Carbon emission source and boundary identification and accounting method, system, equipment and medium

Abstract

The invention provides a carbon emission source and boundary identification and accounting method, system and device and a medium, and belongs to the technical field of carbon emission management. The method comprises the following steps: collecting enterprise organization related information, including an organization structure, a business range, a financial statement and production process flow data; performing text formatting processing on the related information of the enterprise organization, inputting the information into a pre-training generation type large model based on a Transform architecture for analysis, and identifying carbon boundary and carbon emission source information of the enterprise organization; constructing an accounting method library containing a plurality of carbon emission source accounting methods, and analyzing and inputting emission source types, industry ranges, accounting formulas and required data suitable for each accounting method; according to the identified carbon emission source information, matching and screening are carried out by synthesizing semantics and rules, and an applicable accounting method is matched in an accounting method library; and issuing an identification result and a matched accounting method, and displaying the carbon emission source information and the accounting method through a visual interface.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission management technology, and more specifically relates to a method, system, device and medium for identifying and calculating carbon emission sources and boundaries. Background Technology

[0002] As global attention to climate change continues to grow, monitoring and managing the carbon emissions of businesses and organizations is becoming increasingly important. Determining carbon emission accounting boundaries and identifying emission sources are fundamental and crucial steps in carbon emission management.

[0003] In existing technologies, methods for determining organizational boundaries mainly include operational control, financial control, and equity ratio methods. However, these methods have many limitations in practical applications. For example, the operational control method may fail to include some organization-related emission sources within the boundary due to its vague definition of operational activities; the financial control method focuses too much on the financial aspect and may overlook some links that generate significant carbon emissions in the organization's actual operations but have weak financial connections; and the equity ratio method is prone to inaccurate accounting scope when determining organizational boundaries under complex equity structures.

[0004] In emission source identification, traditional methods require staff to possess both theoretical knowledge and on-site experience, involving steps such as obtaining a list of key energy-consuming equipment, discussing process flows, and conducting site inspections. This approach is not only inefficient and demands extremely high levels of professional expertise from staff, but it is also prone to overlooking emission sources during on-site investigations. For example, in some factories with complex processes involving numerous procedures and equipment, manual identification of emission sources is difficult to achieve comprehensively and accurately. Furthermore, differences in the understanding and judgment of process flows among different staff members can also lead to inconsistencies in emission source identification results.

[0005] In terms of matching accounting methods, existing technologies often lack systematicity and intelligence. For identified emission sources, it is difficult to quickly and accurately match the most suitable method from a large number of accounting methods, which affects the accuracy and reliability of carbon emission accounting. For example, different industries and types of emission sources may be applicable to multiple accounting methods, but there is currently a lack of effective means to automatically screen and adapt them, relying heavily on human experience and judgment, which easily leads to the problem of inappropriate selection of accounting methods. Summary of the Invention

[0006] To address the above problems, the present invention aims to provide a method, system, device, and medium for identifying and calculating carbon emission sources and boundaries. By leveraging generative large models, semantic matching algorithms, and visualization technology, it can accurately identify carbon emission sources and boundaries and efficiently match calculation methods, providing strong support for enterprises' carbon management and low-carbon transformation.

[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, embodiments of this application provide a method for identifying and calculating carbon emission sources and boundaries, including: Collect relevant information about the enterprise's organization, including organizational structure, business scope, financial statements, and production process data; The relevant information of the enterprise organization is formatted into text and then input into a pre-trained generative large model based on the Transformer architecture for analysis to identify the carbon boundary and carbon emission source information of the enterprise organization. Construct an accounting method library containing multiple carbon emission source accounting methods, and parse and input the emission source type, industry scope, accounting formula and required data applicable to each accounting method; Based on the identified carbon emission source information, a suitable accounting method is selected from the accounting method library by combining semantic and rule matching filtering. The system distributes the identification results and matching calculation methods, and displays carbon emission source information and calculation methods through a visual interface.

