Method, system, equipment and medium for designing medium and small-sized user electricity and carbon beneficiation mode
By building a dynamic carbon emission factor model and using blockchain technology to manage user points, the problems of low carbon emission accounting accuracy and high participation threshold in the traditional carbon credit mechanism have been solved, accurate accounting of carbon emission reduction behaviors and dynamic adjustment of incentive strategies have been achieved, and the participation of small and medium-sized users has been improved.
Patent Information
- Application Number
- CN202510851478.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-24
- Publication Date
- 2025-09-19
AI Technical Summary
The traditional carbon credit mechanism has problems such as low carbon emission accounting accuracy, high participation threshold and static incentives, and cannot effectively guide small and medium-sized users to participate in carbon emission reduction actions.
By acquiring electricity, meteorological and unit status data, building a dynamic carbon emission factor model, and combining blockchain technology to manage user points, real-time quantification of carbon emission reduction behaviors and dynamic adjustment of incentive strategies can be achieved.
It has improved the accuracy and credibility of carbon emission accounting, lowered the participation threshold for small and medium-sized users, and promoted the participation of a wider range of social groups in carbon emission reduction actions.
Smart Images
Figure CN120671927A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of energy management and carbon emission reduction, and in particular to a design method, system, equipment and medium for a carbon-inclusive electricity subsidy model for small and medium-sized users. Background Art
[0002] Traditional carbon credit mechanisms often use fixed carbon emission factors (such as the annual average grid emission factor) to calculate user electricity carbon emissions, which has the following defects: low accuracy, as the fixed factors do not take into account real-time changes in the power system's power generation structure (such as fluctuations in thermal power and renewable energy output), resulting in accounting errors of up to 15%-30%; high participation thresholds, as small and medium-sized users lack professional equipment and technical support and find it difficult to bear the complex carbon accounting costs; static incentives, and the existing points issuance rules are single and not dynamically linked to the cleanliness of the grid, making it impossible to effectively guide users to use electricity off-peak or consume green electricity. Summary of the Invention
[0003] In view of the above existing problems, the present invention is proposed.
[0004] Therefore, the present invention provides a design method, system, equipment and medium for a carbon credit model for small and medium-sized users to solve the problem of low carbon emission accounting accuracy of the existing carbon credit mechanism.
[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for designing a carbon-based electricity subsidy model for small and medium-sized users, comprising: Obtain power data, meteorological data, and unit status data; Based on pre-processed power data, meteorological data, and unit status data, a dynamic carbon emission factor model is constructed. Through calculation and correction, the dynamic carbon emission factor at the current moment is generated. The actual carbon emissions on the user side are calculated by combining the current dynamic carbon emission factor with the user's electricity consumption data, and the user's accumulated points are obtained based on the set dynamic baseline and time-based incentive weights; Blockchain technology is used to manage users' accumulated points and achieve real-time quantification of users' carbon emission reduction behaviors.
[0006] As a preferred solution of the method for designing a carbon-based electricity subsidy model for small and medium-sized users described in the present invention, the preprocessing includes: The 3σ principle is used to eliminate data that exceeds the historical fluctuation range; Use forward filling method to fill missing values; The data are standardized using the mean and standard deviation of historical data for the same period.
[0007] As a preferred solution of the design method of a carbon-inclusive electricity subsidy model for small and medium-sized users described in the present invention, the construction of a dynamic carbon emission factor model includes: Calculate the basic emission factor based on the actual output of different types of energy and their corresponding carbon emission intensity, combined with cross-regional input power and annual average emission factors; The dynamic carbon emission factor at the current moment is obtained through green electricity deviation correction and unit start and stop adjustment.
[0008] As a preferred solution of the design method of the carbon-inclusive electricity subsidy model for small and medium-sized users described in the present invention, the green electricity deviation correction includes using meteorological data to predict green electricity output and correcting the dynamic carbon emission factor according to the deviation between the actual output and the predicted value; The unit start-up and shutdown adjustment includes considering the impact of the start-up and shutdown status of the thermal power unit on the carbon emission factor, and adjusting it through the empirical coefficient to generate a dynamic carbon emission factor at the current moment.
