A method, device and medium for managing carbon emissions based on LLM model

Abstract

The present application discloses a carbon emission management method, equipment and medium based on an LLM model, the method comprising: collecting carbon emission information, analyzing the carbon emission information through an LLM model, and constructing a carbon emission calculation model for calculating the carbon emission corresponding to a carbon emission block according to the carbon emission information; collecting block activity environment data corresponding to the carbon emission block within a preset time interval, and extracting carbon emission parameters matching the carbon emission calculation model from the block activity environment data through the LLM model; calculating the carbon emission corresponding to the carbon emission block according to the carbon emission calculation model and the carbon emission parameters; and evaluating the carbon emission trend in the carbon emission block according to the carbon emissions within different time intervals, so as to issue an early warning to the carbon emission block when the carbon emission trend is abnormal.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission management, and specifically to a carbon emission management method, device and medium based on an LLM model. Background Art

[0002] As the global climate change problem becomes increasingly serious, carbon emission management has become the focus of global attention. Traditional carbon emission information collection and analysis methods often rely on manual operations, which have problems such as low efficiency, low data accuracy, and difficulty in dealing with large-scale data. In the existing technology, the collection and analysis of carbon emission information usually involves multiple links, including data collection, collation, classification and calculation. These links are not only cumbersome and complicated, but also easily affected by human factors, resulting in inaccurate and unreliable data. Summary of the invention

[0003] In order to solve the above problems, this application proposes a carbon emissions management method based on the LLM model, including:

[0004] Collect carbon emission information, analyze the carbon emission information through the LLM model, and construct a carbon emission calculation model for calculating the carbon emission amount corresponding to the carbon emission block according to the carbon emission information;

[0005] According to a preset time interval, the block activity environment data corresponding to the carbon emission block within the time interval is collected, and the carbon emission parameters matching the carbon emission calculation model are extracted from the block activity environment data through the LLM model;

[0006] Calculate the carbon emission amount corresponding to the carbon emission block according to the carbon emission calculation model and the carbon emission parameters;

[0007] The carbon emission trend in the carbon emission block is evaluated according to the carbon emission amounts in different time intervals, so as to issue an early warning to the carbon emission block when the carbon emission trend is abnormal.

[0008] In one implementation of the present application, the carbon emission information is analyzed by an LLM model to construct a carbon emission calculation model for calculating the carbon emission amount corresponding to the carbon emission block according to the carbon emission information, specifically including:

[0009] Through the LLM model, the prompt words for analyzing carbon emission information are constructed;

[0010] According to the prompt word, extracting the carbon emission calculation formula and carbon emission parameter information used to calculate the carbon emission amount corresponding to the carbon emission block in the carbon emission information;

[0011] According to the carbon emission calculation formula and the carbon emission parameter information, a corresponding carbon emission calculation model is constructed.

[0012] In one implementation of the present application, before collecting the block activity environment data corresponding to the carbon emission block within the time interval at a preset time interval, the method further includes:

[0013] Obtaining the standard carbon emissions corresponding to the carbon emission block;

[0014] Determine a standard time interval for collecting block activity environmental data based on the standard carbon emissions;

[0015] For each moment, the next time interval for collecting the activity environment data of the block in the future is obtained according to the current carbon emissions of the carbon emission block in the time interval at the moment and the standard time interval.

[0016] In one implementation of the present application, according to the current carbon emissions of the carbon emission block in the time interval at the moment and the standard time interval, the next time interval for collecting the activity environment data of the block in the future is obtained, specifically including:

[0017] Comparing the current carbon emissions with the standard carbon emissions to determine whether the current carbon emissions are greater than the standard carbon emissions;

[0018] If so, determining a correction coefficient corresponding to the standard time interval according to the difference between the current carbon emissions and the standard carbon emissions;

[0019] The standard time interval is corrected according to the correction coefficient to obtain the next time interval for collecting the activity environment data of the block in the future.

[0020] In one implementation of the present application, determining the correction coefficient corresponding to the standard time interval according to the difference between the current carbon emissions and the standard carbon emissions specifically includes:

[0021] Determining a first difference between the current carbon emissions and the standard carbon emissions;

[0022] The ratio between the first difference and the standard carbon emission is calculated, and the second difference between the standard correction value and the ratio is used as a correction coefficient corresponding to the standard time interval.