[0008] In one optional implementation, the collection of enterprise organizational information, including organizational structure, business scope, financial statements, and production process data, includes: Collect organizational structure information of enterprises, including the hierarchical relationship and equity ratio of parent company, subsidiaries, and branches; Collect a description of the company's business scope, including the types of products it produces and the services it provides; Collect financial statement data from enterprises and annotate financial information related to energy procurement and production equipment investment; Collect facility information, equipment information, and operational data within the enterprise organization.

[0009] In an optional implementation, the step of formatting the enterprise organization-related information into text and inputting it into a pre-trained generative large model based on the Transformer architecture for analysis to identify the enterprise organization's carbon boundary and carbon emission source information includes: Perform text formatting on enterprise organizational information, extract relevant information from financial statement data and convert it into text description; The processed information is input into a pre-trained generative large model based on the Transformer architecture. The large model is used to extract organizational structure and business scope information from the enterprise's organizational information. Based on the equity ratio and actual operational control, subsidiaries and branches that should be included in the organizational carbon boundary are identified, and the enterprise's organizational carbon boundary is determined. By using large models to extract key information from production process data and combining it with equipment information, carbon emission sources are identified, emission source types are determined, and carbon emission source information is generated.

[0010] In an optional implementation, the step of constructing an accounting method library containing multiple carbon emission source accounting methods, and parsing and inputting the emission source type, industry scope, accounting formula, and required data applicable to each accounting method, includes: Collect various carbon emission source accounting standards and methods published by the industry, as well as accounting methods developed by industry associations for the characteristics of their respective industries; Each accounting method is analyzed to extract the applicable emission source types, industry scope, accounting formulas, and required data; Organize and extract the information and enter it into the accounting method library.

[0011] In an optional implementation, the step of matching applicable accounting methods in the accounting method library based on the identified carbon emission source information, combining semantic and rule matching filtering, includes: A combination of semantic matching algorithm and rule matching is used to select applicable accounting methods from the accounting method library based on the emission source type and industry scope, and generate a list of accounting methods. Based on the production process data and data availability, the accounting method is matched from the list of accounting methods.

[0012] In an optional implementation, the step of issuing the identification results and matching calculation methods, and displaying carbon emission source information and calculation methods through a visual interface, includes: The identification results are issued, and information on the organizational carbon boundary and carbon emission sources of the corresponding enterprises is generated based on the identification results; Based on the organization's carbon boundary range and carbon emission source information, the location of each production plant within the organization's carbon boundary is displayed in map form through a visual interface, and the distribution of each carbon emission source is marked with different colors. When the carbon emission source icon in the visualization interface is clicked, the carbon emission source information and the corresponding calculation method will pop up.

[0013] In an optional implementation, the step of organizing and extracting the information and entering it into the accounting method library includes: For accounting methods applicable to stationary combustion emission sources in industrial boilers, enter the following information into the accounting method library: The applicable emission source type is recorded as stationary combustion in industrial boilers; The applicable industry scope is recorded as manufacturing. The calculation formula is recorded as: Emissions = Fuel Consumption × Emission Factor; The required data records are fuel consumption and the corresponding emission factors.

[0014] Secondly, embodiments of this application also provide a system for identifying and calculating carbon emission sources and boundaries, including: The information collection module is used to collect relevant information about the enterprise organization, including organizational structure, business scope, financial statements and production process data; The large model recognition module is used to format text information related to enterprise organizations and input it into a pre-trained generative large model based on the Transformer architecture for analysis to identify the carbon boundary and carbon emission source information of enterprise organizations. The accounting method library module is used to build an accounting method library containing various carbon emission source accounting methods, and to parse and input the emission source type, industry scope, accounting formula and required data applicable to each accounting method; The accounting method matching module is used to select applicable accounting methods from the accounting method library based on the identified carbon emission source information, combining semantic and rule matching. The results output module is used to distribute the identification results and matching calculation methods, and to display carbon emission source information and calculation methods through a visual interface.

[0015] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the carbon emission source and boundary identification and accounting method as described in any of the above.

[0016] Fourthly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the carbon emission source and boundary identification and calculation method as described in any of the above.

