An account management system based on green data assets

Abstract

The present invention discloses an account management system based on green data assets, and relates to the technical field related to green data assets, including an authentication and evaluation system, a smart contract module, which uses blockchain technology to establish a smart contract, executes data ownership transfer, data transaction operations, confirms the rights of data assets through smart contracts, a data asset financial module, a digital energy carbon integrated machine module, a data life cycle management module, a server terminal, and a user terminal. The smart contract module of the present invention can bring multi-dimensional value enhancement to enterprises in terms of system security, reliability, fairness, and efficiency. The smart contract runs on a decentralized blockchain network, avoiding the risk of single point failure and ensuring the overall security of the system. The smart contract is automatically executed through preset rules, which improves the overall operating efficiency and reliability of the system. All transaction records are open to all participants, ensuring the transparency and fairness of the transaction.

Description

Technical Field

[0001] The present invention relates to the technical field related to green data assets, and in particular to an account management system based on green data assets. Background Art

[0002] As the world's awareness of environmental protection and sustainable development continues to deepen, companies and organizations are facing increasing pressure to effectively manage their environmental impact. In this context, the concept of green data assets has emerged. It refers to various types of data related to environment, society and governance (ESG). These data are of great value in promoting enterprises to achieve low-carbon development, improve resource utilization efficiency and meet environmental regulations. However, traditional data management systems have many deficiencies in processing and analyzing such green data assets, and cannot fully meet the needs of modern enterprises for environmental management and strategic decision support.

[0003] On the one hand, the traditional green data asset account management system requires manual intervention during transactions, which not only leads to the possibility of human error and difficulty in ensuring reliability, but also has relatively low efficiency in manual operations. The trading platform provides opportunities for centralized profit-making entities to extract transaction value, making it difficult to be open to all participants and ensuring the transparency and fairness of transactions. Participants have poor trust in the system. On the other hand, it is impossible to collect a company’s carbon emissions and energy usage data in real time, making it impossible to judge the company’s economic benefits, social image, and long-term sustainable development. It is difficult for companies to identify and improve links with high energy consumption and large emissions, resulting in unbalanced resource allocation and low resource utilization, making it difficult to achieve carbon reduction.

[0004] Therefore, it is necessary to propose an account management system based on green data assets to solve the above problems. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides an account management system based on green data assets, which solves the problems that the existing green data asset account management system is prone to human errors, has low efficiency, cannot ensure the transparency and fairness of transactions, cannot make judgments on the economic benefits, social image and long-term sustainable development of the enterprise, has low enterprise resource utilization, and is difficult to achieve carbon reduction.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An account management system based on green data assets, the account management system comprising:

[0010] Certification and evaluation system: Establish data asset evaluation standards, conduct regular performance evaluations of account management systems, and display their certification levels to users;

[0011] Smart contract module: Use blockchain technology to establish smart contracts, automatically execute data ownership transfer and data transaction operations, improve operational efficiency and reduce human errors, confirm data assets through smart contracts, ensure data security and ownership, and facilitate data circulation and transactions;

[0012] Data asset finance module: responsible for data asset credit, data trust services, data asset securitization, data insurance pricing, and data asset value assessment. Enterprises use data assets as collateral to obtain credit funds, solve corporate financing problems, promote enterprises to further develop data trust services, and ensure the safe storage and compliant use of data;

[0013] Digital energy-carbon integrated machine module: provides enterprises with comprehensive solutions including data monitoring, energy consumption management suggestions and carbon asset development guidance, helping enterprises achieve green and low-carbon development;

[0014] Data lifecycle management module: automatically adjusts the data storage location and format according to the data access frequency and importance parameters, optimizes data access efficiency, and reduces the resource occupation of invalid data;

[0015] Server terminal: used to obtain data from the certification and evaluation system, smart contract module, data asset finance module, and digital energy and carbon integrated machine module, and transmit the data to the user terminal;

[0016] User terminal: Receive data transmitted by the server terminal through the Web platform or mobile application; obtain the certification level in the certification and evaluation system; obtain the execution status and results of the smart contract, and users can query specific data or transaction records as needed; obtain information and operation interfaces of data asset financial innovation products, and users can choose financial products that suit them and query transaction records as needed; obtain relevant data and analysis results on carbon emissions and energy management, and users can query and download relevant reports as needed.

[0017] Optionally, the certification and evaluation system includes a data collection and analysis unit, an environmental benefit indicator system, a green asset identification and value assessment unit, a green rating system, and a feedback and improvement mechanism;

[0018] The data collection and analysis unit is used to collect data on system energy consumption, resource usage and environmental impact, and use statistical analysis methods to evaluate the validity and representativeness of the data; the environmental benefit indicator system is used to establish environmental benefit indicators based on the ecological environment, and the indicators include green operating income, green capital expenditure and green operating cost ratio; the green asset identification and value assessment unit adopts a unified principle to identify the direct or indirect environmental benefits generated in the production, operation and financing stages, and uses financial report data to calculate the green operating income ratio, green capital expenditure ratio, green operating cost ratio, green profit ratio, green fixed asset ratio and green investment ratio; the green rating system conducts green rating of the system based on environmental benefit indicators and green asset values ​​to obtain a comprehensive green rating score, and the rating results are used for market communication and regulatory reporting; the feedback and improvement mechanism is used to regularly review and adjust the assessment system to adapt to new environmental standards and technological developments.