[0009] As a preferred solution of the design method of a carbon-inclusive electricity subsidy model for small and medium-sized users described in the present invention, the calculation of the user's actual carbon emissions and the generation of inclusive points based on a set dynamic baseline and time-based incentive weights include: Combine user electricity consumption data with the current dynamic carbon emission factor to calculate the user's actual carbon emissions in a specific time period; The median of the emission factors at the same time in the past seven days is selected as the dynamic baseline; Set different incentive weights according to different time periods of the day; Generate inclusive credits based on a dynamic baseline, the user's actual carbon emissions, and time-based incentive weights; Add up the inclusive points for each time period of the day to get the user's accumulated points.
[0010] As a preferred solution of the design method of the electricity-carbon inclusive model for small and medium-sized users described in the present invention, the management of user accumulated points includes: Use Paillier algorithm to encrypt user electricity consumption data; Generate a data hash value at a fixed time interval using the SM3 algorithm; The encrypted data is stored in a distributed file system and accessed through a unique hash address.
[0011] As a preferred solution of the design method of the electricity carbon credit model for small and medium-sized users described in the present invention, the median of the emission factors at the same time in the past 7 days is selected as the dynamic baseline and expressed as follows: , in, Indicates the dynamic baseline, Represents the operation of taking the median, Indicates the carbon emission factor at the same time 24 hours ago, Indicates the carbon emission factor at the same time 48 hours ago, Indicates the carbon emission factor at the same time 168 hours ago.
[0012] In a second aspect, the present invention provides a system for designing a carbon-based electricity benefit model for small and medium-sized users, comprising: Acquisition module, used to obtain power data, meteorological data and unit status data; The dynamic carbon emission factor generation module is used to build a dynamic carbon emission factor model based on pre-processed power data, meteorological data, and unit status data. Through calculation and correction, it generates the dynamic carbon emission factor at the current moment. The points generation module is used to calculate the actual carbon emissions on the user side by combining the current dynamic carbon emission factor with the user's electricity consumption data, and obtain the user's accumulated points based on the set dynamic baseline and time-based incentive weights; The management module is used to manage users' accumulated points using blockchain technology and realize real-time quantification of users' carbon emission reduction behaviors.
[0013] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of the method for designing a carbon-free electricity model for small and medium-sized users.
[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, it implements the steps of the method for designing a carbon-free electricity model for small and medium-sized users.
[0015] Beneficial effects of the present invention: The present invention integrates the real-time power generation data of the power system with load forecast information to build a minute-level dynamic carbon emission factor prediction model, thereby realizing accurate accounting of the carbon emission intensity of user electricity consumption; based on the edge computing device, local processing of meter data and dynamic factors is carried out, and the dynamic baseline and time-sharing weights are combined to generate inclusive points, breaking through the delay and privacy risks of traditional centralized accounting, and the incentive strategy is more in line with the user's actual emission reduction contribution; blockchain technology is used to perform distributed storage and verification of user carbon data and points, realizing transparent traceability and automated incentives for carbon emission reduction behaviors, thereby not only improving the accuracy and credibility of carbon emission accounting, but also significantly lowering the participation threshold for small and medium-sized users, and promoting a wider range of social groups to participate in carbon emission reduction actions. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0017] Figure 1 A schematic diagram of the basic flow of a method for designing an electricity-carbon inclusive model for small and medium-sized users provided in one embodiment of the present invention. DETAILED DESCRIPTION
[0018] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.
[0019] Example 1, with reference to Figure 1 , which is an embodiment of the present invention, provides a method for designing a carbon-based electricity subsidy model for small and medium-sized users, including: S100: Acquisition of power data, meteorological data, and unit status data; S200: Based on the pre-processed power data, meteorological data, and unit status data, a dynamic carbon emission factor model is constructed. Through calculation and correction, the dynamic carbon emission factor at the current moment is generated. S300: Calculate the actual carbon emissions on the user side by combining the current dynamic carbon emission factor with the user's electricity consumption data, and obtain the user's accumulated points based on the set dynamic baseline and time-based incentive weights; S400: Use blockchain technology to manage users’ accumulated points and achieve real-time quantification of users’ carbon emission reduction behaviors.
