Digital carbon flow monitoring system for carbon emission and wind power photovoltaic cooperative emission reduction and construction method

Abstract

The invention discloses a digital carbon flow monitoring system for carbon emission and wind power photovoltaic cooperative emission reduction and a construction method, and belongs to the field of carbon flow intelligent monitoring, and the system comprises a front-end UI which is used for constructing a digital carbon flow monitoring platform interface; the SDK layer is used for providing function support for the front-end UI; the API layer is used for connecting front-end and background services and realizing data interaction and function calling; the authentication layer is used for guaranteeing user security and system access specifications; the data layer is used for storing, managing and processing multi-source data. According to the invention, comprehensive and efficient monitoring and management of carbon emission and renewable energy synergism are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring of carbon flow, and particularly relates to a digital carbon flow monitoring system for collaborative emission reduction of carbon emissions and wind power and photovoltaic power, and a construction method thereof. Background Art

[0002] With the advancement of carbon neutrality, although numerous carbon emission reduction demonstration projects have been implemented and significant progress has been made in exploring emission reduction methods and planning emission reduction programs. However, most studies still focus on macro-level policies and static emission reduction path design, without considering CO 2 as a resource or commodity that can circulate. Moreover, in view of the problems existing in the existing carbon emission monitoring technologies, such as insufficient real-time performance, limited data integration ability, lack of collaborative optimization, etc., a dynamic carbon flow management method based on a smart platform is proposed. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes a digital carbon flow monitoring system for collaborative emission reduction of carbon emissions and wind power and photovoltaic power, and a construction method thereof, so as to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above object, the present invention provides a construction method for a digital carbon flow monitoring platform for collaborative emission reduction of carbon emissions and wind power and photovoltaic power, including: a front-end UI, an SDK layer, an API layer, an authentication layer, and a data layer;

[0005] Among them, the front-end UI is used to construct the interface of the digital carbon flow monitoring platform;

[0006] The SDK layer is used to provide functional support for the front-end UI;

[0007] The API layer is used to connect the front end with the background service to realize data interaction and function call;

[0008] The authentication layer is used to ensure user security and system access specifications;

[0009] The data layer is used to store, manage, and process multi-source data.

[0010] Optionally, the front-end UI includes: a data visualization module, a user operation and input module, a real-time monitoring and warning module, a data interaction and report generation module, and a multi-role support module;

[0011] Among them, the data visualization module is used to provide an analysis interface for users based on embedded interactive charts, GIS maps, and dynamic heat maps;

[0012] The user operation and input module is used for users to input emission reduction targets, set optimization directions, and dynamically adjust path planning and parameter selection;

[0013] The real-time monitoring and warning module is used to track the carbon emission and storage status in real time, dynamically detect anomalies and provide warnings;

[0014] The data interaction and report generation module is used to generate customized carbon flow monitoring reports, emission reduction effect analysis, and optimization plan documents;

[0015] The multi-role support module is used to provide personalized functions for different users.

[0016] Optionally, the authentication layer includes: an account management module, an application management module, a service management module, an organization management module, and an authentication management module;

[0017] The account management module is used for user identity authentication;

[0018] The application management module is used for fine-grained management of users to ensure that users with different roles can only access function data corresponding to their roles;

[0019] The service management module is used to provide carbon flow path optimization services;

[0020] The organization management module is used to support a multi-tenant architecture and provide an isolated operating environment for enterprises, institutions, and carbon trading users;

[0021] The authentication management module is used for identity authentication and authorization management of users and services.

[0022] Optionally, the data layer adopts a hybrid database architecture, including PostgreSQL and MongoDB;

[0023] Among them, PostgreSQL is used to store structured data;

[0024] MongoDB is used to store unstructured data.

[0025] The present invention also provides a construction method for a digital carbon flow monitoring system for collaborative carbon emission reduction with wind power and photovoltaic power, which is used to implement the digital carbon flow monitoring system for collaborative carbon emission reduction with wind power and photovoltaic power. The construction method includes the following steps:

[0026] Build a front-end UI platform interface to determine the multi-functional requirements of users;

[0027] Develop an SDK layer based on the multi-functional requirements of the users;

[0028] Based on the requirements of connecting the front-end UI platform interface with the back-end service to realize data interaction and function call, design an API layer;

[0029] Based on the requirements of ensuring user security and system access specifications, build an authentication layer;

[0030] Based on the need to improve data processing efficiency, a data layer is constructed, and an operation configuration database for storing and encapsulating complex data is based on the data layer;

[0031] A digital carbon flow monitoring system is obtained based on the front-end UI platform interface, SDK layer, API layer, authentication layer, data layer and database.

[0032] Optionally, the front-end UI platform interface embeds interactive charts, GIS maps and dynamic heat maps to display the dynamic changes of carbon emission sources, carbon sink areas and carbon flow paths in real time, and provides historical emission trends and multi-dimensional data superposition functions.

[0033] Optionally, the SDK layer supports users to input emission reduction targets, set optimization directions, dynamically adjust path planning and parameter selection, and trigger calculations and feedback optimization results in real time through WebSocket technology.

[0034] Optionally, the database adopts distributed deployment and connection pool technology to achieve real-time data update.

