Trusted calculation method and system for regional correction carbon emission factors

Abstract

The invention discloses a trusted computing method and system for regional correction of carbon emission factors, and relates to the technical field of blockchains, comprising: constructing a blockchain intelligent terminal; performing classification comparison; establishing a carbon emission factor calculation model; establishing a carbon emission model; and analyzing the carbon emission coefficient influence factors of the regional power grid. According to the invention, accurate measurement of a data source and calculation under different power utilization conditions by using a block chain intelligent terminal and a carbon emission flow theory are realized, so that the technical problems that the accuracy of carbon emission accounting is insufficient and the nearby consumption of new energy is not fully considered are solved, and the accuracy of carbon emission accounting is improved; therefore, the carbon emission data can reflect the actual situation more truly and reliably, the enthusiasm of users to use renewable energy sources is stimulated, the nearby consumption of new energy sources is promoted, and a powerful basis is provided for making a reasonable carbon reduction policy.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain, and specifically to a trusted computing method and system for regionally calibrated carbon emission factors. Background Art

[0002] In the current severe situation of continuously intensifying global environmental problems, climate change has become a major challenge faced by all mankind. As one of the key factors causing climate change, carbon emission reduction has become the core concern of the international community. The power industry, as an important area of energy consumption in modern society, accounts for a quite large proportion in the total global carbon emissions. Therefore, how to effectively reduce the carbon emissions of the power industry and achieve the green and sustainable development of the power system has become an important issue to be solved urgently. Against the grand background of addressing climate change, the importance of accurately calculating carbon emissions is self-evident.

[0003] Currently, the commonly used carbon emission factor calculation methods have many drawbacks in practical applications. On the one hand, traditional methods usually rely on macro statistical data and general standards, lacking specific consideration for the actual situations of different regions. There are huge differences among different regions in terms of energy structure, industrial layout, geographical environment, and climate conditions. For example, regions rich in energy resources may be dominated by fossil fuels such as coal and oil, while some regions rich in renewable energy rely more on clean energy such as solar energy and wind energy. In terms of industrial structure, the energy consumption and carbon emission patterns in industrially developed regions are completely different from those in regions dominated by agriculture or services. Such regional differences make it difficult for traditional general carbon emission factors to accurately reflect the actual carbon emission situations of specific regions.

[0004] On the other hand, traditional carbon emission factors are often static and difficult to adapt to various dynamic changes within a region. With the development of the economy and the progress of technology, the industrial structure of the region is continuously adjusted, new technologies emerge continuously, and energy policies are also changing constantly. These dynamic factors have a significant impact on carbon emissions. For example, the application of new energy-saving technologies may greatly reduce energy consumption and carbon emission intensity, and the transformation of the industrial structure towards a low-carbon direction will also change the carbon emission characteristics of the region. However, traditional carbon emission factor calculation methods cannot capture these changes in a timely manner, resulting in calculation results lagging behind the actual situation and being difficult to meet the dynamic requirements of regional carbon emission management.

[0005] In order to achieve effective carbon emission management and emission reduction goals, each region urgently needs a more accurate and trusted carbon emission factor calculation method. The trusted computing method for regionally calibrated carbon emission factors can be adjusted and optimized according to the characteristics of specific regions, fully considering factors such as the energy structure, industrial characteristics, geographical climate, etc. of the region and the impact of various dynamic changes, so as to improve the accuracy and practicality of carbon emission calculation results.

[0006] In the field of urban planning, accurate carbon emission factors contribute to the rational planning of transportation networks, the optimization of building designs, and the determination of energy supply plans for public facilities, thereby reducing the urban carbon emission level and enhancing the sustainable development capacity of the city. In terms of industrial development, enterprises can formulate more scientific and reasonable energy conservation and emission reduction plans based on the regionally calibrated carbon emission factors, reducing the negative impact on the environment while improving production efficiency. For government departments, reliable carbon emission factors can provide an important basis for formulating energy policies, environmental regulations, and carbon trading market rules, promoting the green transformation and sustainable development of the regional economy.