[0020] It should be noted that traditional methods usually rely on fixed annual average grid emission factors to calculate users' carbon emissions. This method fails to fully consider changes in the real-time power generation structure of the power system (such as fluctuations in the output of thermal power and renewable energy), resulting in significant accounting errors; small and medium-sized users find it difficult to bear the cost of complex carbon accounting due to the lack of professional equipment and technical support, which not only limits their ability to participate in carbon emission reduction plans, but also affects the overall emission reduction effect and social participation; the existing points issuance rules are single and not dynamically linked to the cleanliness of the grid. It cannot effectively guide users to use electricity off-peak or consume green electricity, reducing the effectiveness and attractiveness of incentives; therefore, it is particularly important to explore the design method of carbon-inclusive electricity models for small and medium-sized users based on dynamic carbon emission factors.
[0021] Therefore, in order to address the problem of low carbon emission accounting accuracy in the existing carbon credit mechanism, steps S100-S400 are used to accurately calculate users' carbon footprints through real-time updated carbon emission factors, and edge computing is used to achieve localized processing to reduce participation costs. Blockchain technology is used to ensure data transparency and the effectiveness of incentives. This not only improves the accuracy and credibility of carbon emission accounting, but also significantly lowers the participation threshold for small and medium-sized users, and promotes the participation of a wider range of social groups in carbon reduction actions.
[0022] Example 2, which is an embodiment of the present invention, provides a design method for a carbon-based electricity benefit model for small and medium-sized users based on the previous embodiment, including: In this embodiment of the present invention, the power data in step S100 includes real-time thermal power, wind power, and photovoltaic unit output data (minute-level sampling) and cross-regional transmission power data (15-minute level) obtained through the power dispatch center API; meteorological data includes access to the meteorological bureau API to obtain regional wind speed, light intensity, and temperature data (10-minute level) for predicting renewable energy output; unit status data includes the start and stop status of thermal power units collected through the SCADA system (binary signal, 1-second sampling).
[0023] In the embodiment of the present invention, the preprocessing in step S200 includes: The 3σ principle is used to eliminate data that exceeds the historical fluctuation range; Use forward filling method to fill missing values; The data are standardized using the mean and standard deviation of historical data for the same period.
[0024] In the embodiment of the present invention, the data is standardized using the mean and standard deviation of historical data for the same period, which is expressed as: , in, Indicates the standardized value; Indicates the raw data point that needs to be standardized. It can be any numerical data, such as the carbon emission factor or electricity consumption at a certain time of day. The mean and standard deviation of the data for the same period in the past 7 days; through the normalization processing formula, any data point X is normalized to its relative position in the past 7 days.
[0025] In the embodiment of the present invention, the step S200 of constructing a dynamic carbon emission factor model includes: Calculate the basic emission factor based on the actual output of different types of energy and their corresponding carbon emission intensity, combined with cross-regional input power and annual average emission factors; By correcting the green electricity deviation and adjusting the start and stop of the unit, the dynamic carbon emission factor at the current moment is obtained; The green electricity deviation correction includes using meteorological data to predict green electricity output and correcting the dynamic carbon emission factor according to the deviation between the actual output and the predicted value; The unit start-up and shutdown adjustment includes considering the impact of the start-up and shutdown status of the thermal power unit on the carbon emission factor, and adjusting it through the empirical coefficient to generate a dynamic carbon emission factor at the current moment.
[0026] In the embodiment of the present invention, the basic emission factor Expressed as: , in, in From 1 to The index represents different generator sets; represents the output of unit k at time t (MW); represents the carbon emission intensity of unit k (thermal power uses the standard reference value of carbon emission intensity for different types of energy generation provided by the Intergovernmental Panel on Climate Change (IPCC). Wind power generation and photovoltaic power generation produce almost no direct carbon emissions during their operation, so their carbon emission intensity is set to 0 when calculating the carbon emission factor); represents the total power generation of the local power system at time t; It represents the inter-regional input power, using the annual average emission factor of the power transferred into the grid.
[0027] In the embodiment of the present invention, green electricity output mainly involves two types of renewable energy: photovoltaic (solar energy) and wind power (wind energy), using meteorological data (such as light intensity and wind speed ) to predict the output of photovoltaic and wind power as follows: , in, represents the predicted photovoltaic power generation power at time t; represents the light intensity at time t; Indicates photovoltaic efficiency (typical value 15%~22%); , Indicates the equivalent photovoltaic / wind power equipment area; represents the predicted wind power generation at time t; Indicates the air density; Indicates wind speed The cube of .