[0035] Compared with the prior art, the present invention has the following advantages and technical effects:

[0036] The digital carbon flow monitoring system for the coordination of carbon emissions and wind power and photovoltaic power of the present invention realizes the all-round and efficient monitoring and management of the coordination of carbon emissions and renewable energy through the collaborative work of the carefully designed front-end UI, SDK layer, API layer, authentication layer and data layer. The front-end UI provides an intuitive and easy-to-use operation interface for users, enabling users to conveniently perform operations such as real-time monitoring of carbon emission data, historical trend analysis, emission reduction target setting, and optimization plan selection, greatly improving the user experience and work efficiency. The SDK layer provides strong functional support for the front-end UI, ensuring the efficient implementation of functions such as data visualization, user operation and input, real-time monitoring and warning, and laying a solid foundation for the stable operation of the system. The API layer, as the core bridge connecting the front-end and the background service, realizes the efficient interaction of data and the flexible invocation of functions, supports key functions such as real-time data acquisition, path optimization service, and user demand transmission, and ensures the real-time performance and response speed of the system. The authentication layer ensures the secure access of users and the standardized operation of the system through strict user identity authentication and permission control, effectively preventing risks such as illegal access and data leakage. The data layer adopts advanced storage, management and processing technologies, which can efficiently store, manage and process multi-source data, provide a reliable data basis for the data analysis and decision support of the system, and support the efficient processing and real-time update of massive data. Overall, the system of the present invention has significant advantages such as strong real-time performance, high data processing efficiency, user-friendly interaction, high security, and good scalability, and can provide strong technical support for achieving the carbon neutrality goal and promoting the sustainable development of the low-carbon economy. Brief Description of the Drawings

[0037] The drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0038] Figure 1 is the flowchart for constructing a digital carbon flow monitoring system for collaborative carbon emission reduction and wind power and photovoltaic power generation in an embodiment of the present invention;

[0039] Figure 2 is the digital monitoring platform in an embodiment of the present invention;

[0040] Figure 3 is the schematic diagram of the transportation framework in an embodiment of the present invention. Detailed Description of the Embodiments

[0041] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine with the embodiments to detail this application.

[0042] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0043] Embodiment 1

[0044] The present invention integrates multi-source data such as satellite remote sensing data, enterprise emission monitoring data, and historical records, and relies on the intelligent algorithm of the large model to perform real-time optimization and dynamic planning on the supply, transportation, and consumption of CO 2 . While meeting the emission reduction requirements, this method aims to minimize costs or maximize benefits, efficiently transports carbon sources to carbon sinks or utilization areas, and provides an innovative solution for realizing all-round carbon flow management, promoting low-carbon transformation, and assisting the carbon neutrality goal.

[0045] The present invention proposes the concept of digital carbon flow, and a digital carbon flow monitoring platform for collaborative carbon emission reduction and wind power and photovoltaic power generation based on edge computing, which solves the problems of insufficient real-time performance, weak data integration ability, and lack of collaborative optimization in the existing technology. Through distributed edge computing technology, the platform processes data near the data source, realizes real-time monitoring of carbon emission dynamics, and reduces data transmission costs and privacy risks. The platform integrates multi-source data, fully utilizes the synergistic benefits of renewable energy such as wind power and photovoltaic power generation and carbon flow management, improves carbon emission reduction efficiency, and provides accurate and low-cost technical support for regional carbon neutrality goals.

[0046] The present invention develops a carbon flow monitoring system that can track and analyze carbon emissions and carbon sequestration dynamics in real time and efficiently. By integrating multi-source data such as satellite remote sensing data, enterprise emission monitoring data, and historical emission records, the system will establish a comprehensive carbon flow database to achieve accurate capture and dynamic update of carbon flow information. According to the monitoring platform and the transportation model behind the platform, when the emission reduction requirements can be input, the existing carbon sources or newly generated carbon sources can be transported to carbon sink areas in a way that minimizes costs or maximizes benefits, realizing all-round perception and analysis of carbon flow, meeting emission reduction requirements, providing a scientific basis for policymakers, providing precise management tools for enterprises, and accelerating the realization of low-carbon transformation and carbon neutrality goals. Intelligent carbon emission reduction in energy economics.

[0047] The most important thing in the present invention is the structure of the entire system. System core architecture: An overall monitoring and optimization architecture that integrates the entire process of carbon sources, carbon sinks, and carbon transportation paths, covering the overall design of data collection, storage, processing, optimization, and visual display. Specifically, it includes the systematic design of a multi-source data fusion module, a path optimization calculation module, and a dynamic feedback mechanism. Carbon flow optimization module: A path optimization algorithm module embedded in the platform, including a data input interface, a calculation engine, and a dynamic programming structure tightly coupled with the carbon flow transportation model, ensuring real-time analysis of carbon flow and generation of the optimal path. Front-end interaction interface: Adopting a fixed visualization logic, it displays the real-time dynamics of carbon flow through GIS maps, dynamic heat maps, and charts, combined with a user operation interface, supporting a complete interaction design for functions such as emission reduction target setting, path adjustment, and parameter optimization. Platform management and permission control: Through the account management, organization management, and service management modules in the authentication layer, combined with the Token authentication and hierarchical permission control mechanism, it ensures the secure access of users and the compliance of platform operations.

[0048] The system provides a real-time carbon flow information display and management interface for end-users, supporting the display of charts of the dynamic status of carbon sources and carbon sinks, real-time carbon flow paths, and synergistic benefits. Through this display platform, users can conveniently view and control the optimized paths of carbon flow transportation, and adjust operation strategies based on the decisions recommended by the model. The display platform is closely integrated with the intelligent optimization model to ensure that all carbon flow adjustments and path optimizations can be real-time feedback to users. At the same time, when the emission reduction requirements are input at the input end of the system or the emission reduction requirements required by national policies, the system can automatically calculate the amount of emission reduction for each industry and give specific factories that need to reduce emissions.