[0019] Optionally, the smart contract module includes a data collection layer, a data processing layer, a data storage layer, and a smart contract execution layer; the data collection layer collects data from IoT devices in real time through a blockchain network to ensure the comprehensiveness and real-time nature of the data, and the data includes device status and operation records; the data processing layer is used to clean, organize and analyze the data collected by the data collection layer, including data verification, error detection, missing value processing, and preliminary statistical analysis to provide a basis for subsequent smart contract execution; the data storage layer uses blockchain as a database management system to store processed data to ensure that the data cannot be tampered with and is secure, and to provide data support for upper-level applications; in the smart contract execution layer, smart contracts are automatically executed according to preset rules to perform data ownership transfer and data transaction operations, and the bytecode of the smart contract is executed through EVM (Ethereum Virtual Machine) to ensure that the results of running in each node of the blockchain network are the same.

[0020] Optionally, the mathematical model of the preset rules of the smart contract execution layer is as follows:

[0021] Determinism of smart contract execution: f(d1,d2,...,di)=r, where f represents the smart contract function, di represents the i-th input data, and r is the execution result. For the same input, the smart contract produces the same output on all nodes to ensure execution consistency;

[0022] Asset confirmation and transaction rules: O=H(D), where O represents the ownership of the asset, H is the hash function, and D is the device data or digital asset data. The hash function is used to associate data with ownership to ensure the tamper-proof nature of asset confirmation.

[0023] Data security and privacy protection rules: S=E(M,k), where S represents encrypted data, E is the encryption function, M is the original data, and k is the encryption key. Encryption technology is used to protect the security and privacy of data during transmission and storage.

[0024] Economic model of smart contract: C=T×(P buy −P sell )−F, where C represents the total cost of smart contract transactions, T is the number of transactions, and P buy represents the purchase price, P sell represents the sales price, and F is the transaction fee, which is used to calculate the economic benefits of smart contracts in financial transactions.

[0025] Optionally, the data asset financial module includes a data collection layer, a data processing layer, a data storage layer, a data analysis and evaluation layer, and a financial application layer; the data collection layer collects the green data of the enterprise in real time through the API interface and Internet of Things equipment technology, and the green data includes energy consumption data, carbon emission data, and environmental monitoring data; the data processing layer cleans, organizes and analyzes the collected data, including data verification, error detection, missing value processing, and conducts preliminary statistical analysis (calculating data mean and variance) to provide basic data support for subsequent financial innovation; the data storage layer uses a database management system to store the processed data, and at the same time applies blockchain technology to ensure that the data cannot be tampered with, providing safe and reliable data support for upper-level applications; the data analysis and evaluation layer uses big data analysis and machine learning technology to conduct in-depth data mining and evaluate the value of data assets (for example, calculating the expected return and risk of data assets through a data asset evaluation model) to provide a basis for financial product design; the financial application layer designs and implements various financial innovation products based on the data analysis results, and the products include data asset credit, data trust, data asset securitization, data insurance pricing, and data asset value evaluation.

[0026] Optionally, the digital energy-carbon integrated machine module includes a data acquisition layer, a data processing layer, a data storage layer, a data analysis layer, and a user interaction layer;

[0027] The data collection layer collects the energy use data and carbon emission data of enterprises in real time through Internet of Things devices and sensor technology, realizes automatic perception and monitoring, and meets the energy consumption and carbon emission monitoring needs of enterprise energy use scenarios; the data processing layer cleans, organizes and analyzes the collected data, and the system flexibly configures carbon management policies, formulas and templates to automatically complete carbon emission calculations, while supporting personalized calculation logic and parameters; the data storage layer uses a database management system to store the processed data, uses blockchain technology to ensure that the data cannot be tampered with, provides safe and reliable data support for upper-level applications, and realizes safe storage and efficient access to data; the data analysis layer uses a carbon emission calculation model, an energy consumption and carbon emission relationship model, a carbon emission reduction benefit evaluation model, an energy use efficiency model, and a carbon emission intensity model to calculate carbon emissions, the relationship between energy consumption and carbon emissions, carbon emission reduction benefits, energy use efficiency, and carbon emission intensity, and evaluates the carbon emission status and energy use efficiency of the enterprise; the user interaction layer displays the relevant data and analysis results of carbon emissions and energy management through a Web platform or mobile application, and users can query and download relevant reports as needed.