[0007] However, existing carbon emission factor calculation methods often lack sufficient consideration of different regional characteristics, resulting in insufficient accuracy and credibility of the calculation results in specific regions. Different regions vary in energy structure, industrial layout, geographical climate, etc., and these factors can significantly affect carbon emission factors. Therefore, a reliable calculation method that can be calibrated according to regional characteristics is needed. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed.

[0009] Therefore, the technical problems solved by the present invention are: the existing carbon emission factor calculation methods have low accuracy and credibility, are greatly affected by other factors, and how to create a reliable calculation method that can be calibrated according to regional characteristics.

[0010] To solve the above technical problems, the present invention provides the following technical solutions: A reliable calculation method for regionally calibrated carbon emission factors, including constructing a blockchain intelligent terminal, performing classification and comparison, establishing a carbon emission factor calculation model, and establishing a carbon emission model to analyze the influencing factors of the carbon emission coefficient of the regional power grid.

[0011] As a preferred embodiment of the reliable calculation method for regionally calibrated carbon emission factors of the present invention, wherein: the construction of the blockchain intelligent terminal includes designing a hardware architecture including functional units such as a power supply module, a main control storage unit, a remote communication module, and a data acquisition module, and a software architecture including an operating system layer and an application layer.

[0012] As a preferred embodiment of the reliable calculation method for regionally calibrated carbon emission factors of the present invention, wherein: the classification and comparison include processing three cases according to whether the self-built PV power generation of the user side is less than, equal to, or greater than the power consumption of the user side in the region.

[0013] As a preferred embodiment of the reliable calculation method for regional corrected carbon emission factors of the present invention, the establishment of the carbon emission factor calculation model includes, based on existing power flow calculations, where the active power of the input node is equal to the active power of the output node, sharing the active power of the output node branches proportionally, calculating the carbon emission flow of each branch, and then obtaining the node carbon emission intensity and the regional carbon emission factor.

[0014] As a preferred embodiment of the reliable calculation method for regional corrected carbon emission factors of the present invention, the establishment of the carbon emission model includes establishing a carbon emission intensity model when the self-built PV on the user side is less than the user's electricity consumption, and establishing a carbon emission intensity correction model when the self-built PV on the user side is equal to the user's consumption.

[0015] As a preferred embodiment of the reliable calculation method for regional corrected carbon emission factors of the present invention, the establishment of the carbon emission model includes establishing a carbon emission intensity correction model for the remaining grid-connected power of the self-built PV on the user side.

[0016] As a preferred embodiment of the reliable calculation method for regional corrected carbon emission factors of the present invention, the analysis of the influencing factors of the regional power grid carbon emission coefficient includes obtaining the influence of the power generation and consumption on the load side of the self-built PV on the regional power grid carbon emission coefficient by analyzing and comparing the calculation results, thereby improving the accuracy of carbon emission accounting and promoting the consumption of renewable energy.

[0017] Another object of the present invention is to provide a reliable calculation system for regional corrected carbon emission factors, which can solve the problems of the current traditional carbon emission factor calculation technology, such as being unable to accurately reflect the actual emissions of the region, not paying attention to the impact of new energy grid connection and consumption, lacking incentives and fairness for user carbon reduction, low timeliness, and difficulty in guaranteeing data authenticity, through one of the solutions, the blockchain intelligent terminal.

[0018] As a preferred embodiment of the reliable calculation system for regional corrected carbon emission factors of the present invention, it includes a power supply module, a main control storage unit module, a remote communication module, and a data acquisition module; the power supply module is used to provide stable power supply for the system to ensure that all other modules can work normally. The power supply module often includes functions such as battery management, power conversion, and voltage stabilization. The main control storage unit module is used to store the data, models, and intermediate operation and decision results of the entire system. The remote communication module is used to transmit information such as the collected data and correction results, and supports functions such as data upload, download, and remote monitoring. The data acquisition module is used to convert the original data into digital signals and transmit them to the main control unit for further processing and analysis.