[0049] As Figure 1 shown, in this embodiment, a digital carbon flow monitoring system for collaborative emission reduction of carbon emissions and wind power and photovoltaic power is provided. The construction of the digital carbon flow monitoring system is mainly divided into six parts:

[0050] I. Front-end UI:

[0051] The front-end UI will be developed using modern technologies to build an intuitive, user-friendly, and feature-rich interface for the digital carbon flow monitoring platform. The data visualization module will display the dynamic changes of carbon emission sources, carbon sink areas, and carbon flow paths in real time by embedding interactive charts (such as line charts and bar charts), GIS maps, and dynamic heat maps. At the same time, it will provide historical emission trends and multi-dimensional data overlay functions, and conduct comprehensive analysis by combining geographical and transportation network data to provide an analysis interface for users. The user operation and input module allows users to set emission reduction targets and select optimization options (such as minimizing costs or maximizing benefits) through a convenient interface, supports path planning and dynamic regulation, and adjusts key parameters (such as carbon market prices or transportation restrictions) in real time through sliders or input boxes. The real-time monitoring and early warning module will track the enterprise emission data and carbon sequestration status in real time, provide abnormal alarms and risk tips to ensure that the emission reduction targets are implemented as planned. The data interaction and report generation module can automatically generate data analysis reports, support downloading and sharing, and facilitate the decision-making support of enterprise managers, policymakers, and carbon trading market users. The multi-role support module will provide a differentiated functional layout for enterprises, policymakers, and market users from a multi-role perspective to meet the needs of all parties. Finally, the entire UI design focuses on user-friendliness. Through an intuitive layout, interactive buttons, and a novice guide function, it reduces the learning cost and ensures that users at different levels can quickly get started and operate efficiently. The platform is as Figure 2 shown.

[0052] II. SDK layer. The SDK layer is used to provide functional support for the front-end UI:

[0053] (1) Data visualization module: The data visualization module integrates data such as carbon emission sources, carbon sink areas, and carbon flow paths to achieve real-time carbon flow dynamic display, historical trend analysis, and multi-dimensional data overlay functions. The front-end visualization interface is developed using JavaScript, and intuitive charts, GIS maps, and heat maps are displayed by combining ECharts and Mapbox. The rendering performance of large amounts of data is optimized through WebGL. The back-end preprocesses data using Python to ensure real-time loading and dynamic update of data, providing an accurate analysis interface for users.

[0054] (2) User operation and input module: The user operation and input module supports users to input emission reduction targets, set optimization directions, and dynamically adjust path planning and parameter selection. The front-end provides an interactive and friendly interface using JavaScript, supports input verification and parameter prompts; the back-end uses Python and C++ to implement complex optimization algorithm logic and dynamically generate path planning solutions. Through WebSocket technology, users' parameter adjustments can trigger calculations in real time and feedback optimization results to meet flexible requirement settings.

[0055] (3) Real-time Monitoring and Warning Module: The real-time monitoring and warning module tracks the carbon emission and sequestration status in real time through a high-performance service architecture, dynamically detects anomalies and provides warnings. The background monitoring service is implemented using Go or Java, and the anomaly detection rules written in Python are flexibly configured. The front end subscribes to the data stream through WebSocket or SSE technology, updates the carbon emission dynamics of enterprises or regions in real time, and pushes alarm reminders when anomalies occur, providing timely information feedback to users.

[0056] (4) Data Interaction and Report Generation Module: The data interaction and report generation module supports the generation of customized carbon flow monitoring reports, emission reduction effect analysis, and optimization plan documents. Python and jsPDF are used to generate PDF reports, and Apache POI is combined to provide Excel data processing functions. The platform supports the export of data in multiple formats (such as CSV, Excel). Users can securely share data through the permission management system and work collaboratively with enterprises or policymakers conveniently.

[0057] (5) Multi-role Support Module: The multi-role support module provides tailored functions for different users. Enterprise managers can view emission situations, cost analysis, and carbon resource flows; policymakers can analyze national or regional carbon flow dynamics to provide a basis for policy decisions; carbon trading market users can monitor carbon trading prices and flow data in real time. Through the front-end interface dynamically designed by JavaScript and the permission management system implemented by back-end Java or Go, the platform can dynamically load role-related content and ensure data isolation and security through JWT or Session tokens.

[0058] III. API Layer. The API layer is the core bridge of the carbon flow monitoring platform, responsible for connecting the front end and the background service to achieve data interaction and function calls. Its functions include real-time data acquisition, path optimization services, user demand transmission, and visualization data interfaces. Through RESTful API or GraphQL, the API layer provides efficient and flexible data query and transmission, supporting the front end to display carbon emission dynamics, carbon flow paths, and historical trend analysis in real time. To ensure the transmission efficiency and security of large-scale data, the API layer improves the response speed through a caching mechanism (such as Redis), and combines HTTPS and authentication tokens (JWT) to ensure the security of data transmission.

[0059] In terms of implementation means, the API layer is developed in Python or Java and uses frameworks (such as FastAPI or Spring Boot) to build high-performance interface services. The data interface is connected to the backend databases (such as PostgreSQL and MongoDB) and the carbon flow optimization algorithm module, supporting the dynamic adjustment of path planning and emission reduction targets. The API layer also integrates WebSocket to provide real-time push functions for the instant transmission of monitoring and warning information, ensuring users' all-round perception and management of the carbon flow.

[0060] IV. Authentication layer. The authentication layer plays an important role in ensuring user security and system access norms in the carbon flow monitoring platform. Its main functions include account management, application management, service management, organization management, and authentication management.