[0023] In one implementation of the present application, after comparing the current carbon emissions with the standard carbon emissions to determine whether the current carbon emissions are greater than the standard carbon emissions, the method further includes:

[0024] If not, the block activity environmental data of the carbon emission block is collected in the future according to the standard time interval.

[0025] In one implementation of the present application, the carbon emission trend in the carbon emission block is evaluated according to the carbon emissions in different time intervals, so as to issue an early warning to the carbon emission block when the carbon emission trend is abnormal, specifically including:

[0026] According to the carbon emissions in different time intervals, a carbon emission curve of the carbon emission block is obtained by fitting;

[0027] According to the carbon emission curve, calculating the growth rate of the carbon emission corresponding to the carbon emission block at different time intervals;

[0028] When the growth rate is greater than the preset rate, it is determined that the carbon emission trend corresponding to the carbon emission block is abnormal and an early warning is issued.

[0029] In one implementation of the present application, before collecting carbon emission information, the method further includes:

[0030] For a preset carbon emission block, a data source corresponding to the carbon emission block is determined according to the block type to which the carbon emission block belongs, so as to collect carbon emission information corresponding to the block type through the data source.

[0031] The embodiment of the present application provides a carbon emission management device based on the LLM model, the device comprising:

[0032] at least one processor;

[0033] and, a memory communicatively coupled to the at least one processor;

[0034] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a carbon emission management method based on an LLM model as described in any one of the above items.

[0035] The embodiment of the present application provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as follows:

[0036] A carbon emissions management method based on the LLM model as described in any of the above items.

[0037] The carbon emission management method based on the LLM model proposed in this application can bring the following results:

[0038] Beneficial effects:

[0039] By analyzing carbon emission information through the LLM model, the carbon emission calculation model used to calculate carbon emissions can be extracted, thereby improving the accuracy and credibility of carbon emission accounting results. The block activity environmental data of the carbon emission block is automatically collected at preset time intervals, and the carbon emission parameters matching the carbon emission calculation model are extracted through the LLM model to realize the automatic calculation and evaluation of carbon emissions. By real-time monitoring and analysis of carbon emission data, this method can timely detect abnormal changes in carbon emission trends and take corresponding early warning measures to improve the real-time and effectiveness of carbon emission management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0041] Figure 1 A schematic diagram of a process of a carbon emission management method based on an LLM model provided in an embodiment of the present application;

[0042] Figure 2 A structural schematic diagram of a carbon emission management device based on the LLM model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0044] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0045] like Figure 1 As shown, the embodiment of the present application provides a carbon emission management method based on the LLM model, including:

[0046] S101: Collect carbon emission information, analyze the carbon emission information through the LLM model, and build a carbon emission calculation model for calculating the carbon emission amount corresponding to the carbon emission block according to the carbon emission information.

[0047] The accounting of carbon emissions has a unified standard. However, since different carbon emission blocks correspond to different block types, the parameter types and calculation methods selected for carbon emissions accounting will also be different. Block types can be various entities that need to monitor carbon emissions, such as factories, enterprises, transportation hubs, and urban residential areas. Each carbon emission block has its own specific attributes or characteristics. Different block types may produce different types of carbon emissions, so different data collection and analysis methods are required. Therefore, in order to accurately obtain carbon emission information for each carbon emission block, it is necessary to find the data source corresponding to the block type. The data source can be in various forms, such as sensor data, government statistics, corporate reports, satellite images, etc. Different block types may need to obtain data from different data sources. For example, for a chemical plant, it may be necessary to obtain real-time emission data from sensors and monitoring equipment inside the plant; while for urban areas, it may be necessary to obtain indirect emission data such as energy consumption and traffic flow from government statistics departments.