[0019] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a trusted computing method for regional correction of carbon emission factors.

[0020] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of a trusted computing method for regional correction of carbon emission factors are implemented.

[0021] Advantages of the present invention: The present invention realizes trusted data collection at the source and full-process traceability through a blockchain intelligent terminal, constructs a multi-scenario dynamic carbon emission calculation model in combination with the power flow tracking theory, and introduces a spatio-temporal two-dimensional dynamic correction mechanism, significantly improving the accuracy and timeliness of carbon emission accounting. Specifically, the system designs a hardware-software collaborative architecture to ensure the non-tampering and traceability of multi-source data from the power generation side to the user side; establishes a differential scenario model for photovoltaic accommodation on the user side based on the power flow tracking theory to accurately quantify the impact of new energy's nearby accommodation on the carbon emissions of the regional power grid; innovatively integrates a two-layer correction model of the time dimension and the space dimension to achieve spatio-temporal dynamic adjustment of carbon emission factors through a dynamic weight matrix and a climate characteristic weight matrix. This solution not only provides spatio-temporal differentiation basis for policies such as peak-valley electricity prices and carbon trading, but also can effectively identify high-carbon emission periods and regions, guide users to use electricity during off-peak hours and rationally arrange power sources, and promote the optimal allocation of energy. Compared with traditional static and macroscopic carbon emission calculation methods, the present invention ensures data trust through blockchain technology, combines multi-scenario dynamic modeling and spatio-temporal correction mechanisms, and has remarkable effects in regions with high new energy penetration rates, providing scientific support for the regional green and low-carbon transformation and helping to achieve the "dual carbon" goal. Description of the Drawings

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 It is the overall flowchart of a trusted computing method for regional correction of carbon emission factors provided by the first embodiment of the present invention.

[0024] Figure 2 It is the principle diagram of flow tracking of a trusted computing method for regional correction of carbon emission factors provided by the first embodiment of the present invention.

[0025] Figure 3 It is the node schematic diagram of a trusted computing method for regional correction of carbon emission factors provided by the second embodiment of the present invention.

[0026] Figure 4 Schematic diagram of an example node for a reliable calculation method of regional corrected carbon emission factors provided in the second embodiment of the present invention.

[0027] Figure 5 Spatial-temporal distribution carbon emission correction model architecture diagram for a reliable calculation method of regional corrected carbon emission factors provided in the second embodiment of the present invention

[0028] Figure 6 Typical regional spatial-temporal carbon emission factor comparison curve for a reliable calculation method of regional corrected carbon emission factors provided in the second embodiment of the present invention. By comparing the changes in carbon emission factors of different regions in the time dimension, the effectiveness of the spatio-temporal correction model in the patent is visually verified. Detailed implementation manners

[0029] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following provides a detailed description of the specific implementation manners of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0030] Embodiment 1, referring to Figure 1 and Figure 2 , provides a reliable calculation method for regional corrected carbon emission factors according to an embodiment of the present invention, including:

[0031] S1: Construct a blockchain intelligent terminal for classification and comparison.

[0032] Specifically, constructing the blockchain intelligent terminal includes designing a hardware architecture including functional units such as a power supply module, a main control storage unit, a remote communication module, and a spatio-temporal data acquisition module, as well as a software architecture including an operating system layer and an application layer.

[0033] Furthermore, from the power generation side, software methods are used to ensure that subsequent data cannot be tampered with and is traceable, but there is still a risk of data tampering at the source of the terminal device, resulting in insufficient credibility of source data collection and measurement. In the distribution Internet of Things, there is an urgent need to study blockchain chip fusion technology for demand-side perception. Based on the hardware underlying architecture of the device, we designed a reliable data chain function to achieve reliable management of the entire process of data source collection, data chain upload, and data storage.

