Tissue carbon interrogation automation system and AI Agent implementation method thereof

Abstract

The invention relates to the technical field of carbon emission accounting and artificial intelligence, in particular to a tissue carbon interrogation generation method and system based on an artificial intelligence agent (AI Agent). According to the method, the organization boundary and the accounting range of the organization carbon interrogation service are automatically determined in a natural language interaction mode, the emission source collection list conforming to the user business characteristics is constructed based on the emission scene knowledge base, and the multi-modal data recognition capability is supported; structured and unstructured emission activity data such as energy bills, invoices and production and operation data submitted by users are analyzed, cleaned and processed in a structured mode. According to the method, automatic matching of emission factors and verification of emission amount calculation are realized based on a carbon emission knowledge graph and a self-error correction mechanism, so that auditable total greenhouse gas emission amount accounting is completed. Furthermore, the method can automatically generate a compliant and complete organization carbon checking report file according to national and industrial related carbon checking policies, technical standards and regulatory requirements. Meanwhile, organizational carbon interrogation result interpretation and carbon emission reduction strategy suggestions are provided for the user through a retrieval enhancement generation technology, and the efficiency, accuracy and intelligent degree of organizational carbon interrogation work are remarkably improved. The method is suitable for various industry scenes, realizes full-process automation of carbon data acquisition, accounting and report generation, and has wide popularization and application values.

Description

Technical Field

[0001] This invention relates to the field of organizational carbon emissions management, specifically to an organizational carbon inventory system and method based on an artificial intelligence agent (AI agent). This system aims to improve the efficiency and accuracy of carbon emissions inventory for enterprises or organizations through automated data collection, emissions calculation, and report generation processes, helping users achieve accurate accounting and report generation of organizational carbon emissions. Background Technology

[0002] With the increasing severity of global climate change, the regulation and management of carbon emissions has become a major task for governments, businesses, and social organizations worldwide. Carbon emissions are not only directly related to environmental protection but also have a profound impact on the sustainable development of nations and businesses. Carbon inventory, as an important tool for assessing carbon emissions and formulating emission reduction policies, is widely used in the environmental compliance work of enterprises and organizations.

[0003] Traditional methods of organizing carbon inventory typically rely on manual operation. Companies need to manually collect various emission data, match and calculate emission factors, and ultimately output a carbon emission report. However, this process faces several significant problems: 1. The complexity of data collection and processing. Corporate carbon emission data comes from complex sources, involving multiple aspects such as energy consumption, production data, and transportation. Existing methods typically rely on manual collection of data from different systems and documents, and these data vary greatly in format and type, making manual sorting and integration tedious and error-prone. 2. Lack of standardization and consistency The current process and standards for organizing carbon audits lack uniformity, and different companies vary significantly in their methods of data collection, calculation, and report generation, resulting in inconsistencies in the results. This lack of standardization is particularly prominent in the international carbon market, impacting the carbon compliance management of multinational corporations. 3. High cost and low efficiency of manual intervention Because the carbon inventory process involves a large amount of data entry, calculation, and report generation, these tasks typically require a significant investment of manpower from companies. Manual operations are not only time-consuming but also susceptible to human error, reducing efficiency and accuracy. 4. Report generation and compliance issues The generation of carbon inventory reports still largely relies on manual input, lacking flexibility and intelligent support. While existing reporting tools can generate basic carbon emission data, they often cannot customize report content according to the specific needs and industry characteristics of enterprises, and they are difficult to connect with the latest policies and regulations in real time, which may result in insufficient compliance and timeliness of the reports.

[0004] To address these issues, existing technologies attempt to improve the efficiency of organizational carbon inventory processes through automation tools. However, most current automation tools face the following challenges: 1. Data heterogeneity: Existing tools can usually only handle data input in a single format, making it difficult to deal with complex data from different systems and formats within an enterprise; 2. Insufficient intelligence: Although some tools can automate some processes, they are still insufficient in terms of emission factor matching, calculation accuracy, and personalized report generation, and cannot fully meet the needs of enterprises. 3. Lack of real-time updates: Existing tools are updated infrequently and cannot quickly respond to the latest carbon emission data and policy changes, which affects the adaptability and effectiveness of the tools.