[0028] Optionally, the mathematical formula of the digital energy-carbon integrated machine module is as follows:

[0029] Carbon emissions calculation model:

[0030] ,

[0031] Among them, CE represents the total carbon emissions, E i represents the consumption of the i-th energy source, CF i represents the carbon emission coefficient of the i-th energy source;

[0032] Energy consumption and carbon emissions relationship model:

[0033] ,

[0034] Among them, EC is the total energy consumption, a and b are regression coefficients obtained by fitting historical data;

[0035] Carbon emission reduction benefit assessment model:

[0036] ,

[0037] Among them, B represents the carbon emission reduction benefit, CE old and CE new They represent the carbon emissions before and after emission reduction, respectively, and C represents the carbon trading price;

[0038] Energy efficiency model:

[0039] ,

[0040] Among them, EU is energy efficiency, GDP is gross domestic product, and EC is total energy consumption;

[0041] Carbon emission intensity model:

[0042] ,

[0043] Among them, CI stands for carbon emission intensity, which reflects the carbon emission level per unit of economic output.

[0044] Optionally, the data lifecycle management module includes a data generation and collection unit, a data preprocessing and cleaning unit, a data analysis unit, a data storage and management unit, and a data destruction unit, and involves a data compression algorithm, a data deduplication technology, and a data classification and indexing technology;

[0045] The data generation and collection unit, the data comes from a variety of channels, including databases, sensors, logs, determine the source, type and purpose of the data, so as to determine the subsequent processing flow;

[0046] The data preprocessing and cleaning unit uses a data cleaning algorithm to identify and correct errors, missing values ​​and abnormal values ​​in the data, uses data deduplication technology to identify and eliminate duplicate data, optimizes storage, facilitates subsequent processing, and uses a data compression algorithm to reduce the storage space required for data, reduce storage requirements and improve processing speed;

[0047] The data analysis unit uses statistical analysis, machine learning and data mining techniques to conduct in-depth analysis of data. The analysis results support decision making and provide insights for the business.

[0048] The data storage and management unit classifies and indexes the data according to the frequency of use of the data, and stores the data in a classified manner, thereby improving data retrieval efficiency and facilitating indexing and rapid retrieval;

[0049] The data destruction unit destroys the data using a data erasure algorithm and a physical destruction method when the data is no longer needed;

[0050] The data compression algorithm is expressed by a mathematical model formula: S′=S×compression_factor, where S′ is the compressed data size, S is the original data size, and compression_factor is the compression coefficient, which is less than 1;

[0051] The data deduplication technology, the mathematical model formula is expressed as: D′=D−duplicate_data, where D′ is the amount of data after deduplication, D is the amount of data before deduplication, and duplicate_data is the amount of duplicate data removed;

[0052] The data classification and indexing technology is expressed in a mathematical model formula as follows: I={(k1,v1),(k2,v2),…,(kn,vn)}, where kn is a unique identifier and vn is a data storage location.

[0053] (III) Beneficial effects

[0054] The present invention provides an account management system based on green data assets, which has the following beneficial effects:

[0055] 1. The certification and evaluation system of the present invention has a positive impact on the economic benefits, social image and long-term sustainable development of enterprises. Through the green certification and evaluation system, enterprises can identify and improve links with high energy consumption and large emissions. Green certification standards usually require enterprises to improve resource utilization and improve the production efficiency of enterprises. In order to meet the requirements of green certification, enterprises are encouraged to carry out technological innovation, and these innovations often bring about significant improvements in production efficiency.

[0056] 2. The smart contract module of the present invention can bring multi-dimensional value enhancement to enterprises in terms of system security, reliability, fairness and efficiency. Smart contracts run on a decentralized blockchain network, avoiding the risk of single point failure and ensuring the overall security of the system. Smart contracts are automatically executed through preset rules, improving the overall operating efficiency and reliability of the system. All transaction records are open to all participants, ensuring the transparency and fairness of transactions.

[0057] 3. The data asset finance module of the present invention not only helps enterprises to achieve more efficient data asset management, but also promotes the development of enterprises in the green field. Enterprises can convert static data resources into liquid funds or other forms of assets, increasing the cash flow of enterprises. Enterprises can give priority to high-quality data assets for credit pledge or securitization, thereby optimizing capital structure and improving overall operational efficiency.

[0058] 4. The digital energy-carbon integrated machine module of the present invention can improve data management efficiency, optimize resource allocation, and enhance market competitiveness, so that enterprises can instantly understand their own carbon emission status and provide strong data support for subsequent carbon reduction work. The digital energy-carbon integrated machine module helps enterprises identify the main sources of carbon emissions and key links, so as to formulate targeted carbon reduction measures.

[0059] 5. The data lifecycle management module of the present invention can improve the overall performance, efficiency and sustainability of the system. Through intelligent data classification and storage, the system can use storage resources more effectively, reduce energy consumption and hardware requirements, and help reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a module schematic diagram of the account management system of the present invention;

[0061] Figure 2 It is a module diagram of the certification and evaluation system of the present invention;

[0062] Figure 3 It is a schematic diagram of the digital energy carbon integrated machine module of the present invention;

[0063] Figure 4 It is a schematic diagram of the data lifecycle management module of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0065] Please refer to Figure 1-4, an account management system based on green data assets, the account management system includes:

[0066] Certification and evaluation system: Establish data asset evaluation standards, conduct regular performance evaluations on the account management system, and display its certification level to users to enhance the market competitiveness of the system;

[0067] Smart contract module: Use blockchain technology to establish smart contracts, automatically execute data ownership transfer and data transaction operations, improve operational efficiency and reduce human errors, confirm data assets through smart contracts, ensure data security and ownership, and facilitate data circulation and transactions;