[0034] (1) Terminal architecture design

[0035] Build a complete functional unit system, covering a power supply module, a main control storage unit, a remote communication module, and a data acquisition module with protocol parsing and data forwarding functions, etc., and equipped with external interfaces such as Bluetooth and Ethernet, thereby laying a solid foundation for data acquisition, processing, and transmission from the hardware basis; in terms of software architecture, a layered architecture including an operating system layer (composed of system components, operating system kernel, driver framework, startup program, etc.) and an application layer is designed. With such an architecture arrangement, ensure the stable operation of the system and the realization of various application functions, and enable the terminal to process various data operation tasks in an efficient manner.

[0036] (2) Terminal function design

[0037] It is clear that the blockchain device can comprehensively collect, deeply analyze, and securely store the data of various measurement devices according to the pre-configured collection tasks, and actively send this data to the blockchain monitoring platform. At the same time, it can also respond in a timely manner and send data when called for testing, and it is required that the measurement device data recorded must be highly consistent with the corresponding data displayed by the connected measurement device to ensure the accuracy and reliability of the data.

[0038] (3) Communication method planning

[0039] When communicating with the blockchain monitoring platform, the terminal can accurately follow the platform instructions. Whether it is timed or on-demand, it can efficiently send the collected and stored information to the platform in a timely and accurate manner to ensure the timeliness and effectiveness of information transmission; at the same time, when it comes to the transmission of important data, parameter settings, and control messages, strong security measures will be firmly implemented to effectively prevent data leakage and illegal tampering, and effectively maintain the security and stability of communication. In terms of communicating with the measurement device, the measurement device can collect and store data in an orderly manner according to the established collection interval, and can also quickly respond to the data forwarding instructions of the blockchain monitoring platform, and transmit the data to the monitoring platform quickly and directly through the remote channel to achieve the rapid flow of data; and it is emphasized that strict security measures must be implemented for the communication between the two to further strengthen the data security barrier and ensure the security and reliability of the entire communication link, thereby providing a solid guarantee for the data interaction of the blockchain intelligent terminal in the calculation of regional corrected carbon emission factors.

[0040] Conduct classification and comparison, including dividing into three cases according to the self-built PV power generation and power consumption on the user side in the region and processing them separately, namely, the self-built PV power generation on the user side is less than the power consumption, the self-built PV power generation on the user side is equal to the power consumption, and the self-built PV power generation on the user side is greater than the power consumption.

[0041] Furthermore, by installing photovoltaic inverter monitoring equipment, photovoltaic power generation monitoring equipment, and a data platform, and connecting it to a cloud platform or local data acquisition system, real-time power generation data can be obtained and recorded through the platform. Data analysis tools can then be used to process the collected data to obtain hourly or daily power generation data. By installing smart meters or traditional meters, hourly or daily electricity consumption can be collected and recorded.

[0042] It should be noted that once real-time data on photovoltaic power generation and user electricity consumption are available, they can be compared using the following methods: direct comparison method, difference calculation method, and automatic comparison method for system monitoring.

[0043] S2: Establish a carbon emission factor calculation model.

[0044] Specifically, the establishment of a carbon emission factor calculation model includes calculating the active power of the input node equal to the active power of the output node based on the existing power flow, sharing the active power of the output node branch in proportion, calculating the carbon emission flow of each branch, and then deriving the node carbon emission intensity and regional carbon emission factor.

[0045] Furthermore,

[0046] (1) Active power is P n As shown in the following formula:

[0047]

[0048] Where s is the power of all input nodes I, P G is the power generated by node I’s own generator set, P n Branch P flowing out of node I n Medium P m The proportion of node I, P is the total injected power of node I.

[0049] (2) Carbon emission flow is R n As shown in the following formula:

[0050]

[0051] Where n is the nth branch, ρ represents the carbon emission intensity of the nth branch, R n It is the sum of the inflows from each branch in I+ and the generator.

