quarterly or semi-annual.

[0097] In this embodiment, in order to simplify cloud computing and reduce the amount of cloud computing, steps A11 and A12 are completed by the terminal located in the enterprise and the data tag is uploaded to the cloud. In an optional embodiment, steps A11 and A12 can also be completed directly by the cloud.

[0098] S2. Calculate the comprehensive energy consumption index of each enterprise to be analyzed based on the data tags of each enterprise to be analyzed.

[0099] Specifically, the comprehensive energy consumption index k of each enterprise is calculated according to the following formula. i :

[0100] k i =ω1×α i +ω2×β i +ω3×γ i +ω4×d i +ω5×e i ;

[0101] In the formula, ω1, ω2, ω3, ω4, and ω5 are the energy efficiency labels α of the region A to be analyzed. i Energy consumption and economic label β i Carbon emission impact indicator γ i Energy consumption industry label d i Carbon neutrality and management history label e i The weighting coefficients for the region to be analyzed can be set by the regional monitoring center according to the region's energy consumption management and carbon management needs.

[0102] For example, in provinces and regions with high energy efficiency requirements, the energy efficiency label α can be used. i The weighting coefficient ω1 is set to a higher value, while in regions with similar energy efficiency requirements but tighter carbon quotas, the energy efficiency label α can be set to a higher value. i The weighting coefficient ω1 is set to a low value, while the carbon emission impact index γ is set to a lower value. i The weighting coefficient ω3 and the weighting coefficient ω5 of carbon and management history label ei are set to higher values.

[0103] Alternatively, in provinces and regions with a large number of enterprises subject to emission control, the majority of these enterprises are subject to emission control due to factors such as the layout of their technology and economic industrial structure. In such cases, it is not feasible to make large-scale transformation of enterprises' emission control types in these regions. In this case, ω4 can be set to a lower value, while the weighting coefficient ω1 of the energy efficiency label can be set to a higher value, so as to focus on the economic efficiency of energy conversion and the cleanliness of energy of each enterprise subject to emission control in the region.

[0104] Alternatively, for regions where there are significant differences in electricity consumption during peak and off-peak periods (such as areas with a relatively high concentration of industrial parks), in addition to optimizing and controlling power flow calculations on the generation side, an ideal scenario is to encourage businesses in the region to balance their electricity consumption to achieve a "peak shaving and valley filling" effect. To achieve this goal, the energy consumption economic label β can be used... iThe weighting coefficient ω4 is adjusted to a higher value to achieve differentiated measurement of energy consumption management and carbon emission management of enterprises in different regions. It fully considers the differentiated management needs of economic development level and energy consumption and carbon emission management level in various regions. Based on these differentiated management needs, enterprises with high current energy consumption and carbon emission levels in the region to be analyzed are identified to achieve more targeted energy conservation and emission reduction.

[0105] In this embodiment, the weighting coefficients satisfy the following constraints:

[0106] 1 = ω1 + ω2 + ω3 + ω4 + ω5;

[0107] S3. Select enterprises whose comprehensive energy consumption index exceeds the set high energy consumption threshold as high energy consumption and high emission enterprises in the region to be analyzed.

[0108] Specifically, cloud servers are used to calculate the comprehensive energy consumption index k for each enterprise. i Perform bubble sort and select items whose overall energy consumption index is greater than the threshold K. th These enterprises constitute the high-energy-consuming and high-emission enterprises to be optimized under the differentiated measurement needs of region A. Let G be the total number of high-energy-consuming and high-emission enterprises, and M be a specific high-energy-consuming and high-emission enterprise among them. j Where 1≤j≤G, the threshold K th These are the energy consumption and carbon emission thresholds for the region to be analyzed, which are set by the energy consumption and emission monitoring personnel in the region.

[0109] S4. For high-energy-consuming and high-emission enterprises, the dimension in which the value of their data label is higher than the average value of the region to be analyzed is taken as the direction for improvement of high-energy-consuming and high-emission enterprises.