[0068] Data asset finance module: responsible for data asset credit, data trust services, data asset securitization, data insurance pricing, and data asset value assessment. Enterprises use data assets as collateral to obtain credit funds, solve corporate financing problems, and promote enterprises to further develop data trust services, ensure the safe storage and compliant use of data, and release the value of data. By securitizing data assets, data assets can be traded in the financial market, broadening the financing channels of enterprises;

[0069] Digital energy-carbon integrated machine module: provides enterprises with comprehensive solutions including data monitoring, energy consumption management suggestions and carbon asset development guidance, helping enterprises achieve green and low-carbon development;

[0070] Data lifecycle management module: automatically adjusts the data storage location and format according to the data access frequency and importance parameters, optimizes data access efficiency, and reduces the resource occupation of invalid data;

[0071] Server terminal: used to obtain data from the certification and evaluation system, smart contract module, data asset finance module, and digital energy and carbon integrated machine module, and transmit the data to the user terminal;

[0072] User terminal: Receive data transmitted by the server terminal through the Web platform or mobile application; obtain the certification level in the certification and evaluation system; obtain the execution status and results of the smart contract, and users can query specific data or transaction records as needed; obtain information and operation interfaces of data asset financial innovation products, and users can choose financial products that suit them and query transaction records as needed; obtain relevant data and analysis results on carbon emissions and energy management, and users can query and download relevant reports as needed. Example 2

[0073] See also Figure 2 , This embodiment is further optimized on the basis of embodiment 1. Specifically, the certification and evaluation system includes a data collection and analysis unit, an environmental benefit indicator system, a green asset identification and value evaluation unit, a green rating system, and a feedback and improvement mechanism;

[0074] Data collection and analysis unit, used to collect data on system energy consumption, resource use and environmental impact, and use statistical analysis methods to evaluate the validity and representativeness of the data; environmental benefit indicator system, used to establish environmental benefit indicators based on the ecological environment, including green operating income, green capital expenditure and green operating cost ratio; green asset identification and value assessment unit, using the principle of unification, identifying direct or indirect environmental benefits generated in the production, operation and financing stages, and using financial report data to calculate the green operating income ratio, green capital expenditure ratio, green operating cost ratio, green profit ratio, green fixed asset ratio, and green investment ratio; green rating system, based on environmental benefit indicators and green asset values, the system is rated green to obtain a comprehensive green rating score, and the rating results are used for market communication and regulatory reporting; feedback and improvement mechanism, used to regularly review and adjust the assessment system to adapt to new environmental standards and technological developments, and continuously optimize green performance through feedback from users and stakeholders;

[0075] The Green Operating Income Ratio is calculated as follows:

[0076] ,

[0077] Green Revenue refers to the income obtained from green products. Green operating income refers to the income obtained by enterprises through the production and sale of green products that meet environmental protection standards. Green products refer to products that minimize environmental impact during design, production, use and disposal. These products meet the requirements of ecological protection and sustainable development by reducing resource consumption, reducing pollution emissions and improving energy efficiency. For example, electric vehicles are a kind of green product. They reduce the use of fossil fuels and reduce tail gas emissions, thereby reducing negative impacts on the environment.

[0078] The green capital expenditure ratio is calculated as follows:

[0079] ,

[0080] Green Capex refers to investment in green projects and technologies, green capital expenditure refers to the expenditure made by enterprises to develop, purchase or improve long-term assets for green products and services, and is used to purchase environmental protection technologies and equipment, build green plants, and research and develop sustainable products. Green projects and technologies refer to projects and technologies that contribute to environmental protection, resource conservation and climate change mitigation, including renewable energy technologies (such as wind power and solar energy), energy-saving and emission-reduction technologies (efficient lighting and smart grids), water resource management systems, and waste recycling and treatment technologies. For example, green projects that use photovoltaic technology to generate electricity can not only reduce dependence on fossil fuels, but also reduce carbon dioxide emissions.

[0081] The green operating cost ratio is calculated as follows:

[0082] ,

[0083] Green OpEx includes the daily expenses required to maintain green operations. Green operating costs refer to the daily expenses incurred by enterprises in normal operating activities to directly support green products or services, including the purchase cost of green raw materials, the maintenance cost of energy-saving and environmental protection equipment, and the operating cost of the environmental management system.

[0084] The green profit ratio is calculated as follows:

[0085] ,

[0086] Among them, Green Profit refers to the net profit obtained from green activities. Green profit refers to the net income obtained by enterprises through green business activities, which is the surplus after deducting the production costs of green products or services, environmental protection costs and related taxes and fees. Green activities refer to activities that reduce environmental impacts, improve the efficiency of natural resource utilization and enhance ecological sustainability. Activities include green research and development, green transformation of production processes, participation in carbon trading markets, and environmental education. For example, enterprises organize employees to plant trees to offset the carbon footprint of their business activities, which is a green activity.