[0052] (3) The carbon emission intensity of the nth branch of node I is as follows:

[0053]

[0054] Among them, P n ′ is the active power P n The first derivative of .

[0055] (4) User-side carbon emissions intensity \(E\) for user-side carbon emissions I (That is, the carbon emissions generated per unit of electricity consumption of each user) is shown in the following formula:

[0056]

[0057] The carbon emissions intensity is the carbon emissions generated by the generator when Node I consumes a single unit of kilowatt-hour of electric energy. The carbon emissions intensity of a node is determined by the carbon emissions intensity of all inbound branches of that node. It can be observed that the carbon emissions intensity of a given outflow branch does not depend on \(n\). Therefore, all outflow branches of a given node have the same branch carbon emissions density, and this density depends only on the total input carbon emissions of that node and the carbon emissions generated by any generator on that node.

[0058] (5) The regional carbon emissions factor is shown in the following formula:

[0059]

[0060] The regional carbon emissions intensity and carbon emissions factor can be calculated according to the above formula.

[0061] The carbon emissions factor calculation model further includes a dynamic weight matrix, specifically as follows:

[0062] 1) Time weight matrix: Train a neural network model based on historical load data and output the carbon emissions sensitivity coefficients for each time period.

[0063] \(\omega\) time \(= f\)

[0064] where \(f\) is a neural network fitting function, specifically represented by the load curve and renewable energy output data.

[0065] 2) Spatial weight matrix: Combine the regional energy map and meteorological data and output the carbon emissions correction coefficients for each geographical unit.

[0066] \(\omega\) space \(= g\)

[0067] where \(g\) is a regression model based on GIS data, specifically the energy structure and geographical climate characteristics.

[0068] S3: Establish a carbon emissions model and analyze the influencing factors of the carbon emissions coefficient of the regional power grid.

[0069] Specifically, establishing a carbon emissions model includes establishing a carbon emissions intensity model when the self-built PV on the user side is less than the user's electricity consumption, establishing a carbon emissions intensity correction model when the self-built PV on the user side is equal to the user's consumption, and a carbon emissions intensity correction model for the surplus grid connection of the self-built PV on the user side.

[0070] Furthermore, the basic principle of power flow tracking: Based on the existing power flow calculation, without considering power losses and other situations, the active power input at node I is equal to the active power output at node I.

[0071] (1) Carbon emission intensity model when the self-built PV on the user side is less than the user consumption

[0072] When the user's electricity consumption is greater than the self-built PV and wind power generation, the user can be equivalent to an ordinary load in the power system. The calculation method is the carbon emission factor calculation model in step 3.

[0073] (2) Carbon emission intensity correction model when the self-built PV on the user side is equal to the user consumption

[0074] When the self-built PV system on the user side is equal to the user's electricity consumption, all the electricity consumed on the user side belongs to green electricity and the carbon emissions generated are zero. According to the formula, all the outgoing branches of a given node have the same branch carbon emission density. Assume that the user with self-built PV is at branch F. When the production and sales of self-built PV offset each other, the remaining outgoing branches of node I will evenly distribute the carbon emissions, that is:

[0075]

[0076] In the formula, E j is the average carbon emission distribution of the remaining outgoing branches of node I.

[0077] (3) Carbon emission intensity correction model for the remaining grid-connected power of the self-built PV on the user side

[0078] When the self-built PV power generation on the user side exceeds the user's electricity consumption, the remaining electricity will be sold to the grid. At this time, the user is transformed from a load to a power source. As shown in the attachment Figure 3 , a simple system is represented by three nodes. Node 1 is connected to a thermal power unit, and nodes 2 and 3 are user-side loads. Node 2 is the self-built PV system on the user side, which can be treated as a normal load when the PV power generation is less than or equal to the consumption. When the PV power generation is greater than the electricity consumption, node 2 is equivalent to connecting a new energy unit for a day. The user converts it into a power source and transmits green electricity to the grid system. If we still calculate the carbon emission flow according to the first part, the result will be inaccurate. The formula is obtained:

[0079]

[0080] Analyzing the influencing factors of the carbon emission coefficient of the regional power grid includes obtaining the influence of the power generation and consumption on the load side of the self-built PV on the carbon emission coefficient of the regional power grid by analyzing and comparing the calculation results, so as to improve the accuracy of carbon emission accounting and promote the consumption of renewable energy.