[0061] The account management module realizes user authentication through OAuth 2.0 or JWT, supporting functions such as user registration, login, password recovery, and multi-factor authentication (MFA) to ensure account security. Through single sign-on (SSO) and third-party login (such as enterprise authentication platforms or social login), users can conveniently access the platform. All user information is stored using encryption technologies (such as bcrypt) to ensure data security and privacy.

[0062] The application management and service management modules realize refined management of user functions through role-based access control (RBAC), ensuring that different roles (such as enterprise managers, policy makers, and carbon trading market users) can only access their corresponding function modules and data. The service management uses microservice architecture and containerization technologies (such as Docker and Kubernetes) to provide highly available services such as carbon flow path optimization, real-time monitoring, and data analysis, supporting the elastic expansion of the platform.

[0063] The organization management module supports a multi-tenant architecture and can provide isolated operating environments for enterprises, institutions, and carbon trading users. Users can manage their internal organizational structures, department permissions, and cross-organizational carbon flow data sharing and collaboration. Through a flexible resource allocation and permission configuration mechanism, it ensures that each organization can use the platform independently and securely.

[0064] The authentication management module is responsible for the identity authentication and authorization management of users and services. It realizes identity authentication based on JWT tokens, supports the token refresh mechanism and access session management, provides dynamic permission control and real-time security policy updates. The authentication management also integrates real-time logging and anomaly detection mechanisms to ensure user access compliance and provide traceable security audit records for platform administrators.

[0065] Through the collaborative work of these functional modules, the authentication layer provides strong security guarantees for the carbon flow monitoring platform, supports users in precisely managing carbon emission and emission reduction targets, and promotes the realization of low-carbon transformation and carbon neutrality goals.

[0066] V. Data Layer, improving the processing efficiency of carbon flow data. The carbon flow monitoring platform encapsulates complex database operations through stored procedures. Based on PostgreSQL, the platform writes stored procedures for batch updates, complex queries, and data integration operations, such as merging carbon emission source data, matching carbon sink resources, and generating an initial dataset for path optimization. In addition, the trigger mechanism is used for automated data updates to ensure the dynamic consistency of carbon flow information under multi-source input.

[0067] Data Caching To accelerate high-frequency data queries and improve the platform's response speed, the platform uses Redis as an in-memory caching system. High-frequency data query results (such as carbon emission dynamic data, optimized paths, and historical analysis results) are cached in memory, and the cache expiration policy is used to maintain the timeliness and accuracy of the data. This mechanism effectively reduces the database load and ensures that users can obtain carbon flow dynamics and optimization results in real time.

[0068] Objective Function Calculation The carbon flow platform realizes path optimization and emission reduction strategy support through objective function calculation, including minimizing transportation costs, maximizing carbon sequestration, transportation distance limits, unit emission reduction cost limits, etc., or optimizing multi-objective combinations. The high-performance objective function module is implemented in C++ and integrated with the platform through a Python encapsulation interface. This module directly reads carbon emission source, carbon sink location, and transportation network data from the database and uses heuristic algorithms (such as genetic algorithms or simulated annealing algorithms) to dynamically optimize the path and generate the optimal transportation plan.

[0069] Transaction Management To ensure the atomicity, consistency, isolation, and durability (ACID) of data operations, the platform introduces a transaction management mechanism. Through the transaction support of PostgreSQL, operations such as path planning adjustments, user input updates, and carbon emission record insertions are processed to avoid data conflicts or errors under multi-user concurrent access. In addition, transaction logs record all operation histories to ensure the reliability of fault recovery and data auditing.

[0070] Reading and Writing Databases The carbon flow monitoring platform adopts a hybrid database architecture to support efficient data reading and writing operations. PostgreSQL is responsible for storing structured data (such as carbon emission records and carbon flow paths), while MongoDB is used to store unstructured data (such as GIS maps and historical chart data). Combining partitioning and indexing to optimize query performance, a database connection pool (such as HikariCP) provides high-concurrency support to ensure efficient data processing in scenarios such as multi-dimensional data superposition, historical analysis, and real-time monitoring.

[0071] VI. Database. The database of the carbon flow monitoring platform is mainly responsible for storing, managing, and processing multi-source data to support the core functions of carbon emissions and carbon flow monitoring. The database stores structured data such as enterprise carbon emission records, carbon sink locations, and transportation network information, as well as unstructured data such as GIS maps and dynamic heat maps, providing reliable data support for real-time monitoring, historical trend analysis, and route optimization. Through the transaction management function, the platform realizes the security and consistency of multi-user operations, and the log records ensure the traceability of data and the disaster tolerance ability of the system.

[0072] The database is implemented using a hybrid architecture, combining two types of data storage: PostgreSQL and MongoDB. PostgreSQL is responsible for the efficient management of structured data, supporting complex queries, index optimization, and transaction processing; MongoDB, on the other hand, handles unstructured and semi-structured data, ensuring the fast writing and retrieval of dynamic and multi-dimensional information. At the same time, Redis is used as an in-memory cache layer to accelerate high-frequency data access and improve the platform's response speed. Through distributed deployment and connection pool technology (such as HikariCP), the database system achieves high concurrency performance and real-time data updates, meeting the high-efficiency processing requirements of the carbon flow platform for massive data.