[0087] The green fixed asset ratio is calculated as follows:

[0088] ,

[0089] Including equipment and technology used for green production. Green fixed assets refer to large capital items used to produce green products and provide green services with a service life of more than one year, such as energy-saving production equipment, water-saving systems, and emission reduction facilities. Green production equipment and technology refer to equipment and technology that can reduce energy consumption, reduce pollutant emissions, and improve material utilization during the production process, including wastewater recovery systems, waste gas purification facilities, and intelligent energy-saving control systems. The use of wastewater treatment technology can not only reduce the burden of industrial wastewater on the environment, but also increase the reuse rate of water resources, creating economic benefits for enterprises;

[0090] The green investment ratio is calculated as follows:

[0091] ,

[0092] Including investment in green projects and companies, which refer to enterprises or projects dedicated to the research and development, production and promotion of green products and services, and reducing the impact on the environment through various green innovations;

[0093] The overall green rating score is calculated as follows:

[0094] Composite Green Rating=w1×Green Turnover Ratio+w2×Green Capex Ratio+⋯+wn×Green Investment Ratio,

[0095] Among them, wn is the weight of each indicator, which is determined according to its importance. Example 3

[0096] This embodiment is a further optimization based on Embodiment 1. Specifically, the smart contract module includes a data collection layer, a data processing layer, a data storage layer, and a smart contract execution layer;

[0097] The data collection layer collects data from IoT devices in real time through the blockchain network to ensure the comprehensiveness and real-time nature of the data. The data includes device status and operation records. The data processing layer is used to clean, organize and analyze the data collected by the data collection layer, including data verification, error detection, missing value processing, and preliminary statistical analysis to provide a basis for subsequent smart contract execution. The data storage layer uses blockchain as a database management system to store processed data to ensure the immutability and security of the data and provide data support for upper-level applications. In the smart contract execution layer, smart contracts are automatically executed according to preset rules to perform data ownership transfer and data transaction operations. The bytecode of the smart contract is executed through EVM (Ethereum Virtual Machine) to ensure that the results of running in each node of the blockchain network are the same.

[0098] The mathematical model of the preset rules of the smart contract execution layer is as follows:

[0099] Determinism of smart contract execution: f(d1,d2,...,di)=r, where f represents the smart contract function, di represents the i-th input data, and r is the execution result. For the same input, the smart contract produces the same output on all nodes to ensure execution consistency;

[0100] Asset confirmation and transaction rules: O=H(D), where O represents the ownership of the asset, H is the hash function, and D is the device data or digital asset data. The hash function is used to associate data with ownership to ensure the tamper-proof nature of asset confirmation.

[0101] Data security and privacy protection rules: S=E(M,k), where S represents encrypted data, E is the encryption function, M is the original data, and k is the encryption key. Encryption technology is used to protect the security and privacy of data during transmission and storage.

[0102] Economic model of smart contract: C=T×(P buy −P sell )−F, where C represents the total cost of smart contract transactions, T is the number of transactions, and P buy represents the purchase price, P sell represents the sales price, and F is the transaction fee, which is used to calculate the economic benefits of smart contracts in financial transactions.

[0103] In this embodiment, the smart contract module can bring multi-dimensional value enhancement to enterprises in terms of system security, reliability, fairness, and efficiency; smart contracts run on a decentralized blockchain network, avoiding the risk of single point failure. Each node independently verifies and executes the contract logic, ensuring the overall security of the system. The immutability of the blockchain ensures that the execution results of the smart contract cannot be modified, which provides reliable security for green data asset transactions;

[0104] Smart contracts are automatically executed according to preset rules without human intervention, reducing the possibility of human error. This automated operation improves the overall operating efficiency and reliability of the system. Multiple independent nodes repeatedly process and verify smart contracts, effectively avoiding manipulation risks and ensuring high network availability and on-time execution of contracts.

[0105] The use of a peer-to-peer decentralized network to execute contract terms reduces the opportunity for centralized profit-making entities to extract transaction value. All transaction records are open to all participants, ensuring the transparency and fairness of transactions. The rules and execution conditions of smart contracts are public, and anyone can verify their fairness, which enhances the trust of market participants and promotes fair transactions of green data assets.

[0106] Smart contracts automate traditional manual back-end processes such as custody, maintenance, execution and delivery, greatly improving transaction efficiency. Contracting parties do not need to wait for manual processing or third-party intermediaries, saving time and costs. Example 4

[0107] This embodiment is a further optimization based on the embodiment 1. Specifically, the data asset finance module includes a data collection layer, a data processing layer, a data storage layer, a data analysis and evaluation layer, and a financial application layer;

[0108] The data collection layer collects the green data of enterprises in real time through API interfaces and Internet of Things equipment technology. Green data includes energy consumption data, carbon emission data, and environmental monitoring data. The data processing layer cleans, organizes and analyzes the collected data, including data verification, error detection, missing value processing, and conducts preliminary statistical analysis (calculating data mean and variance) to provide basic data support for subsequent financial innovation. The data storage layer uses a database management system to store the processed data, and at the same time applies blockchain technology to ensure that the data cannot be tampered with, providing safe and reliable data support for upper-level applications. The data analysis and evaluation layer uses big data analysis and machine learning technology to conduct in-depth data mining and evaluate the value of data assets (for example, calculating the expected returns and risks of data assets through data asset evaluation models) to provide a basis for financial product design. The financial application layer designs and implements various financial innovation products based on the results of data analysis. The products include data asset credit, data trust, data asset securitization, data insurance pricing, and data asset value evaluation. According to different market needs and risk preferences, green financial solutions are provided to enterprises.