[0005] Therefore, existing organizational carbon inventory technologies still have significant shortcomings in terms of data processing, calculation accuracy, and flexibility. There is an urgent need for a new organizational carbon inventory method and system to achieve more efficient, accurate, and intelligent carbon emission accounting. Summary of the Invention

[0006] To address the problems of low data processing efficiency, insufficient accuracy, and high human intervention in existing organizational carbon inventory methods, this invention provides an intelligent organizational carbon inventory method based on AI Agent. This method utilizes natural language interaction, multimodal data recognition, knowledge graphs, and automated report generation technologies to achieve automation, intelligence, and efficiency throughout the entire organizational carbon inventory process. The specific implementation scheme of this invention is described below.

[0007] S101: Surveying the Accounting Boundaries of the Organization's Carbon Inventory Service through Natural Language Interaction. This invention first interacts with users using natural language processing technology to obtain their needs and accounting boundaries for the organization's carbon inventory. The purpose of this step is to clarify the scope of the organization's carbon inventory service, including but not limited to the types of emission sources, the time and organizational boundaries for data collection, and the scope of activities to be covered. In practice, the system employs an AI Agent based on a dialogue system. Users interact with the Agent in real time through a dialogue page, gradually confirming the boundaries of the organization's carbon inventory service. For example, the system asks users whether the information covers emission sources in Scope 1, Scope 2, and Scope 3 to ensure the accuracy and completeness of the collected information. Based on the user's answers, the AI ​​Agent automatically analyzes and extracts the relevant information, forming clear accounting boundaries and preparing for the subsequent generation of an emission source collection list. This step effectively reduces the complexity of user input through natural language interaction and enables the organization's carbon audit service to be flexibly adjusted according to the characteristics of different enterprises, ensuring the customization and flexibility of the organization's carbon audit service.

[0008] S102: Based on the accounting boundaries of this carbon inventory, a customized emission source collection list is generated to suit the user's situation. According to the accounting boundaries confirmed by the user, the system automatically generates an emission source collection list tailored to the company's characteristics. This list covers the emission sources that need to be calculated and related data items. Users can add or delete emission sources based on their specific operational situation. When generating the list, the system combines industry best practices with user needs, automatically selecting the types of emission sources suitable for the company's operations using preset emission source templates. For example, if the user's company is involved in manufacturing, the system will automatically include data items such as energy consumption and exhaust emissions in the production process; if the company is involved in transportation, it will add carbon emission sources from transportation vehicles. In this way, the system can efficiently provide users with an accurate emission source collection list, while avoiding omissions and errors that occur when manually creating the list.

[0009] S103: Based on multiple multimodal data recognition technologies, it identifies and automatically processes emission source data provided by users to assist users in completing the collection list. During the emission source data collection phase, the system employs various multimodal data recognition technologies to automatically identify and process the raw data provided by users. This data may include data sources in formats such as Excel spreadsheets, PDF files, and API interface data. Specifically, the system uses Optical Character Recognition (OCR) technology to extract text information from scanned documents or images. For structured data (such as Excel files), automated data extraction algorithms are used to parse the data and unify its format into a structured digital format. Simultaneously, for data provided by enterprise systems through API interfaces, the system automatically parses and connects to the system to read the interface data. Through these multimodal data recognition technologies, the system can efficiently and accurately extract key information from different data sources and update the collection list in real time. This step effectively reduces the workload of manual input, improves the efficiency and accuracy of data collection, and helps users efficiently complete the preparation of emission source data sorting and accounting.