[0028] In an embodiment of the present invention, correcting the dynamic carbon emission factor based on the deviation between the actual output and the predicted value includes comparing the difference between the actual green electricity output and the predicted value, and determining whether it exceeds a first threshold (10%). If the deviation between the actual output and the predicted value exceeds 10%, the dynamic carbon emission factor needs to be corrected; otherwise, the correction step is skipped directly.
[0029] In the embodiment of the present invention, the dynamic carbon emission factor at the current moment after the start and stop adjustment of the thermal power unit is Expressed as: , in, express Capacity change of units started and stopped at any moment (MW); represents the benchmark capacity (taken as 10% of the total installed capacity of the region); represents the empirical correction coefficient, =0.15.
[0030] In the embodiment of the present invention, constructing the dynamic carbon emission factor model further includes inputting the dynamic carbon emission factor at the current moment and historical data into the autoregressive integrated moving average model to predict the dynamic carbon emission factor at the next moment. Expressed as: , in, It represents the dynamic carbon emission factor at the current moment after all corrections. Represents the previous moment after all corrections Dynamic carbon emission factors, , represents the autoregressive coefficient, , represents the sliding mean coefficient; express The white noise error term at time , Indicates the previous moment The white noise error term.
[0031] It should be noted that the dynamic carbon emission factor model requires a starting point for subsequent adjustments and optimization. The base emission factor, a static value derived from an average of long-term data, can serve as this starting point. It provides a basic reference framework for the dynamic model, allowing it to be modified based on real-time data during actual operation.
[0032] In this embodiment of the present invention, the calculation of the user's actual carbon emissions in step S300 and the generation of inclusive credits based on the set dynamic baseline and time-based incentive weights include: Combine user electricity consumption data with the current dynamic carbon emission factor to calculate the user's actual carbon emissions in a specific time period; The median of the emission factors at the same time in the past seven days is selected as the dynamic baseline; Set different incentive weights according to different time periods of the day; Generate inclusive credits based on a dynamic baseline, the user's actual carbon emissions, and time-based incentive weights; Add up the inclusive points for each time period of the day to get the user's accumulated points.
[0033] In the embodiment of the present invention, the actual carbon emissions are expressed as: , in, Indicates user electricity consumption data (kWh); Indicates the line loss coefficient (default is 0.95).
[0034] In the embodiment of the present invention, the median of the emission factors at the same time in the past 7 days is selected as the dynamic baseline and expressed as: , in, Indicates the dynamic baseline, Represents the operation of taking the median, Indicates the carbon emission factor at the same time 24 hours ago, Indicates the carbon emission factor at the same time 48 hours ago, Indicates the carbon emission factor at the same time 168 hours ago.
[0035] In the embodiment of the present invention, the time-division incentive weight Expressed as: , In the embodiment of the present invention, Puhui points are generated Expressed as: , in, Denotes the reference emission factor.
[0036] In this embodiment of the present invention, users accumulate points Expressed as: , in, It represents the total accumulated hourly from time point 0 (i.e. midnight 00:00) to time point 23 (i.e. 23:00 at night).
[0037] In an embodiment of the present invention, the management of user accumulated points in step S400 includes: Use Paillier algorithm to encrypt user electricity consumption data; Generate a data hash value at a fixed time interval using the SM3 algorithm; The encrypted data is stored in a distributed file system and accessed through a unique hash address.
[0038] In the embodiment of the present invention, the Paillier (Paillier homomorphic encryption algorithm) algorithm is used to encrypt the user's electricity consumption data. Expressed as: , in, Represents plaintext data (power consumption); g, n represent public key parameters, and r represents a random number; If there are two plaintext data and , and their corresponding ciphertexts are and , then the addition operation can be performed by the following formula: .
[0039] It should be noted that this feature allows operations on encrypted data without decryption, which is very suitable for data processing scenarios that require privacy protection.
[0040] In this embodiment of the present invention, the data hash value is generated once at a fixed time interval (every 15 minutes) using the National Cryptography SM3 (Commercial Cryptographic Hash Algorithm) algorithm and is expressed as: , in, Represents data splicing to ensure uniqueness and tamper-proofing; Indicates a timestamp.