[0109] The mathematical formulas related to the data asset finance module are as follows:

[0110] Data asset credit model:

[0111] ,

[0112] Where C represents the total amount of credit, P represents the value of data assets, r is the annual interest rate, and t is the time (year). The model is used to calculate the credit limit based on the value of data assets;

[0113] Data Trust Management Fee Model:

[0114] ,

[0115] Among them, F represents the total trust management fee, P represents the value of the data asset, r is the annual fee rate, and t is the time (year). The model is used to estimate the management fee of the data trust;

[0116] Data asset securitization valuation model:

[0117] ,

[0118] Among them, V represents the valuation of data asset securitization products, CF i represents the cash flow in the i-th year, r is the discount rate, and n is the expected life. The model is used to evaluate the value of data assets converted into securities products;

[0119] Data insurance pricing model:

[0120] ,

[0121] Where I represents the insurance cost, P represents the value of the data asset, r is the annual insurance premium rate, and t is the insurance period (years). The model is used to calculate the cost of data asset insurance.

[0122] Data asset value assessment model:

[0123] ,

[0124] Among them, P represents the value of data assets, E is the expected economic benefits brought by data assets, d is the depreciation rate of data value over time, and t is time. The model is used to evaluate the future value of data assets.

[0125] In this embodiment, the data asset finance module not only helps enterprises achieve more efficient data asset management, but also promotes the development of enterprises in the green field; by securitizing data assets and developing data asset credit, enterprises can convert static data resources into liquid funds or other forms of assets, thereby greatly improving the monetization ability of data, not only increasing the cash flow of enterprises, but also discovering the true value of data assets through market mechanisms; the data asset finance module allows enterprises to dynamically adjust resource allocation according to the value of data assets and market demand. Enterprises can give priority to high-quality data assets for credit pledge or securitization, thereby optimizing capital structure and improving overall operational efficiency;

[0126] Through the data asset finance module, enterprises can more effectively support green projects and energy-saving and emission reduction measures; it can help enterprises enter new market areas, such as the carbon trading market and the green financial market, enhance the company's social responsibility image and brand influence, and attract more consumers and investors who pay attention to environmental protection; encourage enterprises to invest in new technology research and development, such as blockchain evidence platform pledge, data asset securitization, etc., to promote the low-carbon transformation of enterprises through scientific and technological progress, which not only improves the operational efficiency of enterprises, but also provides strong technical support for green financial innovation. Example 5

[0127] See also Figure 3 This embodiment is a further optimization based on the embodiment 1. Specifically, the digital energy carbon integrated machine module includes a data acquisition layer, a data processing layer, a data storage layer, and a data analysis layer;

[0128] The data collection layer collects the energy usage data and carbon emission data of enterprises in real time through IoT devices and sensor technology, realizes automatic perception and monitoring, and meets the energy consumption and carbon emission monitoring needs of enterprises in energy use scenarios; the data processing layer cleans, organizes and analyzes the collected data, pre-processes the data through artificial intelligence algorithms, removes outliers and redundant information, improves the quality and availability of data, and flexibly configures carbon management policies, formulas and templates in the system to automatically complete carbon emission calculations, while supporting personalized calculation logic and parameters; the data storage layer uses a database management system to store the processed data, uses blockchain technology to ensure that the data cannot be tampered with, provides safe and reliable data support for upper-level applications, and realizes safe storage and efficient access to data; the data analysis layer uses carbon emission calculation models, energy consumption and carbon emission relationship models, carbon emission reduction benefit evaluation models, energy use efficiency models, and carbon emission intensity models to calculate carbon emissions, energy consumption and carbon emission relationship, carbon emission reduction benefits, energy use efficiency, and carbon emission intensity, and evaluates the carbon emission status and energy use efficiency of enterprises;

[0129] The mathematical formula of the digital energy carbon integrated machine module is as follows:

[0130] Carbon emissions calculation model:

[0131] ,

[0132] Among them, CE represents the total carbon emissions, E i represents the consumption of the i-th energy source, CF i represents the carbon emission coefficient of the i-th energy source;

[0133] Energy consumption and carbon emissions relationship model:

[0134] ,

[0135] Among them, EC is the total energy consumption, a and b are regression coefficients obtained by fitting historical data;

[0136] Carbon emission reduction benefit assessment model:

[0137] ,

[0138] Among them, B represents the carbon emission reduction benefit, CE old and CE new They represent the carbon emissions before and after emission reduction, respectively, and C represents the carbon trading price;

[0139] Energy efficiency model:

[0140] ,

[0141] Among them, EU is energy efficiency, GDP is gross domestic product, and EC is total energy consumption;

[0142] Carbon emission intensity model:

[0143] ,

[0144] Among them, CI stands for carbon emission intensity, which reflects the carbon emission level per unit of economic output.