[0081] (4) Establish a spatio-temporal distribution carbon emission correction model on the user side

[0082] The differences in power supply structure in different seasons / time periods and the impact of regional geographical characteristics on the accommodation of new energy. To solve this problem, the present invention realizes the dynamic correction of carbon emission factors by quantifying the influence parameters in the time dimension (seasons / time periods) and the space dimension (regional geographical characteristics). This model forms a three-level linkage with the carbon emission factor calculation model in step S2 and the user-side correction model in step S3, and constructs a spatio-temporal refined carbon emission accounting system covering all links of power generation - power transmission and distribution - load.

[0083] 1) Time dimension correction

[0084] λ t = λ base ×α season ×β time

[0085] Among them, α season is the season correction coefficient, and β time is the time slot correction coefficient.

[0086] 2) Space dimension correction

[0087] λ s = λ t ×γ region ×δ climate

[0088] Among them, γ region is the regional type correction coefficient, and δ climate is the climate correction coefficient.

[0089] In summary, through the correction model, we can obtain the accurate carbon emission flow on the user side.

[0090] Furthermore, through the carbon emission factor calculation model and the carbon emission model, simulate the carbon emission changes under different scenarios (such as different photovoltaic power generation scales, different grid demands, etc.), and predict the specific impact of the self-built photovoltaic system on the carbon emission factor. According to the model prediction results, propose strategies such as optimizing power dispatching and increasing the scale of the photovoltaic system to reduce the carbon emissions of the power grid.

[0091] Example 2, referring to Figures 3 - 6 , is an embodiment of the present invention, which provides a reliable calculation method for regional correction of carbon emission factors. To verify the beneficial effects of the present invention, scientific demonstrations are carried out through economic benefit calculations and simulation experiments.

[0092] First, take the IEEE14 node system as an example for analysis. Connect 5 generator sets to nodes 1, 2, 3, 6, and 8 respectively, and simulate three scenarios:

[0093] Scenario 1: During the initial operation of the system, there are three thermal power units (connected to nodes 1, 2, and 6) and two new energy units (connected to nodes 3 and 8). During the research process, the node carbon potential and branch carbon flow are used as research indicators and analyzed. The red part is the carbon flow diagram. Through alternative power, the carbon emission intensity of thermal power generation is reduced on this basis, thereby gradually reducing the proportion of thermal power generation.

[0094] Scenario 2: We change all the output of Unit 4 (connected to Node 6) to green power.

[0095] Scenario 3: On the basis of Scenario 2, 50% of the thermal power output of Unit 1 is replaced with green power.

[0096] Scenario 4: Considering the verification of the corrected model considering spatio-temporal distribution, the Beijing-Tianjin-Hebei region and the Yangtze River Delta region are selected for comparison to compare the differences in carbon emission factors between summer and winter.

[0097] The experimental results of Scenario 1, Scenario 2, and Scenario 3 are attached Figure 4 indicating that as the new energy power generation on the user side increases, the carbon emissions in the power grid also change accordingly.

[0098] Example 3, an embodiment of the present invention, provides a trusted computing system for regional corrected carbon emission factors, including a power supply module, a main control storage unit module, a remote communication module, and a data acquisition module;

[0099] Among them, the power supply module is used to provide stable power supply for the system to ensure that all other modules can work normally. The power supply module often includes functions such as battery management, power conversion, and voltage stabilization. The main control storage unit module is used to store the data, models, and intermediate operation and decision results of the entire system. The remote communication module is used to transmit information such as the collected data and corrected results, and supports functions such as data upload, download, and remote monitoring. The data acquisition module is used to convert the original data into digital signals and transmit them to the main control unit for further processing and analysis.