[0073] Embodiment 2

[0074] A construction method for a digital carbon flow monitoring system for collaborative carbon emission reduction with wind power and photovoltaic power generation includes the following steps: constructing a front-end UI platform interface to determine the multi-functional requirements of users; developing an SDK layer based on the multi-functional requirements of users; designing an API layer based on the need to connect the front-end UI platform interface with the back-end service to achieve data interaction and function call; constructing an authentication layer based on the need to ensure user security and system access specifications; constructing a data layer based on the need to improve data processing efficiency, and storing and encapsulating the operation configuration database of complex data based on the data layer; obtaining a digital carbon flow monitoring system based on the front-end UI platform interface, SDK layer, API layer, authentication layer, data layer, and database.

[0075] As a specific implementation manner of this embodiment, the specific implementation process of the construction method of the digital carbon flow monitoring system includes:

[0076] I. Front-end UI:

[0077] The front - end UI will be designed and developed using modern technologies to build an intuitive, user - friendly, and feature - rich platform interface. The data visualization module will display the dynamic changes of carbon emission sources, carbon sink areas, and carbon flow paths in real - time by embedding interactive charts (such as line charts, bar charts), GIS maps, and dynamic heat maps. At the same time, it provides historical emission trends and multi - dimensional data overlay functions, conducts comprehensive analysis by combining geographical and transportation network data, and provides an analysis interface for users. The user operation and input module allows users to set emission reduction targets, select optimization options (such as minimizing costs or maximizing benefits) through a convenient interface, supports path planning and dynamic regulation, and can adjust key parameters (such as carbon market prices or transportation restrictions) in real - time through sliders or input boxes. The real - time monitoring and early - warning module will track enterprise emission data and carbon sequestration status in real - time, provide abnormal alarms and risk prompts, and ensure that the emission reduction targets are implemented as planned. The data interaction and report generation module can automatically generate data analysis reports, support downloading and sharing, and facilitate decision - making support for enterprise managers, policymakers, and carbon trading market users. The multi - role support module will provide a differentiated functional layout for enterprises, policymakers, and market users from the perspectives of multiple roles, meeting the needs of all parties. Finally, the entire UI design focuses on user - friendliness. Through an intuitive layout, interactive buttons, and a novice guidance function, it reduces the learning cost and ensures that users at different levels can quickly get started and operate efficiently.

[0078] II. SDK layer. The SDK layer can achieve the following functions:

[0079] (1) Data visualization module: The data visualization module integrates data such as carbon emission sources, carbon sink areas, and carbon flow paths to achieve real - time carbon flow dynamic display, historical trend analysis, and multi - dimensional data overlay functions. It uses JavaScript to develop a front - end visualization interface, combines ECharts and Mapbox to achieve intuitive chart, GIS map, and heat map displays, and optimizes the rendering performance of large amounts of data through WebGL. The back - end uses Python to pre - process data to ensure real - time data loading and dynamic updates, providing users with an accurate analysis interface.

[0080] (2) User operation and input module: The user operation and input module supports users to input emission reduction targets, set optimization directions, and dynamically adjust path planning and parameter selection. The front - end provides an interactive - friendly interface through JavaScript, supports input validation and parameter prompts; the back - end uses Python and C++ to implement complex optimization algorithm logic and dynamically generates path planning solutions. Through WebSocket technology, users' parameter adjustments can trigger calculations in real - time and feedback optimization results to meet flexible requirement settings.

[0081] (3) Real-time Monitoring and Warning Module: The real-time monitoring and warning module uses a high-performance service architecture to track the carbon emission and sequestration status in real time, dynamically detect anomalies and provide warnings. The background monitoring service is implemented using Go or Java, and the anomaly detection rules written in Python are flexibly configured. The front end subscribes to the data stream through WebSocket or SSE technology, updates the carbon emission dynamics of enterprises or regions in real time, and pushes alarm reminders when anomalies occur, providing timely information feedback to users.

[0082] (4) Data Interaction and Report Generation Module: The data interaction and report generation module supports the generation of customized carbon flow monitoring reports, emission reduction effect analysis, and optimization plan documents. Python and jsPDF are used to generate PDF reports, and ApachePOI is combined to provide Excel data processing functions. The platform supports the export of data in multiple formats (such as CSV, Excel). Users can securely share data through the permission management system and work collaboratively with enterprises or policymakers conveniently.

[0083] (5) Multi-role Support Module: The multi-role support module provides tailored functions for different users. Enterprise managers can view the emission situation, cost analysis, and carbon resource flow; policymakers can analyze the national or regional carbon flow dynamics to provide a basis for policy decisions; carbon trading market users can monitor the carbon trading price and flow data in real time. Through the front-end interface dynamically designed by JavaScript and the permission management system implemented by the back-end Java or Go, the platform can dynamically load role-related content and ensure data isolation and security through JWT or Session tokens.

[0084] III. API Layer. The API layer is the core bridge of the carbon flow monitoring platform, responsible for connecting the front end and the background service to achieve data interaction and function calls. Its functions include real-time data acquisition, path optimization services, user demand transmission, and visualization data interfaces. Through RESTful API or GraphQL, the API layer provides efficient and flexible data query and transmission, supporting the front end to display carbon emission dynamics, carbon flow paths, and historical trend analysis in real time. To ensure the transmission efficiency and security of large-scale data, the API layer improves the response speed through a caching mechanism (such as Redis), and combines HTTPS and authentication tokens (JWT) to ensure the security of data transmission.

[0085] In terms of implementation means, the API layer is developed through Python or Java, and a high-performance interface service is built using frameworks such as FastAPI or Spring Boot. The data interface is connected to the backend databases (such as PostgreSQL and MongoDB) and the carbon flow optimization algorithm module, supporting dynamic adjustment of path planning and emission reduction targets. The API layer also integrates WebSocket to provide real-time push functionality for instant transmission of monitoring and warning information, ensuring users' all-round perception and management of carbon flow.