[0017] As can be seen from the above technical solutions, the present invention has the following advantages: The carbon emission source and boundary identification and accounting method provided in this application automatically collects and analyzes multi-dimensional information from enterprises, uses a large-scale model to accurately identify carbon emission sources and boundaries, and intelligently matches accounting methods with visualization displays. This greatly improves the efficiency and accuracy of carbon accounting, reduces manual operation costs and errors, and provides an intuitive and visual interface to help enterprises quickly grasp their carbon emission status, providing strong support for their carbon management decisions. This method is highly versatile and widely applicable to enterprises in different industries, effectively promoting their green and low-carbon transformation and sustainable development.

[0018] This application significantly reduces the time required for determining organizational carbon boundaries and identifying emission sources through the rapid analysis and processing capabilities of large-scale models. Compared to traditional manual methods, it can process large amounts of organizational information in a short time, quickly and accurately identify emission sources, improve work efficiency, and enable enterprises and organizations to carry out carbon emission management more promptly.

[0019] This application's large-scale model, based on abundant data and powerful algorithms, enables a more comprehensive and accurate understanding of an organization's business and production processes, avoiding omissions and errors caused by subjective factors during manual identification. Regarding accounting method matching, intelligent algorithms precisely filter from the accounting method library, improving the compatibility between accounting methods and emission sources, thereby ensuring the accuracy of carbon emission accounting results.

[0020] This application reduces reliance on a large number of professionals for on-site investigations and the selection of complex accounting methods, thereby lowering labor and time costs. At the same time, by improving the accuracy of carbon emission management, it avoids potential economic losses and compliance risks caused by accounting errors.

[0021] This application can adapt to the carbon emission management needs of organizations of different industries and sizes. Whether it is a small enterprise with a simple process or a large company with complex operations, it can accurately determine the organization's carbon boundary, identify emission sources, and match appropriate accounting methods through the method of this invention. Attached Figure Description

[0022] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating the method for identifying and calculating carbon emission sources and boundaries provided in this application.

[0024] Figure 2 A schematic diagram of the structure of the carbon emission source and boundary identification and accounting system provided in this application.

[0025] Figure 3 A schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0026] The various embodiments of this disclosure will be described more fully in the detailed steps below for identifying and accounting for carbon emission sources and boundaries. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0027] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figure 1 The diagram shows a flowchart of a method for identifying and calculating carbon emission sources and boundaries in a specific embodiment. The method includes: S1: Collect relevant information about the enterprise organization, including organizational structure, business scope, financial statements and production process data.

[0030] In a specific implementation, an enterprise organization-related information collection system is built, and enterprise users are guided through the system interface to complete the entry of information in all dimensions. Regarding organizational structure information collection, a hierarchical input template is provided. Enterprise users fill in basic information such as the names, registered addresses, and contact information of the parent company, subsidiaries, and branches according to the template, while clearly defining the hierarchical relationship between each entity and accurately entering the equity ratio, such as the parent company holding 60% of the equity of a subsidiary. The system automatically verifies the entered equity data to ensure accuracy. For business scope description, standardized input boxes are set up. Enterprise users enter the type of products they produce, such as "synthetic resins and rubber products" for chemical companies, and the services provided, such as "chemical product R&D consulting services." The system supports keyword suggestion to help users standardize their input. In the financial statement data upload stage, various formats of financial statement files can be uploaded, such as Excel and PDF. After uploading, the system automatically identifies energy procurement-related data in the reports, such as electricity procurement costs and natural gas procurement costs, as well as production equipment investment information, such as the purchase amount and installation costs of newly added reactors, and highlights these key information. When collecting production process data, a flowchart drawing tool and a text description area are provided. Enterprise users need to describe in detail everything from the procurement channels and specifications of raw materials, such as crude oil and additives for chemical companies, to the filtration and purification operations in the pretreatment stage, to the reaction conditions and catalyst usage in the chemical reaction process, as well as the product separation and purification methods and the finished product packaging process.

[0031] At the same time, at each process node, the system forms are used to enter a list of equipment, including equipment name and model, such as "Reaction Kettle R-101", and fill in the equipment operating parameters, such as power of 50kW, average daily operating time of 8 hours, energy consumption type of natural gas, and hourly consumption of 10m³, etc.

[0032] S2: The relevant information of the enterprise organization is formatted into text and input into a pre-trained generative large model based on the Transformer architecture for analysis to identify the carbon boundary and carbon emission source information of the enterprise organization.