[0048] After determining the data source corresponding to a specific block type, carbon emission information related to the block type is collected from these data sources. Carbon emission information may include basic data such as energy consumption, emission data in the production process, and emissions in transportation that can directly calculate carbon emissions. It may also include data that indirectly characterize carbon emissions, such as vehicle flow, animal and plant activity data, and human activity data. By analyzing the collected carbon emission information according to the LLM model, the relevant carbon emission parameters and calculation formulas for calculating the carbon emissions corresponding to the carbon emission block can be extracted, thereby constructing a carbon emission calculation model for calculating the carbon emissions corresponding to the carbon emission block.

[0049] Specifically, the analysis requirements for carbon emission information are clarified, which includes determining the types of key information that need to be extracted, such as carbon emission calculation formulas, carbon emission parameters, etc. Then, through the LLM model, prompt words that match the analysis requirements are generated. Prompt words can be descriptive sentences or phrases to guide the LLM model to find and identify key information in carbon emission information. For example, prompt words can include: Please analyze the calculation formula for carbon emissions and related carbon emission parameter information based on the above materials. The format is as follows: {formula: calculation formula [expressed in python syntax, parameters expressed in x1, x2, etc., coefficients expressed in a1, a2, etc.], parameters: [{name: x1, desp: carbon emission parameters, coefficient: {name: a1, desp: parameter explanation, value: recommended value}}...]}. According to the prompt words, the LLM model will use the language knowledge and context understanding ability learned during its training to accurately identify and extract key information, and then construct the corresponding carbon emission calculation formula based on the extracted carbon emission calculation formula and carbon emission parameter information. The carbon emission calculation model can automatically calculate the carbon emissions of the carbon emission block based on the input carbon emission parameter values.

[0050] S102: According to a preset time interval, the block activity environment data corresponding to the carbon emission block within the time interval is collected, and the carbon emission parameters matching the carbon emission calculation model are extracted from the block activity environment data through the LLM model.

[0051] After the carbon emission calculation model is constructed, the corresponding block activity environmental data of the carbon emission block within the time interval must be collected through the collection equipment at the preset time interval. The block activity environmental data is a data set consisting of the internal environmental data and activity data of the carbon emission block, including environmental monitoring data, visual monitoring data, and remote sensing monitoring data. After the block activity environmental data is input into the LLM model, the carbon emission parameters that match the carbon emission calculation model can be automatically extracted from it.

[0052] In one embodiment, the standard carbon emissions of the carbon emission block are obtained according to the industry standards, historical data or relevant regulations of the carbon emission block. This standard may be a fixed value or a dynamic value based on multiple factors (such as block type, production capacity, etc.). Based on the standard carbon emissions, a standard time interval is set as the collection benchmark. However, since the carbon emissions of each carbon emission block are dynamically changing, carbon emissions analysis through fixed standard time intervals may result in analysis lags and fail to reflect the changes in carbon emissions of the corresponding blocks in a timely manner. Therefore, when collecting the block activity environment data of each time interval, it is necessary to timely adjust the next time interval for collecting the block activity environment data in the future according to the current carbon emissions of the time interval at the current moment and the standard time interval. For example, during the period when the carbon emissions fluctuations are significantly increased, the time interval can be appropriately shortened so that it can be monitored whether there are abnormalities in the carbon emissions.

[0053] Specifically, the current carbon emissions are compared with the standard carbon emissions to determine whether the current carbon emissions are greater than the standard carbon emissions. If the current carbon emissions exceed the standard, a correction factor needs to be determined based on the excess amount (i.e. the difference between the current carbon emissions and the standard carbon emissions) to adjust the standard time interval so that data can be collected more frequently.

[0054] Among them, when making corrections, correction rules can be set according to actual needs, and the rules define the relationship between the excess and the correction coefficient. For example, for every 10% increase in the excess, the correction coefficient is reduced by 10% (which means that the time interval is shortened by 10%). Alternatively, the first difference between the current carbon emissions and the standard carbon emissions can be determined, the ratio between the first difference and the standard carbon emissions can be calculated, and the second difference between the standard correction value and the ratio can be used as the correction coefficient corresponding to the standard time interval. The standard time interval can be corrected according to the correction coefficient, so that after the acquisition of the block activity environment data of the current time interval is completed, the next time interval for acquiring the block activity environment data in the future is determined. If the correction coefficient is less than 1, the new time interval will be the standard time interval multiplied by the correction coefficient. It should be noted that the next time interval can determine the collection period of the future block activity environment data.