[0086] As a specific implementation manner of this embodiment, the process by which the API layer supports the dynamic adjustment of path planning and emission reduction targets through real-time data acquisition, path optimization services, user demand transmission, and visual data interfaces includes:

[0087] S1. Obtain the CO 2 emission source locations and emission amounts based on the longitude, latitude, and production volume of the emitting enterprises;

[0088] S2. Evaluate the CO 2 capture cost based on the CO 2 emission amount and emission coefficient;

[0089] S3. Combine with wind power, solid waste collaborative emission reduction according to the CO 2 capture cost to determine the CO 2 utilization locations and storage locations; Further, determining the CO 2 utilization locations and storage locations includes: Based on the evaluation of the longitude, latitude, and emission reduction potential of each emitting enterprise, combined with the emission reduction potential of wind power, photovoltaic electrolysis of water to produce hydrogen and CO 2 to produce green methanol, determine the CO 2 utilization locations and storage locations;

[0090] S4. Evaluate the CO 2 utilization and storage costs based on the CO 2 utilization locations and storage locations; Further, evaluating the CO 2 utilization and storage costs includes: Based on the CO 2 utilization locations and storage locations, evaluate the wind power and photovoltaic potential to determine the CO 2 emission reduction potential of producing green methanol; Based on the evaluation results of the wind power and photovoltaic potential, evaluate the potential of steel slag to solidify CO 2 ; Based on the evaluation results of the potential of steel slag to solidify CO 2 , evaluate the storage potential and storage cost;

[0091] S5. Evaluate the transportation locations, transportation feasibility, and transportation cost based on the CO 2 capture cost and the CO 2 utilization and storage costs, and construct a transportation model;

[0092] Specifically, the assessment of transportation routes, transportation feasibility, and transportation costs:

[0093] The influencing factors of pipeline construction include social, geographical, and geological factors. Based on this principle, the impacts of the above factors on pipeline construction are quantitatively analyzed respectively to lay a foundation for subsequent pipeline route selection. The research framework of this embodiment is shown in the following figure, and the main research ideas are as follows: ① First, obtain the detailed factors of geography, geology, and society that affect pipeline construction, mainly including geographical spatial data of rivers, lakes, reservoirs, railways, highways, nature reserves, ecological function areas, seismic zones, terrain undulations, urban and rural settlements; ② Perform standard processing and conversion of data types to uniformly convert them into raster layer data with a unit size of 1 km × 1 km in precision; ③ Reclassify the raster layer, and quantify the costs of influencing factors according to the specific gravity of the impacts of different influencing factors on pipeline construction costs in existing research to obtain cost raster layers of different influencing factors; ④ Perform map algebra operations, and mathematically sum and overlay the cost raster layers of different influencing factors to obtain a comprehensive influencing factor cost raster layer. As Figure 3 shown, the schematic diagram of the transportation framework.

[0094] S6. According to the transportation model, optimize the transportation path to achieve CO 2 transportation and emission reduction. Further, according to the transportation model, optimizing the transportation path includes: determining the difference between the total costs of capture, transportation, and storage and the carbon storage benefits of oil and gas fields, non - mineable coal seams, and the production of green methanol; according to the principle of cost minimization, optimize the CO 2 capture, transportation, and storage processes, and satisfy the capture and storage constraints, transportation constraints, and CCUS target constraints.

[0095] Specifically, the transportation model is:

[0096] The total cost is the minimum of the difference (net cost) between the total costs of capture, transportation, and storage and the carbon storage benefits of oil and gas fields, non - mineable coal seams, and the production of green methanol.

[0097] Objective function:

[0098]

[0099] Constraint conditions;

[0100] Capture and storage constraints: The CO 2 capture volume of any emission source is not higher than its CO 2 annual emission volume, and the CO 2 quantity stored in any storage site within the planning period is not higher than its basin CO 2 total storage potential, and the injection well of any storage site CO 2The injection volume shall not be less than the annual sequestration volume of the basin.

[0101]

[0102] Transportation constraint: The transportation capacity of any pipeline shall not be less than the transportation volume to be borne, and the input and output volumes of any emission source or sequestration site node shall maintain a zero balance.

[0103]

[0104]

[0105] CCUS target constraint for capture and sequestration: The total capture volume in the coal-fired power industry is equal to the CCUS emission reduction target, and the total sequestration volume is equal to the total capture volume in the coal-fired power industry.

[0106] Σ i∈S a i -Amount_CCUS = 0 (8)

[0107] ∑ j∈R b j -Amount_CCUS = 0 (9)

[0108] Supply-demand constraint: The coal-fired power production is equal to the power supply target to be borne.

[0109] ∑ i∈S t i ·power i -Power_generation = 0 (10)

[0110] Non-negativity constraint: Coal-fired power generation hours constraint. The total capture volumes in the coal-fired power, iron and steel, and cement industries are non-negative, the injection volume of any sequestration site is non-negative, and the transportation volume of any pipeline is non-negative.

[0111]

[0112] Model parameters and decision variables;

[0113] The parameters in the pipeline network optimization model are divided into variables and parameters. Table 1 lists the model parameters and decision variables.

[0114] Table 1

[0115]

[0116]

[0117]

[0118] IV. Authentication Layer The authentication layer plays a crucial role in ensuring user security and system access norms in the carbon flow monitoring system. Its main functions include account management, application management, service management, organization management, and authentication management.