[0100] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0101] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0102] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing when necessary, and then stored in a computer memory.

[0103] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A reliable calculation method for regionally corrected carbon emission factors, characterized in that, Including: Construct a blockchain intelligent terminal for classification and comparison; Establish a carbon emission factor calculation model; Establish a carbon emission model to analyze the influencing factors of the carbon emission coefficient of the regional power grid; Establish a spatio-temporal distribution carbon emission correction model to dynamically adjust the calculation parameters of the carbon emission factor according to the time dimension and the space dimension.

2. The credible calculation method for regionally corrected carbon emission factors as claimed in claim 1, wherein: The construction of the blockchain intelligent terminal includes designing a hardware architecture of functional units including a power module, a main control storage unit, a remote communication module, and a data acquisition module, and a software architecture including an operating system layer and an application layer.

3. The credible calculation method for regionally calibrated carbon emission factors according to claim 2, characterized in that: The classification and comparison includes processing three cases according to the regional self-built PV power generation and electricity consumption on the user side, namely, the self-built PV power generation on the user side is less than the electricity consumption, the self-built PV power generation on the user side is equal to the electricity consumption, and the self-built PV power generation on the user side is greater than the electricity consumption.

4. The credible calculation method for regional calibration of carbon emission factors according to claim 3, characterized in that: The establishment of the carbon emission factor calculation model includes based on the existing power flow calculation, the input node active power is equal to the output node active power, the branch active power of the output node is shared proportionally, the carbon emission flow of each branch is calculated, and then the node carbon emission intensity and the regional carbon emission factor are obtained; and the carbon emission factor calculation model includes a spatio-temporal weight matrix, where the time weight coefficient is determined according to the power grid load curve and the output characteristics of renewable energy, and the space weight coefficient is determined according to the regional energy structure and geographical and climatic characteristics.

5. The credible calculation method for regionally calibrated carbon emission factors as claimed in claim 4, wherein: The establishment of the carbon emission model includes establishing a carbon emission intensity model when the self-built PV on the user side is less than the user's electricity consumption and establishing a carbon emission intensity correction model when the self-built PV on the user side is equal to the user's consumption.

6. The credible calculation method for regionally calibrated carbon emission factors as described in claim 5, characterized in that: The establishment of the carbon emission model includes establishing a carbon emission intensity correction model for the remaining grid-connected power generation of the self-built PV on the user side.

7. The trusted calculation method for regional calibration of carbon emission factors according to claim 6, characterized in that: The analysis of the influencing factors of the carbon emission coefficient of the regional power grid includes obtaining the influence of the load-side power generation and electricity consumption of the self-built PV on the carbon emission coefficient of the regional power grid by analyzing and comparing the calculation results, so as to improve the accuracy of carbon emission accounting and promote the consumption of renewable energy.

8. A system adopting the trusted computing method for regionally corrected carbon emission factors as described in any one of claims 1 to 7, characterized in that: Including a power module, a main control storage unit module, a remote communication module, and a data acquisition module; The power module is used to provide stable power supply for the system to ensure that all other modules can work normally. The power module includes battery management, power conversion, and voltage stabilization; The main control storage unit module is used to store the data, models, and intermediate operation and decision results of the entire system; The remote communication module is used to transmit the collected data and correction result information, and support functions such as data upload, download, and remote monitoring; The data acquisition module is used to convert the original data into digital signals and transmit them to the main control unit for processing and analysis.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the reliable calculation method for regional correction of carbon emission factors described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the reliable calculation method for regional correction of carbon emission factors described in any one of claims 1 to 7.

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