[0010] S104: Based on knowledge graphs and self-correction mechanisms, identify emission source data in the user-collected inventory and calculate the total CO2 equivalent emissions from this carbon inventory. After the emission source data is collected, the system uses knowledge graph technology to identify and associate the data. The knowledge graph contains a large number of standard entities in the field of carbon management, such as emission factors, unit conversion rules, and industry standards, which can help the system accurately identify and process the specific data of each emission source. For example, the system matches the collected energy consumption data with the corresponding emission factors and uses standard calculation formulas to calculate the carbon dioxide equivalent emissions. If any data item is abnormal, the system will intelligently correct it through a built-in self-correction mechanism. The system's self-correction mechanism can also be verified based on historical data and statistical models to ensure the accuracy of the calculation results; This step not only enables efficient and accurate emissions calculation, but also further improves data quality through a self-correction mechanism, avoiding errors that may occur during manual calculation.

[0011] S105: Automatically generate complete, compliant, and downloadable organizational carbon inventory reports in accordance with relevant carbon accounting policies, industry standards, and regulations. After calculating carbon emissions, the system automatically generates a compliant organizational carbon inventory report based on current carbon accounting policies, industry standards, and relevant regulations. This report includes detailed carbon emission data, the calculation process, and compliance analysis. The report generation process is fully automated; the system automatically populates the report with information based on the company's accounting boundaries, emission source inventory, and calculation results. Simultaneously, the system checks the report's compliance to ensure it meets international standards (such as ISO 14064), industry standards, and local regulations. The generated report can be downloaded in PDF format for easy archiving, submission, or review by relevant departments. This step greatly simplifies the traditional process of generating carbon inventory reports for organizations, reduces the workload of manual review and modification, and ensures the compliance and efficient generation of report content.

[0012] S106: Based on search-enhanced generation technology, address users' questions about organizational carbon inventory reports and provide carbon reduction suggestions based on the report content. After the report is generated, users may have questions about certain data or calculation results. To address this, the system employs an intelligent question-and-answer function based on retrieval-enhanced generation technology. This function can retrieve relevant content from the report and automatically generate answers based on the specific questions raised by the user. For example, a user can ask, "Why does a certain energy consumption lead to high carbon emissions?" The system will automatically generate an explanation based on the data and background information in the report, helping the user understand the sources and causes of carbon emissions. Simultaneously, the system will analyze carbon emission data to identify possible emission reduction measures and provide corresponding carbon reduction recommendations based on the calculation results. For instance, if carbon emissions are high in a particular stage, the system will suggest that the user adopt more efficient energy sources or consider green alternatives. This step leverages the intelligent capabilities of AI to help users answer questions, provide actionable emission reduction suggestions, and encourage companies to further optimize their carbon emission management.

[0013] AppendixFigure 2 This paper demonstrates the modular design of an organizational carbon inventory agent system. This design, through the collaborative work of multiple sub-modules, ensures the automation and intelligence of the organizational carbon inventory process, and mainly includes the following steps.

[0014] S201: Confirmation of User Information Input and Accounting Boundaries Users input necessary basic information through an interactive interface and confirm the accounting boundaries of the organization's carbon inventory service with an AI agent via natural language interaction. Based on the information provided by the user, the system automatically identifies and determines the applicable scope of the organization's carbon inventory.

[0015] S202: Emission Source Data Collection Module This module is responsible for collecting emission source data from various channels. Users can choose from three data collection methods: uploading data files, with the system automatically reading the files and extracting relevant data; manually entering or modifying data; or connecting with the company's existing system via an API interface to automatically obtain emission source data.

[0016] S203: Emissions Calculation Module The emissions calculation module calculates carbon emissions based on collected emissions source data and corresponding emissions factors. The system automatically calculates emissions and converts them into standardized carbon dioxide equivalent values ​​based on user-selected accounting boundaries and emissions source lists.

[0017] S204: Report Generation Module Based on the calculated carbon emission data, the system automatically generates a standard-compliant organizational carbon inventory report, ensuring the report's compliance, completeness, and usability. The report includes detailed information on emission sources, emission volumes, and compliance analysis.

[0018] S205: Carbon Emission Reduction Recommendations and Analysis Module This module uses search-enhanced generation technology to answer users' questions about organizational carbon inventory reports and provides customized carbon reduction recommendations based on the report content. The system combines industry best practices and technological solutions to help companies optimize their carbon emission management.