[0119] The account management module implements user authentication through OAuth 2.0 or JWT, supporting functions such as user registration, login, password recovery, and multi-factor authentication (MFA) to ensure account security. Through single sign-on (SSO) and third-party login (such as enterprise authentication platforms or social login), users can conveniently access the platform. All user information is stored using encryption technologies (such as bcrypt) to ensure data security and privacy.

[0120] The application management and service management modules achieve refined management of user functions through role-based access control (RBAC), ensuring that different roles (such as enterprise managers, policy makers, and carbon trading market users) can only access their corresponding function modules and data. Service management uses microservice architecture and containerization technologies (such as Docker and Kubernetes) to provide highly available services such as carbon flow path optimization, real-time monitoring, and data analysis, supporting the elastic expansion of the platform.

[0121] The organization management module supports a multi-tenant architecture and can provide isolated operating environments for enterprises, institutions, and carbon trading users. Users can manage their internal organizational structures, department permissions, and cross-organizational carbon flow data sharing and collaboration. Through a flexible resource allocation and permission configuration mechanism, it ensures that each organization can use the platform independently and securely.

[0122] The authentication management module is responsible for the identity authentication and authorization management of users and services. It implements identity authentication based on JWT tokens, supports token refresh mechanisms and access session management, provides dynamic permission control and real-time security policy updates. Authentication management also integrates real-time logging and anomaly detection mechanisms to ensure user access compliance and provide traceable security audit records for platform administrators.

[0123] Through the collaborative work of these functional modules, the authentication layer provides strong security protection for the carbon flow monitoring platform, supports users to accurately manage carbon emission and emission reduction targets, and promotes the realization of low-carbon transformation and carbon neutrality goals.

[0124] V. Data Layer To improve the processing efficiency of carbon flow data, the carbon flow monitoring platform encapsulates complex database operations through stored procedures. Based on PostgreSQL, the platform writes stored procedures for batch updates, complex queries, and data integration operations, such as merging carbon emission source data, matching carbon sink resources, and generating an initial dataset for path optimization. In addition, the trigger mechanism is used for automated data updates to ensure the dynamic consistency of carbon flow information under multi-source input.

[0125] Data Caching To accelerate high-frequency data queries and improve the platform's response speed, the platform uses Redis as an in-memory caching system. The results of high-frequency data queries (such as carbon emission dynamic data, optimized paths, and historical analysis results) are cached in memory, and a cache expiration policy is used to maintain the real-time nature and accuracy of the data. This mechanism effectively reduces the database load and ensures that users can obtain carbon flow dynamics and optimization results in real time.

[0126] Objective Function Calculation The carbon flow platform realizes path optimization and emission reduction strategy support through objective function calculation, including minimizing transportation costs, maximizing carbon sequestration, transportation distance limits, unit emission reduction cost limits, etc., or optimizing multi-objective combinations. The high-performance objective function module is implemented in C++ and integrated with the platform through Python wrapper interfaces. This module directly reads carbon emission sources, carbon sink locations, and transportation network data from the database and uses heuristic algorithms (such as genetic algorithms or simulated annealing algorithms) to dynamically optimize paths and generate optimal transportation plans.

[0127] Transaction Management To ensure the atomicity, consistency, isolation, and durability (ACID) of data operations, the platform introduces a transaction management mechanism. Through the transaction support of PostgreSQL, operations such as path planning adjustments, user input updates, and carbon emission record insertions are processed to avoid data conflicts or errors under multi-user concurrent access. In addition, transaction logs record all operation histories to ensure the reliability of fault recovery and data auditing.

[0128] Reading and Writing Databases The carbon flow monitoring platform adopts a hybrid database architecture to support efficient data reading and writing operations. PostgreSQL is responsible for storing structured data (such as carbon emission records and carbon flow paths), while MongoDB is used to store unstructured data (such as GIS maps and historical chart data). Combining partitioning and indexing to optimize query performance, a database connection pool (such as HikariCP) provides high-concurrency support to ensure efficient data processing in scenarios such as multi-dimensional data superposition, historical analysis, and real-time monitoring.

[0129] VI. Database The database of the carbon flow monitoring platform is mainly responsible for storing, managing, and processing multi-source data to support the core functions of carbon emission and carbon flow monitoring. The database stores structured data such as enterprise carbon emission records, carbon sink locations, and transportation network information, as well as unstructured data such as GIS maps and dynamic heat maps, providing reliable data support for real-time monitoring, historical trend analysis, and path optimization. Through the transaction management function, the platform realizes the security and consistency of multi-user operations, and the log records ensure the traceability of data and the disaster tolerance of the system.

[0130] The database is implemented using a hybrid architecture, combining two types of data storage: PostgreSQL and MongoDB. PostgreSQL is responsible for the efficient management of structured data, supporting complex queries, index optimization, and transaction processing; MongoDB processes unstructured and semi-structured data, ensuring fast writing and retrieval of dynamic and multi-dimensional information. At the same time, Redis is used as a memory cache layer to accelerate high-frequency data access and improve the platform's response speed. Through distributed deployment and connection pool technology (such as HikariCP), the database system achieves high concurrency performance and real-time data updates, meeting the Carbon Flow platform's needs for efficient processing of massive data.

[0131] This embodiment builds a digital carbon flow monitoring system by integrating multi-source data and combining the underlying transportation model, so that the digital carbon flow monitoring system can dynamically plan the optimal path plan for carbon flow, realize all-round, full-system monitoring and efficient management of the carbon supply chain, open up all links of carbon flow, promote the precise matching of carbon emissions and carbon storage, and promote the realization of low-carbon transformation and carbon neutrality goals.