[0110] In a preferred embodiment, the high-energy-consuming and high-emission enterprise M j carbon emission impact index γ i The value is significantly higher than the carbon emission impact index γ of all enterprises in region A under analysis. i If the average value of the carbon emission is less than the average value of the other indicators, it indicates that the carbon emission ratio of the enterprise is relatively large. The enterprise needs to improve its carbon emission absorption equipment and set up more or more efficient carbon emission secondary treatment equipment or recycling equipment to reduce the enterprise's carbon emission.

[0111] In another preferred embodiment, the energy efficiency label α of the high-energy-consuming and high-emission enterprise Mj i and energy economy label β i All of them are higher than the energy efficiency label α of all enterprises in the region A to be analyzed. i and energy economy label β iThe average value indicates that the company uses high-energy-consuming and high-emission energy conversion equipment during its electricity consumption process. It needs to improve the energy (mainly electrical energy) conversion efficiency, such as by purchasing high-conversion-rate electrical equipment to improve electricity efficiency. The company should also adjust its electricity consumption plan appropriately and increase the use of "off-peak electricity" as much as possible according to actual production needs. This will enable the company to participate in the peak shaving and valley filling of the regional power grid as much as possible, thereby improving the company's electricity economy and reducing the fluctuation of regional power grid electricity consumption.

[0112] It should be noted that, based on the values ​​of other data labels, individual or combined optimizations can be performed on the energy consumption and emissions of enterprises with different situations, and the energy consumption and emission optimization schemes and optimization effects of the high-energy-consuming and high-emission enterprise Mj can be recorded.

[0113] Analysis of production processes requiring energy and carbon emission management can be achieved in various ways. For example, carbon emission monitors can be installed in production workshops to monitor equipment carbon emission levels, or the energy consumption of energy-intensive equipment in the workshop can be statistically analyzed. Then, the corresponding carbon emissions can be calculated according to the 2006 IPCC National Greenhouse Gas Inventory Guidelines, thus obtaining the carbon emissions of the production process. Further improvements can be made by purchasing or replacing energy-saving and low-carbon equipment, upgrading energy utilization technologies, or strengthening carbon emission recovery. Cloud servers can collect and gather successful energy management and carbon emission transformation solutions from enterprises, storing them as low-carbon transformation solution templates. When enterprises need to implement energy management or carbon emission transformation, suggested template solutions can be pushed to them via the network.

[0114] Compared to existing technologies that often struggle to quickly determine directions for improvement in energy consumption management and carbon emission management, this invention uses multi-dimensional data to compare and determine an enterprise's energy consumption and carbon emission data, enabling rapid assistance in identifying areas for improvement in energy conservation and emission reduction.

[0115] S5. Match the data tags of high-energy-consuming and high-emission enterprises with the data tags of the stored optimization solutions, and push the improvement directions and matching optimization solutions to the enterprise.

[0116] At the end of the year, monitor the annual energy consumption and carbon emission levels of high-energy-consuming and high-emission enterprises. If any of these enterprises improves its energy consumption and carbon emission levels through technological upgrades, its comprehensive energy consumption index k will be adjusted accordingly. i Reduced to the threshold K th If the energy consumption and carbon emission levels do not exceed the corresponding quotas given to enterprises by region A, the enterprise will be marked as an energy-qualified enterprise, its energy consumption and carbon emission technical improvement plan will be recorded, uploaded to the cloud server, and marked as an optimized alternative plan (also known as an energy-saving and emission-reduction alternative plan).

[0117] If a company, after making technological improvements in energy consumption and carbon emissions, still fails to reduce them to the threshold K... th If the company's energy consumption and carbon emissions still exceed the corresponding quotas given to the company by Region A, the data will be uploaded to the cloud server and marked as a reference scheme, and the company will be marked as an energy consumption non-compliant company.

[0118] Before the start of the next year, for companies whose energy consumption did not meet the standards in the previous year, the aforementioned optimization alternatives will be proactively pushed from the cloud server for the companies to refer to, and the companies will make their own choices.

[0119] The cloud server can categorize the optimization alternatives, for example, by classifying the various optimization alternatives according to the aforementioned data labels of different dimensions. a For labeling, one or more of the aforementioned different dimensions can be selected as combined data labels U. b By labeling, enterprises that fail to meet energy consumption standards can make selections based on different data labels, thereby obtaining more suitable technical solutions for their enterprises.