[0041] In an embodiment of the present invention, the encrypted data is stored in a distributed file system and accessed through a unique hash address, including storing the encrypted data of the original data in IPFS and generating an IPFS hash address. Expressed as: .
[0042] in, Represents an encoding method used to convert binary data into human-readable strings. Represents the Secure Hash Algorithm 256-bit, which is used to generate hash values for encrypted data.
[0043] In an embodiment of the present invention, managing the user's accumulated points in step S400 further includes: Define points redemption rules, including redemption standards for carbon quotas and green electricity certificates; Dynamically adjust the enterprise administrator's authority threshold and open batch redemption permissions based on user points contribution; Use zero-knowledge proof verification technology to verify the total amount of points and carbon emissions The matching relationship ensures the consistency between the total points and carbon emissions without exposing specific data; Provides full-link traceability query function, retrieves historical transaction records in the blockchain based on time windows, and enhances transparency and traceability.
[0044] In the embodiment of the present invention, the carbon quota exchange standard Expressed as: , in, Indicates the user's accumulated points; every 100 points can be exchanged for 0.1 tons of carbon quota, rounded down.
[0045] In the embodiment of the present invention, the redemption standard of green electricity certificates is Expressed as: , In an embodiment of the present invention, the enterprise administrator authority threshold is dynamically adjusted Expressed as: , in, Indicates the number of enterprise-associated users, which refers to the total number of valid users who have passed enterprise authentication and bound accounts; Indicates the The points value of each user is dynamically calculated based on carbon emission behavior (such as energy-saving rewards, green electricity use, etc.); Indicates the total points of the region, which is the sum of the points of all users within the current administrative / geographical scope. When the total points of enterprise users account for more than 80% of the total points of the region, batch redemption permissions are automatically opened.
[0046] In this embodiment of the present invention, the total amount of points is verified using zero-knowledge proof verification technology. and carbon emissions The matching relationship is expressed as: , in, Represents a commitment function (such as a Pedersen commitment) used to generate an encrypted value; It is a random number, ensuring that the commitment value generated each time is different; and They are the baseline carbon emissions and the user's actual carbon emissions.
[0047] In an embodiment of the present invention, historical transaction records in the blockchain are retrieved based on the time window. Expressed as: , in, Represents blockchain transaction records, and Indicates the start and end time of the time window.
[0048] Example 3 is an embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a design system for an electricity-carbon inclusive model for small and medium-sized users.
[0049] It should be noted that the technical solution of the design system of the electricity-carbon inclusive model for small and medium-sized users and the technical solution of the design method of the electricity-carbon inclusive model for small and medium-sized users mentioned above belong to the same concept. For details not described in detail in the technical solution of the design system of the electricity-carbon inclusive model for small and medium-sized users in this embodiment, please refer to the description of the technical solution of the design method of the electricity-carbon inclusive model for small and medium-sized users mentioned above.
[0050] In this embodiment, a system for designing a carbon-based electricity subsidy model for small and medium-sized users includes: Acquisition module, used to obtain power data, meteorological data and unit status data; The dynamic carbon emission factor generation module is used to build a dynamic carbon emission factor model based on pre-processed power data, meteorological data, and unit status data. Through calculation and correction, it generates the dynamic carbon emission factor at the current moment. The points generation module is used to calculate the actual carbon emissions on the user side by combining the current dynamic carbon emission factor with the user's electricity consumption data, and obtain the user's accumulated points based on the set dynamic baseline and time-based incentive weights; The management module is used to manage users' accumulated points using blockchain technology and realize real-time quantification of users' carbon emission reduction behaviors.
[0051] This embodiment further provides an electronic device applicable to a method for designing a carbon-based electricity subsidy model for small and medium-sized users, including: Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a design method for a carbon-free electricity subsidy model for small and medium-sized users as proposed in the above embodiment.
[0052] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a design method for a carbon-free electricity subsidy model for small and medium-sized users as proposed in the above embodiment.
[0053] The storage medium proposed in this embodiment and the design method for implementing a carbon-inclusive electricity benefit model for small and medium-sized users proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.
[0054] From the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware. Of course, it can also be implemented using hardware, but in many cases the former is the preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product can be stored on a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disk, and includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.