[0019] The design of the entire system ensures the efficiency, accuracy, and intelligence of the organization's carbon inventory process, significantly improving the company's ability to calculate carbon emissions and formulate emission reduction strategies. Attached Figure Description

[0020] Appendix Figure 1 The overall implementation scheme and steps of the method of the present invention are shown.

[0021] Appendix Figure 2 The modular design of the organization's carbon inventory agent was demonstrated. Specific implementation methods

[0022] The specific implementation method of this invention aims to design an artificial intelligence-based organizational carbon inventory agent system. Through reasonable system architecture design and the application of intelligent algorithm models, this invention can effectively improve the automation and intelligence level of the organizational carbon inventory process, thereby reducing the complexity and error of manual operation and meeting the needs of enterprises in carbon accounting, report generation, and emission reduction decision support. The following are detailed implementation schemes for each step of the design phase.

[0023] In step S101, a survey module based on Natural Language Processing (NLP) technology needs to be constructed, primarily to obtain organizational carbon inventory accounting boundary information provided by users. This survey module should be designed as a dialogue-based interactive system, allowing users to communicate with the system through natural language and easily provide information related to carbon emission accounting. Specifically, the system will first pose a series of questions to the user, covering different aspects of the accounting boundary, such as the scope of emission sources (e.g., whether supply chain emissions, employee travel emissions, etc. are included), and the time and spatial scope of emission accounting. The design should fully consider the accuracy of the questions and their ease of understanding for users, enabling them to answer quickly and easily. The design of these questions will be based on standard procedures and regulatory requirements for carbon emission accounting, and the system will adjust the depth and scope of the questions in real time to ensure comprehensive acquisition of the required information. The system employs natural language understanding technology to automatically parse user responses, thereby extracting key information (such as whether cross-border emissions are involved, whether indirect emissions are included, etc.). Through semantic analysis and contextual understanding, the system determines the final accounting boundaries based on the user's responses, generating an organizational carbon inventory scope that meets the user's needs.

[0024] In step S102, after determining the accounting boundaries, the design step S102 of this invention is to generate an emission source collection list that matches the user's situation. To this end, a flexible template generation system needs to be constructed. This system can automatically provide users with customized emission source lists based on different accounting boundaries. The system will automatically select the types of emission sources to be collected based on industry standards and user-defined accounting boundaries, and integrate them into the user's emission source list. For example, for carbon emissions from energy consumption, the system will include relevant emission sources from different energy sources such as electricity, natural gas, and coal; for emissions from employee commuting, the system will provide emission source data items for different modes of transportation. The design must ensure the flexibility of the emission inventory, allowing for adjustments based on different enterprise sizes, industry characteristics, and carbon accounting requirements. Furthermore, a dynamic update mechanism should be designed so that the system can promptly adjust the emission source inventory as users provide further information, ensuring it matches actual needs.

[0025] In step S103, during the data collection phase, users provide emission source data by uploading documents, tables, or other file formats. The system needs to incorporate multimodal data recognition technology to efficiently identify and process various data types uploaded by users, including text, tables, and images. To this end, we need to design a multimodal data processing module that combines optical character recognition (OCR), natural language processing (NLP), tabular data parsing, and image recognition technologies to ensure that the system can accurately extract emission source data from files of various formats. For example, for user-uploaded PDF reports, OCR technology can be used to extract text content, while for files with charts and tables, the system needs to be able to parse the tables and extract carbon emission-related data. Furthermore, the design process must consider automated data validation and preprocessing. The system should be able to automatically detect formatting issues, missing data, or outliers in uploaded files and provide modification suggestions to help users efficiently complete data collection.

[0026] In step S104, the knowledge graph-based module is one of the core components of the design of this invention. During the design process, the system should be able to utilize industry-standard knowledge graphs to identify user-submitted emission source data and calculate the total carbon dioxide equivalent emissions based on relevant emission factors. First, a carbon domain knowledge graph needs to be constructed, containing data such as emission factors, carbon accounting standards, and industry regulations. This graph can support entity recognition and data reasoning, helping the system accurately match emission sources with relevant emission factors in user data. For example, for user-provided "coal consumption" data, the system can automatically identify and apply the corresponding emission factors for calculation; At the same time, a self-correction mechanism needs to be incorporated into the design to ensure the accuracy of user data. When the system detects logical contradictions or outliers in the data provided by the user, it should promptly provide feedback to the user and suggest possible correction solutions.