[0120] In another alternative embodiment, the cloud server collects data tags from different dimensions of the energy-inefficient enterprises, matches them in the cloud, and pushes suitable optimization alternatives to the enterprises based on the matching results. Taking energy efficiency tags as an example:

[0121]

[0122] The energy efficiency label value represents the energy efficiency label value of a certain energy-unqualified enterprise, and the energy efficiency label value represents the energy efficiency label value of a certain optimized alternative enterprise. When T is greater than a certain threshold, it means that the enterprise that designed the optimized alternative is similar to the energy-unqualified enterprise.

[0123] Of course, you can also select combined data labels to optimize the matching of alternative solutions. The matching method is as follows:

[0124]

[0125] Among them, the energy consumption economic label value represents a certain energy consumption non-compliant enterprise, the energy consumption economic label value represents a certain optimized alternative enterprise, the carbon emission impact index value represents a certain energy consumption non-compliant enterprise, the carbon emission impact index value represents a certain optimized alternative enterprise, the energy consumption industry label value represents a certain energy consumption non-compliant enterprise, and the energy consumption industry label value represents a certain optimized alternative enterprise. , , , and are the weighting coefficients of each ratio, which can be set by the energy consumption non-compliant enterprise itself, or uniformly set by the energy consumption and carbon emission management personnel in region A to be analyzed.

[0126] Please refer to Figure 2Embodiment two of the present invention is as follows:

[0127] A cloud computing-based carbon management terminal 1 for high-energy-consuming industries includes a memory 3, a processor 2, and a computer program stored on the memory 3 and run on the processor 2. When the processor 2 executes the computer program, it implements the method described in Embodiment 1 above.

[0128] Please refer to Figure 3 Embodiment 3 of the present invention is as follows:

[0129] A cloud-based carbon management system for high-energy-consuming industries includes a cloud and multiple enterprise terminals. The cloud includes a first memory, a first processor, and a first computer program stored in the first memory and capable of running on the processor.

[0130] The enterprise terminal includes a second memory, a second processor, and a second computer program stored in the second memory and capable of running on the processor.

[0131] The characteristic is that, when the first processor executes the first computer program, it implements steps S1-S5 in the method of the above embodiment one;

[0132] When the second processor executes the second computer program, it implements steps A11-A12 in the method of the above embodiment one and transmits the data tags of each enterprise to be analyzed to the cloud.

[0133] In summary, the present invention provides a cloud-based carbon management method, terminal, and system for high-energy-consuming industries. This system sets multiple data tags for enterprises across different dimensions, facilitating a comprehensive assessment of the enterprise's energy consumption and carbon emissions from multiple perspectives, rather than solely focusing on energy consumption and carbon emissions. These multiple data tags help to comprehensively analyze the enterprise's actual energy consumption and carbon emissions, improving the objectivity of identifying high-energy-consuming enterprises. Furthermore, optimization schemes with high data tag matching are pushed to enterprises that have not met annual energy consumption standards, serving as templates or improvement bases for the following year's energy conservation and emission reduction optimization schemes. This overall improves the effectiveness of carbon management for enterprises.