[0145] In this embodiment, the digital energy-carbon integrated machine module can improve data management efficiency, optimize resource allocation, and enhance market competitiveness; the digital energy-carbon integrated machine module can collect the company's carbon emissions and energy use data in real time, which greatly improves the real-time and accuracy of data management, enabling companies to instantly understand their own carbon emissions status, providing strong data support for subsequent carbon reduction work, and can automatically generate detailed reports on carbon emissions and energy use, reducing the company's manpower input and time costs, which not only improves data processing efficiency, but also avoids errors in manual operations, ensuring the accuracy and consistency of reports;

[0146] Through in-depth analysis of carbon emission data, the digital energy-carbon integrated machine module helps enterprises identify the main sources of carbon emissions and key links, so as to formulate targeted carbon reduction measures, enabling enterprises to allocate resources more effectively and focus on high-efficiency carbon reduction projects. The module has energy supervision and energy-saving control functions, which can monitor energy consumption in real time and optimize equipment operation, thereby improving resource utilization efficiency;

[0147] By achieving efficient carbon reduction through the digital energy-carbon integrated module, enterprises can significantly improve their green image in the minds of the public and consumers and enhance their market competitiveness. Example 6

[0148] See also Figure 4 This embodiment is a further optimization based on the embodiment 1. Specifically, the core of the data lifecycle management module is how to efficiently and dynamically manage the whole process from data generation to destruction, which involves data generation, storage, processing, analysis and final destruction. The data lifecycle management module includes a data generation and collection unit, a data preprocessing and cleaning unit, a data analysis unit, a data storage and management unit, a data destruction unit, and involves data compression algorithms, data deduplication technology, and data classification and indexing technology;

[0149] Data generation and collection unit: data comes from various channels, including databases, sensors, and logs. The source, type, and purpose of the data are determined to determine the subsequent processing flow;

[0150] Data preprocessing and cleaning unit, using data cleaning algorithms to identify and correct errors, missing values ​​and outliers in the data, using data deduplication technology to identify and eliminate duplicate data, optimize storage, facilitate subsequent processing, and use data compression algorithms to reduce the storage space required for data, reduce storage requirements and increase processing speed;

[0151] The data analysis unit uses statistical analysis, machine learning and data mining techniques to conduct in-depth analysis of data. The analysis results support decision making and provide insights for the business;

[0152] The data storage and management unit classifies and indexes the data according to the frequency of data use, and stores the data in categories to improve data retrieval efficiency and facilitate indexing and rapid retrieval;

[0153] Data destruction unit, when data is no longer needed, uses data erasure algorithms and physical destruction methods to ensure that data cannot be recovered, destroys data safely and prevents data leakage;

[0154] Data compression algorithm, the mathematical model formula is expressed as: S′=S×compression_factor, where S′ is the compressed data size, S is the original data size, and compression_factor is the compression factor, which is less than 1;

[0155] Data deduplication technology, the mathematical model formula is expressed as: D′=D−duplicate_data, where D′ is the amount of data after deduplication, D is the amount of data before deduplication, and duplicate_data is the amount of duplicate data removed;

[0156] Data classification and indexing technology, the mathematical model formula is expressed as: I={(k1,v1),(k2,v2),…,(kn,vn)}, where kn is a unique identifier and vn is the data storage location.

[0157] In this embodiment, the data lifecycle management module can improve the overall performance, efficiency and sustainability of the system; through intelligent data classification and storage, the system can use storage resources more efficiently, reduce energy consumption and hardware requirements, help reduce operating costs, reduce unnecessary storage costs and energy expenses, intelligently destroy data that is no longer needed, enhance data security, reduce energy consumption and electronic waste, and meet the goals of sustainable development.