[0027] In step S105, the fifth step in the design phase is to build an automated report generation module. The main task of this module is to automatically generate compliant organizational carbon inventory reports based on carbon emission accounting results and industry policy requirements. To this end, the design team needs to consider how to automatically correlate the accounting results with regulatory requirements and ensure the accuracy and compliance of the report content. The system will automatically populate the various data in the report based on the carbon emission accounting results and ensure that the report format conforms to relevant industry standards. Simultaneously, to meet the needs of different enterprises, the system design needs to consider support for different report formats (such as PDF, Excel, Word, etc.) and provide users with convenient download functionality.

[0028] In step S106, the final and sixth step of the design phase, is to develop retrieval-enhanced generation technology to address user questions about the organization's carbon inventory reports and provide emission reduction recommendations based on the report content. To this end, the design team needs to build a question-answering system based on natural language processing and information retrieval.

[0029] This system can extract relevant information from an organization's carbon inventory report in real time based on user questions and generate accurate answers. Simultaneously, the system should also incorporate industry best practices and emission reduction technologies to provide users with personalized carbon reduction recommendations. For example, if the report shows that a particular emission source accounts for an excessive proportion, the system will automatically suggest reducing that source, helping companies better plan and implement carbon reduction strategies.

Claims

1. An intelligent survey and data processing method for organizational carbon inventory services, characterized in that, Comprising the following steps: S101, investigate the accounting boundary of this organization carbon inventory service through the natural language interaction module to the user, the accounting boundary includes but is not limited to the emission accounting range, time range, space range and industry type; S102, based on the accounting boundary, the customized emission source management module generates the emission source collection list that conforms to the user's situation, and automatically adapts to the user's carbon emission source structure; S103, through the multi-modal data recognition technology module, identify the emission source data provided by the user and automatically process, assist the user to complete the filling of the emission source list, including text data, table data and image data, etc.; S104, based on the knowledge graph and self-correction mechanism, the emission source data in the collection list is checked, the total emission of this organization carbon inventory is calculated by using the carbon accounting standard, and the carbon dioxide equivalent value is obtained; S105, according to the relevant carbon accounting policy, industry standard and regulation requirements, automatically generate the organization carbon inventory report file that conforms to the regulation, is complete and can be downloaded; S106, based on the search enhancement generation technology, answer the user's questions about the organization carbon inventory report, and provide personalized carbon emission reduction suggestions for the user according to the report content.

2. The method of claim 1, wherein, In the step S101, the natural language interaction module obtains the feedback of the user about the accounting boundary through the dialogue engine based on semantic understanding, and dynamically adjusts the investigation questionnaire according to the feedback, to ensure the accuracy and integrity of the accounting information collection.

3. The method of claim 1, wherein, In the step S102, the emission source collection list is automatically customized and generated for the user by combining the industry-specific carbon emission standards and policy framework, and the emission source project list that conforms to the business mode of the user is generated.

4. The method of claim 1, wherein, In the step S103, the multi-modal data recognition technology module includes an optical character recognition (OCR) module, an image recognition module and a table data analysis module, which can structure the user uploaded emission source data and automatically fill in the list.

5. The method of claim 1, wherein, In the step S104, the knowledge graph links the standard entities in the carbon accounting field, such as emission factor, energy type, industry classification and other information, to automatically correct the emission source data and use the self-correction mechanism to improve the data accuracy.

6. The method of claim 1, wherein, In the step S105, the organization carbon inventory report generation automatically generates a standardized report according to the relevant regulations, and supports customizing part of the content according to the user's needs.

7. The method of claim 1, wherein, In the step S106, the search enhancement generation technology includes a question and answer engine based on a large language model, which can answer the user's questions about the organization carbon inventory report, and provide practical carbon emission reduction suggestions for the user.

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