[0055] If the current carbon emissions are not greater than the standard carbon emissions, it means that the carbon emissions of the current carbon emission block have not exceeded the standard. At this time, the block activity environmental data can still be collected in the future according to the standard time interval.

[0056] S103: Calculate the carbon emission amount corresponding to the carbon emission block according to the carbon emission calculation model and carbon emission parameters.

[0057] The carbon emission calculation model is used to calculate the carbon emission according to the input carbon emission parameter values. The model will use the built-in carbon emission calculation formula and algorithm, combined with the parameter values ​​to calculate the carbon emissions.

[0058] S104: Evaluate the carbon emission trend in the carbon emission block according to the carbon emission in different time intervals, so as to issue an early warning to the carbon emission block when the carbon emission trend is abnormal.

[0059] After collecting carbon emissions at different time intervals, the carbon emission trend of the carbon emission block can be evaluated based on this data to determine whether there is abnormal emission in the carbon emission block.

[0060] Specifically, according to the carbon emissions in different time intervals, the carbon emission curve of the carbon emission block is fitted. According to the carbon emission curve, the growth rate of the corresponding carbon emissions of the carbon emission block at different time intervals is calculated. When the growth rate is greater than the preset rate, it is determined that the carbon emission trend corresponding to the carbon emission block is abnormal and an early warning is issued. It should be noted that if a carbon emission block has a fixed carbon emission behavior in a specific period of time, then when issuing a carbon emission early warning for this carbon emission block, the management personnel need to determine the actual preset rate based on this behavior. For example, the carbon emission block is a transportation hub. During holidays, the traffic volume increases significantly, and the corresponding carbon emission growth rate will also increase significantly. However, this type of carbon emission behavior is a legal behavior, and it is difficult for relevant departments to save energy and reduce emissions through certain control measures. Therefore, by increasing the preset rate within this period, there is no need for an early warning, saving management resources.

[0061] The above are embodiments of the method proposed in this application. Based on the same idea, some embodiments of this application also provide devices and non-volatile computer storage media corresponding to the above methods.

[0062] Figure 2 The structural diagram of a carbon emission management device based on the LLM model provided in the embodiment of the present application. Figure 2 As shown, including:

[0063] at least one processor; and,

[0064] at least one processor is communicatively connected to a memory; wherein,

[0065] The memory stores instructions executable by at least one processor, the instructions being executed by at least one processor to enable the at least one processor to:

[0066] Collect carbon emission information, analyze the carbon emission information through the LLM model, and build a carbon emission calculation model for calculating the carbon emission amount corresponding to the carbon emission block based on the carbon emission information;

[0067] According to the preset time interval, the block activity environmental data corresponding to the carbon emission block within the time interval is collected, and the carbon emission parameters matching the carbon emission calculation model are extracted from the block activity environmental data through the LLM model;

[0068] According to the carbon emission calculation model and carbon emission parameters, the carbon emission corresponding to the carbon emission block is calculated;

[0069] According to the carbon emissions in different time intervals, the carbon emission trend in the carbon emission block is evaluated to issue an early warning to the carbon emission block when the carbon emission trend is abnormal.

[0070] The embodiment of the present application provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as follows:

[0071] Collect carbon emission information, analyze the carbon emission information through the LLM model, and build a carbon emission calculation model for calculating the carbon emission amount corresponding to the carbon emission block based on the carbon emission information;

[0072] According to the preset time interval, the block activity environmental data corresponding to the carbon emission block within the time interval is collected, and the carbon emission parameters matching the carbon emission calculation model are extracted from the block activity environmental data through the LLM model;

[0073] According to the carbon emission calculation model and carbon emission parameters, the carbon emission corresponding to the carbon emission block is calculated;

[0074] According to the carbon emissions in different time intervals, the carbon emission trend in the carbon emission block is evaluated to issue an early warning to the carbon emission block when the carbon emission trend is abnormal.