[0033] In a specific implementation, the first step is to format the enterprise's organizational information into text. For financial statement data, information such as energy procurement costs, corresponding energy types, and uses is extracted and transformed into textual descriptions understandable to the larger model, such as "The cost of natural gas purchased this year is X yuan, mainly used for heating the reactor in the production workshop." For production process data, the equipment list, operating parameters, and process flow are linked to generate structured text such as "In the chemical reaction process, a reactor of model R-101 with a power of 50kW is used, powered by natural gas, consuming 10m³ per hour, and operating for an average of 8 hours per day." All formatted information is input into a pre-trained generative large model based on the Transformer architecture. The large model extracts key information such as equity ratios and operational control descriptions from the text. Based on pre-defined decision rules, such as an equity ratio exceeding 50% indicating actual control, and considering the relevance of business scope, it determines which subsidiaries and branches should be included in the organization's carbon boundary. For example, even if a subsidiary has a 45% equity stake, if the parent company has actual decision-making power over its production and operations, it is also included in the carbon boundary, and a boundary identification report is generated, clearly defining the organizational units included in the boundary and the basis for the determination.

[0034] For example, the process of identifying equity percentages includes: First, the large model extracts entities related to equity from the text, such as the holding entity, the held entity, and the equity percentage. Then, it analyzes the shareholding relationships between these entities, including direct and indirect shareholding. For direct shareholding, the large model can easily extract the specific equity percentage. For indirect shareholding, it needs to parse nested shareholding structures, such as a parent company holding shares indirectly through a subsidiary. Furthermore, the large model must handle fuzzy expressions, such as "wholly owned," and correctly infer it as a 100% equity percentage. The entire process involves information extraction, relationship analysis, and semantic understanding to ensure accurate identification of equity percentages.

[0035] Simultaneously, the large-scale model extracts key information from the production process data and, combined with equipment information, identifies carbon emission sources, determines their types, and generates carbon emission source information. For example, the large-scale model performs in-depth analysis of the production process and equipment information, using natural language processing technology to extract key information related to energy consumption and chemical reactions, such as identifying the energy consumption behavior of "the reactor uses natural gas for heating" from the text. Combined with the energy consumption type of the equipment, the reactor is identified as a carbon emission source, and its emission source type is determined to be a stationary combustion emission source based on the energy consumption method, thus classifying the emission source as range one. For equipment using purchased electricity, such as workshop ventilation fans, it is identified as a range two emission source, and information such as the process and equipment name of the emission source is recorded.

[0036] S3: Construct an accounting method library containing multiple carbon emission source accounting methods, and parse and input the emission source type, industry scope, accounting formula and required data applicable to each accounting method.

[0037] In specific implementation methods, a wide range of carbon emission source accounting standards and methods published internationally, nationally, and within industries are collected. Through professional database searches and standard platform integration, accounting methods for different types of emission sources, such as stationary combustion and process emissions, from the IPCC National Greenhouse Gas Inventory Guidelines are obtained, along with documents from various industry associations, such as the detailed rules for carbon emission accounting in the chemical industry formulated by the China Chemical Industry Association. Each collected accounting method was analyzed in detail. By analyzing standard documents, applicable emission source types were extracted, such as stationary combustion emission sources from industrial boilers and mobile emission sources; the applicable industry scope was clarified, such as the chemical and steel industries within manufacturing; accounting formulas were compiled, such as "emissions = fuel consumption × emission factor" and "emissions = activity level × emission factor × oxidation rate"; and the required data, such as fuel consumption, corresponding emission factors, and activity level data, were determined. The analyzed information was then compiled and entered into the accounting method library. A database table structure was established, including fields such as method ID, emission source type, industry scope, accounting formula, data requirements, and standard source. For example, for the accounting method applicable to stationary combustion emission sources from industrial boilers, the library records its emission source type as "stationary combustion from industrial boilers," its applicable industry scope as "manufacturing," its accounting formula as "emissions = fuel consumption × emission factor," and the required data as fuel consumption (in m³ or kg) and the corresponding emission factor (in kgCO / m³ or kgCO / kg), and notes that the method originates from the IPCC National Greenhouse Gas Inventory Guidelines 2019. At the same time, the accounting method library is updated regularly to include newly released standard methods.