[0134] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A carbon management method for high-energy-consuming industries based on cloud computing, characterized in that, Including the following steps: S1. Obtain data labels for different dimensions of each enterprise in the region to be analyzed; S2. Calculate the comprehensive energy consumption index of each enterprise to be analyzed based on the data tags of each enterprise to be analyzed; S3. Select enterprises whose comprehensive energy consumption index exceeds the set high energy consumption threshold as high energy-consuming and high-emission enterprises in the region to be analyzed. S4. For high-energy-consuming and high-emission enterprises, the dimension in which the value of their data label is higher than the average value of the region to be analyzed is taken as the direction for improvement of high-energy-consuming and high-emission enterprises. S5. Match the data tags of high-energy-consuming and high-emission enterprises with the data tags of the stored optimization solutions, and push the improvement directions and matching optimization solutions to the enterprise. The data labels of different dimensions include one or more of the following: Energy efficiency label : ； In the formula, For the current carbon emission data of the company to be analyzed this year, The data to be analyzed is the current electricity consumption data of company i for this year; Energy consumption and economic label : ； In the formula, This refers to the electricity consumption of the enterprise i to be analyzed during the current off-peak period of the power grid this year. This refers to the electricity consumption of the enterprise i to be analyzed during the current peak period of the power grid this year; District carbon emission impact indicators : ； In the formula, c i This refers to the carbon emissions of the company i being analyzed in its region during the current year. c It is the total carbon emissions of the region to be analyzed during the current carbon management cycle; Energy consumption industry tag d i : According to whether the enterprise i to be analyzed belongs to a high energy consumption enterprise or a low energy consumption enterprise, set the energy consumption industry label d of the industry to which the enterprise i to be analyzed belongs i , the energy consumption industry label d of the high energy consumption industry is 3 i , the energy consumption industry label d of the low energy consumption industry is 2 i , and the energy consumption industry label d of other types of enterprises is 1 i ; Carbon neutral management history label e i : For the industry to which the enterprise i to be analyzed belongs, if the enterprise i to be analyzed has carbon management data of the last year, the carbon neutral management history label e i is the actual carbon emission data of the last year divided by the carbon quota data of the enterprise of the last year; otherwise, the carbon neutral management history label e i is 1; The process of matching the data tags of high-energy-consuming and high-emission enterprises with the data tags of stored optimization schemes specifically involves matching the data tags of improvement directions with the data tags of optimization schemes, or performing a weighted combination matching of all data tags with the data tags of optimization schemes.

2. The carbon management method for high-energy-consuming industries based on cloud computing according to claim 1, characterized in that, Step S2 specifically includes: The data tags of each enterprise to be analyzed are weighted to obtain the comprehensive energy consumption index of each enterprise.

3. A carbon management method for high-energy-consuming industries based on cloud computing according to claim 2, characterized in that, The weight values ​​for data labels in different dimensions are set according to the importance of the data label dimension.

4. A carbon management method for high-energy-consuming industries based on cloud computing according to claim 1, characterized in that, The matching of data labels for improvement directions with data labels for optimization schemes is specifically performed according to the following formula: T= ； In the formula, The data label values ​​represent the improvement directions for high-energy-consuming and high-emission enterprises. The data label value for the improvement direction of the optimization scheme for enterprises with high energy consumption and high emissions is used to optimize the scheme. If T is greater than the set matching threshold, the optimization scheme passes the matching.

5. A carbon management method for high-energy-consuming industries based on cloud computing according to claim 1, characterized in that, The weighted combination matching of all data labels with the data labels of the optimization scheme is specifically performed according to the following formula: T= ； In the formula, Z represents the weight value of the i-th data label. i Y represents the value of the i-th data label for high-energy-consuming and high-emission enterprises. i Let i be the value of the i-th data label in the optimization scheme.

6. A carbon management method for high-energy-consuming industries based on cloud computing according to claim 1, characterized in that, The data labels for each enterprise in the region to be analyzed, based on different dimensions, are obtained according to the following steps: A11. Obtain energy consumption and carbon emission data for each enterprise in the region to be analyzed; A12. For the data collected from each enterprise to be analyzed, set corresponding data labels according to different dimensions of the data.

7. A cloud-based carbon management terminal for high-energy-consuming industries, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1-6.

8. A cloud-based carbon management system for high-energy-consuming industries, comprising a cloud and multiple enterprise terminals, wherein the cloud is communicatively connected to each enterprise terminal, the cloud comprising a first memory, a first processor, and a first computer program stored in the first memory and executable on the processor, and each enterprise terminal comprising a second memory, a second processor, and a second computer program stored in the second memory and executable on the processor, characterized in that, When the first processor executes the first computer program, it implements steps S1-S5 of the cloud computing-based carbon management method for high-energy-consuming industries as described in claim 1. When the second processor executes the second computer program, it implements steps A11-A12 of the cloud computing-based carbon management method for high-energy-consuming industries as described in claim 6, and transmits the data tags of each enterprise to be analyzed to the cloud.

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