[0075] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0076] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0077] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0078] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0083] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0084] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0085] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A carbon emissions management method based on the LLM model, characterized in that: The method comprises: Collect carbon emission information, analyze the carbon emission information through the LLM model, and construct a carbon emission calculation model for calculating the carbon emission amount corresponding to the carbon emission block according to the carbon emission information; According to a preset time interval, the block activity environment data corresponding to the carbon emission block within the time interval is collected, and the carbon emission parameters matching the carbon emission calculation model are extracted from the block activity environment data through the LLM model; Calculate the carbon emission amount corresponding to the carbon emission block according to the carbon emission calculation model and the carbon emission parameters; According to the carbon emissions in different time intervals, the carbon emission trend in the carbon emission block is evaluated to issue an early warning to the carbon emission block when the carbon emission trend is abnormal; The carbon emission information is analyzed by the LLM model to construct a carbon emission calculation model for calculating the carbon emission amount corresponding to the carbon emission block according to the carbon emission information, specifically including: Through the LLM model, the prompt words for analyzing carbon emission information are constructed; According to the prompt word, extracting the carbon emission calculation formula and carbon emission parameter information used to calculate the carbon emission amount corresponding to the carbon emission block in the carbon emission information; According to the carbon emission calculation formula and the carbon emission parameter information, a corresponding carbon emission calculation model is constructed.

2. According to claim 1, a carbon emission management method based on the LLM model is characterized in that: Before collecting the block activity environment data corresponding to the carbon emission block within the time interval at a preset time interval, the method further includes: Obtaining the standard carbon emissions corresponding to the carbon emission block; Determine a standard time interval for collecting block activity environmental data based on the standard carbon emissions; For each moment, the next time interval for collecting the activity environment data of the block in the future is obtained according to the current carbon emissions of the carbon emission block in the time interval at the moment and the standard time interval.

3. The carbon emission management method based on the LLM model according to claim 2 is characterized in that: According to the current carbon emission of the carbon emission block in the time interval at the moment and the standard time interval, the next time interval for collecting the activity environment data of the block in the future is obtained, specifically including: Comparing the current carbon emissions with the standard carbon emissions to determine whether the current carbon emissions are greater than the standard carbon emissions; If so, determining a correction coefficient corresponding to the standard time interval according to the difference between the current carbon emissions and the standard carbon emissions; The standard time interval is corrected according to the correction coefficient to obtain the next time interval for collecting the block activity environment data in the future.

4. The carbon emission management method based on the LLM model according to claim 3 is characterized in that: Determining a correction coefficient corresponding to the standard time interval according to the difference between the current carbon emissions and the standard carbon emissions includes: Determining a first difference between the current carbon emissions and the standard carbon emissions; The ratio between the first difference and the standard carbon emission is calculated, and the second difference between the standard correction value and the ratio is used as a correction coefficient corresponding to the standard time interval.

5. The carbon emission management method based on the LLM model according to claim 2 is characterized in that: After comparing the current carbon emission with the standard carbon emission to determine whether the current carbon emission is greater than the standard carbon emission, the method further includes: If not, the block activity environmental data of the carbon emission block is collected in the future according to the standard time interval.

6. The carbon emission management method based on the LLM model according to claim 1 is characterized in that: According to the carbon emissions in different time intervals, the carbon emission trend in the carbon emission block is evaluated to issue an early warning to the carbon emission block when the carbon emission trend is abnormal, specifically including: According to the carbon emissions in different time intervals, a carbon emission curve of the carbon emission block is obtained by fitting; According to the carbon emission curve, calculating the growth rate of the carbon emission corresponding to the carbon emission block at different time intervals; When the growth rate is greater than a preset rate, it is determined that the carbon emission trend corresponding to the carbon emission block is abnormal and an early warning is issued.

7. The carbon emission management method based on the LLM model according to claim 1 is characterized in that: Before collecting carbon emission information, the method further includes: For a preset carbon emission block, a data source corresponding to the carbon emission block is determined according to the block type to which the carbon emission block belongs, so as to collect carbon emission information corresponding to the block type through the data source.

8. A carbon emission management device based on the LLM model, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a carbon emission management method based on an LLM model as described in any one of claims 1 to 7.

9. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to: A carbon emissions management method based on the LLM model as described in any one of claims 1 to 7.

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