[0158] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An account management system based on green data assets, characterized in that: The account management system comprises: Certification and evaluation system: establish data asset evaluation standards, conduct regular performance evaluations of account management systems, and display their certification levels to users. The certification and evaluation system includes a data collection and analysis unit, an environmental benefit indicator system, a green asset identification and value evaluation unit, a green rating system, and a feedback and improvement mechanism. The data collection and analysis unit is used to collect data on system energy consumption, resource use, and environmental impact. The environmental benefit indicator system is used to establish environmental benefit indicators based on the ecological environment. The indicators include the proportion of green operating income, green capital expenditure, and green operating costs. The green asset identification and value evaluation unit adopts a unified principle to identify direct or indirect environmental benefits generated during the production, operation, and financing stages, and uses financial report data to calculate the green operating income ratio, green capital expenditure ratio, green operating cost ratio, green profit ratio, green fixed asset ratio, and green investment ratio. The green rating system performs green ratings on the system based on environmental benefit indicators and green asset values ​​to obtain a comprehensive green rating score. The feedback and improvement mechanism is used to regularly review and adjust the evaluation system. Smart contract module: Use blockchain technology to establish smart contracts, automatically execute data ownership transfer and data transaction operations, and confirm the ownership of data assets through smart contracts. The smart contract module includes data collection layer, data processing layer, data storage layer, and smart contract execution layer; the data collection layer collects data from IoT devices in real time through the blockchain network, and the data includes device status and operation records; the data processing layer is used to clean, organize and analyze the data collected by the data collection layer, including data verification, error detection, and missing value processing; the data storage layer uses blockchain as a database management system to store processed data and provide data support for upper-level applications; in the smart contract execution layer, smart contracts are automatically executed according to preset rules, perform data ownership transfer and data transaction operations, and execute the bytecode of smart contracts through EVM to ensure that the results of running in each node of the blockchain network are the same; The mathematical model of the preset rules of the smart contract execution layer is as follows: Determinism of smart contract execution: f(d1,d2,...,di)=r, where f represents the smart contract function, di represents the i-th input data, and r is the execution result. For the same input, the smart contract produces the same output on all nodes; Asset confirmation and transaction rules: O=H(D), where O represents the ownership of the asset, H is the hash function, and D is the device data or digital asset data; Data security and privacy protection rules: S=E(M,k), where S represents the encrypted data, E is the encryption function, M is the original data, and k is the encryption key; Economic model of smart contract: C=T×(P buy −P sell )−F, where C represents the total cost of smart contract transactions, T is the number of transactions, and P buy represents the purchase price, P sell represents the sales price, and F is the transaction fee, which is used to calculate the economic benefits of smart contracts in financial transactions; Data asset finance module: responsible for data asset credit, data trust services, data asset securitization, data insurance pricing, data asset value assessment, and ensuring the safe storage and compliant use of data; Digital energy-carbon integrated machine module: provides enterprises with comprehensive solutions including data monitoring, energy consumption management suggestions and carbon asset development guidance; the digital energy-carbon integrated machine module includes data collection layer, data processing layer, data storage layer and data analysis layer; the data collection layer collects the energy use data and carbon emission data of the enterprise in real time through Internet of Things devices and sensor technology; the data processing layer cleans, organizes and analyzes the collected data, and the system flexibly configures carbon management policies, formulas and templates to automatically complete carbon emission calculations, while supporting personalized calculation logic and parameters; the data storage layer uses a database management system to store processed data and uses blockchain technology to ensure that the data cannot be tampered with; the data analysis layer uses carbon emission calculation models, energy consumption and carbon emission relationship models, carbon emission reduction benefit evaluation models, energy use efficiency models, and carbon emission intensity models to calculate carbon emissions, energy consumption and carbon emission relationship, carbon emission reduction benefits, energy use efficiency, and carbon emission intensity, and evaluate the carbon emission status and energy use efficiency of the enterprise; Data lifecycle management module: automatically adjusts the data storage location and format according to the data access frequency and importance parameters, optimizes data access efficiency, and reduces the resource occupation of invalid data; Server terminal: used to obtain data from the certification and evaluation system, smart contract module, data asset finance module, and digital energy and carbon integrated machine module, and transmit the data to the user terminal; User terminal: Receive data transmitted by the server terminal through the Web platform or mobile application; obtain the certification level in the certification and evaluation system; obtain the execution status and results of the smart contract, and users can query specific data or transaction records as needed; obtain information and operation interfaces of data asset financial innovation products, and users can choose financial products that suit them and query transaction records as needed; obtain relevant data and analysis results on carbon emissions and energy management, and users can query and download relevant reports as needed.

2. An account management system based on green data assets according to claim 1, characterized in that: The data asset finance module includes a data collection layer, a data processing layer, a data storage layer, a data analysis and evaluation layer, and a financial application layer; The data collection layer collects the green data of enterprises in real time through API interfaces and IoT device technologies. The green data includes energy consumption data, carbon emission data, and environmental monitoring data. The data processing layer cleans, organizes and analyzes the collected data, including data verification, error detection, missing value processing, and preliminary statistical analysis; the data storage layer uses a database management system to store the processed data; the data analysis and evaluation layer uses big data analysis and machine learning technology to conduct in-depth data mining and evaluate the value of data assets; the financial application layer designs and implements various financial innovation products based on the data analysis results, including data asset credit, data trust, data asset securitization, data insurance pricing, and data asset value assessment.

3. According to claim 1, an account management system based on green data assets is characterized in that: The data lifecycle management module includes a data generation and collection unit, a data preprocessing and cleaning unit, a data analysis unit, a data storage and management unit, and a data destruction unit, and involves data compression algorithms, data deduplication technology, and data classification and indexing technology.

4. An account management system based on green data assets according to claim 3, characterized in that: The data generation and collection unit, the data comes from a variety of channels, including databases, sensors, logs, to determine the source, type and purpose of the data; The data preprocessing and cleaning unit uses a data cleaning algorithm to identify and correct errors, missing values ​​and abnormal values ​​in the data, uses a data deduplication technology to identify and eliminate duplicate data, optimizes storage, and uses a data compression algorithm to reduce the storage space required for the data; The data analysis unit uses statistical analysis, machine learning and data mining techniques to conduct in-depth analysis of the data, and the analysis results support decision making; The data storage and management unit classifies and indexes the data according to the frequency of use of the data, and stores the data in a classified manner by using data classification and indexing technology; The data destruction unit destroys the data using a data erasure algorithm and a physical destruction method when the data is no longer needed.

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