[0132] In terms of carbon flow optimization, the present invention uses an intelligent optimization model to dynamically plan the transportation path from carbon source to carbon sink, thereby maximizing the economy and efficiency of carbon flow management. Unlike the separation of carbon management and new energy utilization in traditional technologies, this embodiment fully considers the volatility of renewable energy such as wind power and photovoltaics, and combines it with the optimization of carbon emission transportation paths. For example, by adjusting the transportation path in real time, renewable energy is preferentially used to assist in the storage or conversion of carbon emissions, thereby significantly improving the synergistic benefits of carbon flow.

[0133] The present invention also builds a comprehensive and dynamic carbon flow database by integrating multi-source data such as satellite remote sensing, enterprise emission monitoring and new energy power generation, making up for the deficiencies of existing technologies in data integration and dynamic updating. The platform relies on the carbon transportation planning model, fully considers the relationship between the volatility of renewable energy such as wind power and photovoltaic power and the dynamics of carbon emissions, optimizes the transportation path from carbon source to carbon sink, and maximizes the synergistic benefits of carbon emission management and renewable energy utilization.

[0134] The present invention proposes the concept of digital carbon flow, with the goal of providing policy makers with accurate dynamic analysis tools for carbon emissions, providing enterprises with efficient carbon management methods, and promoting the realization of low-carbon transformation and carbon neutrality goals. By overcoming the shortcomings of existing technologies, the present invention can achieve all-round and dynamic perception of carbon flow information, improve the intelligence and economy of carbon emission monitoring, and provide strong technical support for addressing climate change.

[0135] In addition, this solution also has obvious advantages in terms of privacy protection and operating costs. By offloading cloud tasks through edge computing, the need for large-scale data transmission is reduced, and the risks of data leakage and bandwidth costs are minimized. At the same time, the distributed processing architecture enhances the fault tolerance and stability of the system, enabling the platform to more flexibly adapt to regional carbon management requirements. Overall, the technical solution of this application demonstrates significant advantages in terms of real-time performance, data integration capabilities, synergy benefits, and security, providing important technical support for the achievement of the carbon neutrality goal.

[0136] The above are only the preferred specific embodiments of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A digital carbon flow monitoring system for carbon emission and wind power photovoltaic coordinated emission reduction, characterized in that: include: Front-end UI, SDK layer, API layer, authentication layer and data layer; Wherein, the front-end UI is used to construct the digital carbon flow monitoring platform interface; The SDK layer is used to provide functional support for the front-end UI; The API layer is used to connect the front-end and back-end services to achieve data interaction and function calls; The authentication layer is used to ensure user security and system access specifications; The data layer is used to store, manage and process multi-source data.

2. The system according to claim 1, characterized in that The front-end UI includes: data visualization module, user operation and input module, real-time monitoring and early warning module, data interaction and report generation module, and multi-role support module; Wherein, the data visualization module is used to provide users with an analysis interface based on embedded interactive charts, GIS maps and dynamic heat maps; The user operation and input module is used for the user to input emission reduction targets, set optimization directions, and dynamically adjust path planning and parameter selection; The real-time monitoring and early warning module is used to track carbon emissions and storage status in real time, dynamically detect anomalies and provide early warnings; The data interaction and report generation module is used to generate customized carbon flow monitoring reports, emission reduction effect analysis and optimization solution documents; The multi-role support module is used to provide personalized functions for different users.

3. The system according to claim 1, characterized in that The authentication layer includes: an account management module, an application management module, a service management module, an organization management module and an authentication management module; The account management module is used for user identity authentication; The application management module is used to manage users in detail, ensuring that different user roles can only access the functional data corresponding to the role; The service management module is used to provide carbon flow path optimization services; The organization management module is used to support a multi-tenant architecture and provide an isolated operating environment for enterprises, institutions and carbon trading users; The authentication management module is used for identity authentication and authorization management of users and services.

4. The system according to claim 1, characterized in that The data layer adopts a hybrid database architecture, including PostgreSQL and MongoDB; Among them, PostgreSQL is used to store structured data; MongoDB is used to store unstructured data.

5. A method for constructing a digital carbon flow monitoring system for carbon emission and wind power photovoltaic coordinated emission reduction, characterized in that: A digital carbon flow monitoring system for implementing the carbon emission and wind power photovoltaic coordinated emission reduction according to any one of claims 1 to 4, wherein the construction method comprises the following steps: Build the front-end UI platform interface to determine the user's multi-functional needs; Develop an SDK layer based on the user's multi-functional requirements; Based on the requirements of connecting the front-end UI platform interface and the back-end service to realize data interaction and function call, the API layer is designed; Based on the needs of ensuring user security and system access specifications, an authentication layer is built; Based on the need to improve data processing efficiency, build a data layer, and configure the database based on the data layer to store and encapsulate complex data; A digital carbon flow monitoring system is obtained based on the front-end UI platform interface, SDK layer, API layer, authentication layer, data layer and database.

6. The method according to claim 5, characterized in that The front-end UI platform interface displays the dynamic changes of carbon emission sources, carbon sink areas and carbon flow paths in real time by embedding interactive charts, GIS maps and dynamic heat maps, and provides historical emission trends and multi-dimensional data overlay functions.

7. The method according to claim 5, characterized in that The SDK layer supports users to input emission reduction targets, set optimization directions, dynamically adjust path planning and parameter selection, and trigger calculations and feedback optimization results in real time through WebSocket technology.

8. The method according to claim 5, characterized in that The database adopts distributed deployment and connection pool technology to achieve real-time data update.

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