[0038] S4: Based on the identified carbon emission source information, a suitable accounting method is matched from the accounting method library by combining semantic and rule matching filtering.

[0039] In a specific implementation, the identified carbon emission source information is used to extract key features such as emission source type, industry, and production process involved. For example, if the emission source type is a stationary combustion emission source, the industry is the chemical industry, and the production process involved is the use of natural gas to heat the reaction vessel in a chemical reaction process. At this point, a combination of semantic matching and rule matching is used to filter applicable accounting methods from the accounting method library based on emission source type and industry scope, generating a list of accounting methods. Then, based on production process data and data availability, accounting methods are matched from the accounting method list.

[0040] For example, rule matching is first performed. Based on the emission source type "stationary combustion emission source" and the industry "chemical industry", all accounting methods applicable to stationary combustion emission sources in the accounting method library are selected to form a preliminary list of methods. Then, a semantic matching algorithm is used to calculate the semantic similarity between the production process descriptions related to the emission sources and the applicable scenario descriptions of each method in the method list. Further filtering is then performed based on the company's data availability. If the company can accurately obtain the natural gas fuel consumption from energy consumption records and obtain corresponding emission factor data from an authoritative database, then the calculation method based on the formula "emissions = fuel consumption × emission factor" is selected from the method list. If the company cannot accurately obtain emission factor data but has detailed natural gas composition analysis reports and reactor operating parameters, then the method of calculating emission factors and then calculating emissions through fuel composition and equipment operating parameters is selected, and a method matching report is generated explaining the matching basis and the applicability of the selected method.

[0041] S5: Issue the identification results and matching calculation methods, and display carbon emission source information and calculation methods through a visual interface. In a specific implementation, the identified organizational carbon boundaries, carbon emission source information, and matched accounting methods are first compiled and summarized to generate a results report which is then sent to the user. The report clearly lists the enterprise's organizational carbon boundary range, including the names and production and operation addresses of the parent company and its subsidiaries included in the boundary. Then, detailed information on each emission source is listed, such as the emission source name being "Chemical Reaction Process R-101 Reactor," located in Area A of the production workshop, type being a stationary combustion emission source, and belonging to Scope 1. Simultaneously, the matching accounting method and key accounting parameters are listed for each emission source. For example, the accounting method is "Emissions = Fuel Consumption × Emission Factor," where fuel consumption can be obtained from the natural gas metering records of the enterprise's energy management system, and the emission factor is determined according to the type of natural gas, referring to the "Guidelines for the Compilation of Provincial Greenhouse Gas Inventories" and taking the value as Y kgCO / m³. Finally, a visual interface was developed to display relevant information. Based on a map of the company's plant area, the interface marks the locations of each production plant within the organization's carbon boundary. Different colored icons are used on the plant map to indicate the distribution of each emission source; for example, red icons represent stationary combustion emission sources, and blue icons represent range 2 emission sources. When a user clicks on each emission source icon, a detailed information window pops up, displaying the source's name, location, type, range, corresponding accounting method, key parameters, and data acquisition methods. Bar charts, pie charts, and other graphs are used to display the proportion of different types of emission sources and the application of different accounting methods, allowing company managers to intuitively understand carbon emissions and supporting subsequent data collection and carbon emission management decisions.

[0042] In this embodiment, a standardized data acquisition system is built to achieve the standardized acquisition of all organizational information. Relying on the Transformer architecture model, carbon boundaries and emission sources are accurately identified. Combined with a multi-dimensional accounting method library and intelligent matching mechanism, an appropriate accounting scheme is determined. Finally, the results are presented intuitively through a visual interface. This not only improves the accuracy and efficiency of carbon emission source and boundary identification and ensures the scientific adaptability of the accounting method, but also provides enterprises with a clear basis for carbon emission management, assists in subsequent data collection and decision optimization, and effectively promotes the standardization and intelligent development of enterprise carbon management.