[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A design method for a carbon-based electricity benefit model for small and medium-sized users, characterized by: include: Obtain power data, meteorological data, and unit status data; Based on pre-processed power data, meteorological data, and unit status data, a dynamic carbon emission factor model is constructed. Through calculation and correction, the dynamic carbon emission factor at the current moment is generated. The actual carbon emissions on the user side are calculated by combining the current dynamic carbon emission factor with the user's electricity consumption data, and the user's accumulated points are obtained based on the set dynamic baseline and time-based incentive weights; Blockchain technology is used to manage users' accumulated points and achieve real-time quantification of users' carbon emission reduction behaviors.
2. The design method for a carbon-based electricity benefit model for small and medium-sized users according to claim 1, characterized in that: The preprocessing comprises: The 3σ principle is used to eliminate data that exceeds the historical fluctuation range; Use forward filling method to fill missing values; The data are standardized using the mean and standard deviation of historical data for the same period.
3. The design method for a carbon-based electricity benefit model for small and medium-sized users according to claim 2, characterized in that: The construction of the dynamic carbon emission factor model includes: Calculate the basic emission factor based on the actual output of different types of energy and their corresponding carbon emission intensity, combined with cross-regional input power and annual average emission factors; The dynamic carbon emission factor at the current moment is obtained through green electricity deviation correction and unit start and stop adjustment.
4. The design method for a carbon-based electricity benefit model for small and medium-sized users according to claim 3, characterized in that: The green electricity deviation correction includes using meteorological data to predict green electricity output and correcting the dynamic carbon emission factor according to the deviation between the actual output and the predicted value; The unit start-up and shutdown adjustment includes considering the impact of the start-up and shutdown status of the thermal power unit on the carbon emission factor, and adjusting it through the empirical coefficient to generate a dynamic carbon emission factor at the current moment.
5. The design method for a carbon-based electricity benefit model for small and medium-sized users according to claim 4, characterized in that: The calculation of the user's actual carbon emissions and the generation of inclusive credits based on the set dynamic baseline and time-based incentive weights include: Combine user electricity consumption data with the current dynamic carbon emission factor to calculate the user's actual carbon emissions in a specific time period; The median of the emission factors at the same time in the past seven days is selected as the dynamic baseline; Set different incentive weights according to different time periods of the day; Generate inclusive credits based on a dynamic baseline, the user's actual carbon emissions, and time-based incentive weights; Add up the inclusive points for each time period of the day to get the user's accumulated points.
6. The method for designing a carbon-based electricity benefit model for small and medium-sized users according to claim 5, characterized in that: The management of user accumulated points includes: Use Paillier algorithm to encrypt user electricity consumption data; Generate a data hash value at a fixed time interval using the SM3 algorithm; The encrypted data is stored in a distributed file system and accessed through a unique hash address.
7. The method for designing a carbon-based electricity benefit model for small and medium-sized users according to claim 6, characterized in that: The median of the emission factors at the same time in the past 7 days is selected as the dynamic baseline and is expressed as: ， in, Indicates the dynamic baseline, Represents the operation of taking the median, Indicates the carbon emission factor at the same time 24 hours ago, Indicates the carbon emission factor at the same time 48 hours ago, Indicates the carbon emission factor at the same time 168 hours ago.
8. A system for designing an electricity-carbon inclusive model for small and medium-sized users, applying a method for designing an electricity-carbon inclusive model for small and medium-sized users as described in any one of claims 1 to 7, characterized in that: include: Acquisition module, used to obtain power data, meteorological data and unit status data; The dynamic carbon emission factor generation module is used to build a dynamic carbon emission factor model based on pre-processed power data, meteorological data, and unit status data. Through calculation and correction, it generates the dynamic carbon emission factor at the current moment. The points generation module is used to calculate the actual carbon emissions on the user side by combining the current dynamic carbon emission factor with the user's electricity consumption data, and obtain the user's accumulated points based on the set dynamic baseline and time-based incentive weights; The management module is used to manage users' accumulated points using blockchain technology and realize real-time quantification of users' carbon emission reduction behaviors.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for designing an electricity carbon credit model for small and medium-sized users as described in any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for designing an electricity-carbon inclusive model for small and medium-sized users as described in any one of claims 1 to 7 are implemented.