[0043] like Figure 2 As shown, the following are embodiments of the carbon emission source and boundary identification and accounting system provided in this disclosure. This system and the carbon emission source and boundary identification and accounting methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the carbon emission source and boundary identification and accounting system, please refer to the embodiments of the carbon emission source and boundary identification and accounting methods described above.

[0044] A system for identifying and calculating carbon emission sources and boundaries, comprising: The information collection module is used to collect relevant information about the enterprise organization, including organizational structure, business scope, financial statements, and production process data.

[0045] The large model recognition module is used to format text information related to enterprise organizations and input it into a pre-trained generative large model based on the Transformer architecture for analysis, in order to identify the carbon boundary and carbon emission source information of enterprise organizations.

[0046] The accounting method library module is used to build an accounting method library containing various carbon emission source accounting methods. It parses and inputs the emission source type, industry scope, accounting formula and required data applicable to each accounting method.

[0047] The accounting method matching module is used to select applicable accounting methods from the accounting method library based on the identified carbon emission source information, combining semantic and rule matching.

[0048] The results output module is used to distribute the identification results and matching calculation methods, and to display carbon emission source information and calculation methods through a visual interface.

[0049] The carbon emission source and boundary identification and accounting system provided in this embodiment significantly improves the accuracy and efficiency of carbon emission accounting by comprehensively collecting enterprise data, accurately identifying carbon boundaries and emission sources using generative large models, constructing an intelligent matching accounting method library, and combining visualization technology for intuitive display. This effectively helps enterprises make low-carbon operation decisions.

[0050] Figure 3A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0051] The carbon emission source and boundary identification and accounting method provided in this application embodiment can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0052] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0053] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0054] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0055] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0056] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0057] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0058] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0059] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0060] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0061] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0062] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0063] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0064] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0065] The aforementioned electronic device realizes the collection of enterprise organization information for the carbon emission source and boundary identification and accounting method of this application, accurately identifies carbon boundaries and emission sources using generative large models, constructs an accounting method library and intelligently matches it, and intuitively displays it with visualization technology, thus achieving the beneficial effects of improving the accuracy, efficiency and ease of use of carbon emission accounting.

[0066] The storage medium provided in this application stores a program product capable of identifying and calculating carbon emission sources and boundaries.

[0067] Methods for identifying and accounting for carbon emission sources and boundaries include: Collect relevant information about the enterprise's organization, including organizational structure, business scope, financial statements, and production process data; The relevant information of the enterprise organization is formatted into text and then input into a pre-trained generative large model based on the Transformer architecture for analysis to identify the carbon boundary and carbon emission source information of the enterprise organization. Construct an accounting method library containing multiple carbon emission source accounting methods, and parse and input the emission source type, industry scope, accounting formula and required data applicable to each accounting method; Based on the identified carbon emission source information, a suitable accounting method is selected from the accounting method library by combining semantic and rule matching filtering. The system distributes the identification results and matching calculation methods, and displays carbon emission source information and calculation methods through a visual interface.

[0068] In some possible implementations, the carbon emission source and boundary identification and accounting method of this disclosure can be implemented as a program product including program code, which, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0069] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0070] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying and calculating carbon emission sources and boundaries, characterized in that, include: Collect relevant information about the enterprise's organization, including organizational structure, business scope, financial statements, and production process data; The relevant information of the enterprise organization is formatted into text and then input into a pre-trained generative large model based on the Transformer architecture for analysis to identify the carbon boundary and carbon emission source information of the enterprise organization. Construct an accounting method library containing multiple carbon emission source accounting methods, and parse and input the emission source type, industry scope, accounting formula and required data applicable to each accounting method; Based on the identified carbon emission source information, a suitable accounting method is selected from the accounting method library by combining semantic and rule matching filtering. The system distributes the identification results and matching calculation methods, and displays carbon emission source information and calculation methods through a visual interface.

2. The method for identifying and calculating carbon emission sources and boundaries according to claim 1, characterized in that, The collected enterprise organizational information includes organizational structure, business scope, financial statements, and production process data, including: Collect organizational structure information of enterprises, including the hierarchical relationship and equity ratio of parent company, subsidiaries, and branches; Collect a description of the company's business scope, including the types of products it produces and the services it provides; Collect financial statement data from enterprises and annotate financial information related to energy procurement and production equipment investment; Collect facility information, equipment information, and operational data within the enterprise organization.

3. The method for identifying and calculating carbon emission sources and boundaries according to claim 2, characterized in that, The process involves formatting text related to enterprise organizations and inputting it into a pre-trained generative large model based on the Transformer architecture for analysis to identify the carbon boundary and carbon emission source information of the enterprise organization, including: Perform text formatting on enterprise organizational information, extract relevant information from financial statement data and convert it into text description; The processed information is input into a pre-trained generative large model based on the Transformer architecture. The large model is used to extract organizational structure and business scope information from the enterprise's organizational information. Based on the equity ratio and actual operational control, subsidiaries and branches that should be included in the organizational carbon boundary are identified, and the enterprise's organizational carbon boundary is determined. By using large models to extract key information from production process data and combining it with equipment information, carbon emission sources are identified, emission source types are determined, and carbon emission source information is generated.

4. The method for identifying and calculating carbon emission sources and boundaries according to claim 3, characterized in that, The construction of an accounting method library containing multiple carbon emission source accounting methods involves parsing and inputting the emission source type, industry scope, accounting formula, and required data applicable to each accounting method, including: Collect various carbon emission source accounting standards and methods published by the industry, as well as accounting methods developed by industry associations for the characteristics of their respective industries; Each accounting method is analyzed to extract the applicable emission source types, industry scope, accounting formulas, and required data; Organize and extract the information and enter it into the accounting method library.

5. The method for identifying and calculating carbon emission sources and boundaries according to claim 4, characterized in that, The step of matching applicable accounting methods in the accounting method library based on the identified carbon emission source information, combined with semantic and rule matching, includes: A combination of semantic matching algorithm and rule matching is used to select applicable accounting methods from the accounting method library based on the emission source type and industry scope, and generate a list of accounting methods. Based on the production process data and data availability, the accounting method is matched from the list of accounting methods.

6. The method for identifying and calculating carbon emission sources and boundaries according to claim 5, characterized in that, The method for issuing identification results and matching is described, and carbon emission source information and the calculation method are displayed through a visual interface, including: The identification results are issued, and information on the organizational carbon boundary and carbon emission sources of the corresponding enterprises is generated based on the identification results; Based on the organization's carbon boundary range and carbon emission source information, the location of each production plant within the organization's carbon boundary is displayed in map form through a visual interface, and the distribution of each carbon emission source is marked with different colors. When the carbon emission source icon in the visualization interface is clicked, the carbon emission source information and the corresponding calculation method will pop up.

7. The method for identifying and calculating carbon emission sources and boundaries according to claim 4, characterized in that, The extracted information, which is then entered into the accounting method library, includes: For accounting methods applicable to stationary combustion emission sources in industrial boilers, enter the following information into the accounting method library: The applicable emission source type is recorded as stationary combustion in industrial boilers; The applicable industry scope is recorded as manufacturing. The calculation formula is recorded as: Emissions = Fuel Consumption × Emission Factor; The required data records are fuel consumption and the corresponding emission factors.

8. A system for identifying and calculating carbon emission sources and boundaries, characterized in that, The system employs the carbon emission source and boundary identification and accounting method as described in any one of claims 1 to 7; The system includes: The information collection module is used to collect relevant information about the enterprise organization, including organizational structure, business scope, financial statements and production process data; The large model recognition module is used to format text information related to enterprise organizations and input it into a pre-trained generative large model based on the Transformer architecture for analysis to identify the carbon boundary and carbon emission source information of enterprise organizations. The accounting method library module is used to build an accounting method library containing various carbon emission source accounting methods, and to parse and input the emission source type, industry scope, accounting formula and required data applicable to each accounting method; The accounting method matching module is used to select applicable accounting methods from the accounting method library based on the identified carbon emission source information, combining semantic and rule matching. The results output module is used to distribute the identification results and matching calculation methods, and to display carbon emission source information and calculation methods through a visual interface.

9. An electronic device 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 program, it implements the steps of the carbon emission source and boundary identification and accounting method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for identifying and calculating carbon emission sources and boundaries as described in any one of claims 1 to 7.

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