Block chain-based power-carbon collaborative data governance and intelligent optimization method, system and device, and medium

Abstract

The invention relates to the technical field of power-carbon collaboration, and discloses a block chain-based power-carbon collaboration data management and intelligent optimization method, system, device and medium, and the method comprises the steps: collecting power-carbon multi-source data in real time, and carrying out the data preprocessing; establishing a correlation model between electricity and carbon data so as to quantify a correlation relationship between unit power generation and carbon emission, and obtaining carbon emission reduction data; a double-objective optimization function is adopted to construct an electricity-carbon collaborative decision-making large model, and an optimal unit output scheme is output through deep learning; and generating a block chain hash identifier for each green electricity transaction, performing contract automatic verification, and if a performance risk is monitored, triggering early warning and sending an optimization suggestion. According to the invention, by constructing a technical system based on power-carbon collaborative data management and intelligent optimization, accurate conduction of carbon emission technical constraints, safety interaction of cross-system data, credible tracing of environmental rights and interests and dynamic prevention and control of risks are realized, and fusion and efficient collaborative operation of a power system and carbon emission management are promoted.

Description

Technical Field

[0001] This invention relates to the field of electricity-carbon synergy technology, and in particular to a blockchain-based method, system, device, and medium for electricity-carbon synergy data governance and intelligent optimization. Background Technology

[0002] Existing research and practices regarding collaborative management and data exchange in the electricity carbon market have revealed numerous technical challenges. Currently, power dispatching systems, carbon emission monitoring systems, and green electricity certification systems fall under the technical jurisdiction of different departments such as energy and environmental protection, resulting in a severe lack of system synergy due to their independent technical systems. The data standards and interface protocols of different systems are independent, and there is a lack of a unified technical format for storing relevant environmental rights certificates, making cross-system mutual recognition impossible. This not only significantly reduces the overall efficiency of data flow but also substantially increases the cost of renewable energy consumption monitoring and carbon emission reduction data verification, severely hindering effective technical integration and collaborative operation between systems.

[0003] At the level of technology constraint transmission, the correlation between the carbon emission characteristics of generating units and power dispatch decisions is unclear. The physical impact of carbon emissions from high-energy-consuming units has not been incorporated into dispatch optimization models through technical means, making it difficult to reasonably allocate their environmental costs through system operation strategies. This hinders efforts to incentivize generation on the power generation side to reduce emission intensity through technological upgrades and fails to effectively guide energy conservation and emission reduction actions at the power system level. Simultaneously, due to obstacles in the transmission of technical parameters, electricity consumers struggle to obtain green electricity selection criteria based on carbon emission data, hindering the accurate matching and efficient utilization of green electricity resources.

[0004] From a system operation perspective, inter-provincial technical barriers (such as incompatible data interfaces and differences in scheduling algorithms) result in insufficient technical support for the cross-provincial consumption of green electricity. This technical limitation hinders the optimal allocation of power resources on a larger scale, leading to low resource utilization efficiency and failing to fully leverage the complementary advantages of green electricity across different regions. Furthermore, existing research on electricity-carbon synergy is not comprehensive enough in its coverage of key technical elements such as data fusion technology, intelligent scheduling algorithms, and risk warning models. This incomplete technical system causes market players to face problems such as inconsistent data and delayed decision-making when conducting electricity-carbon synergy management, making it difficult to establish a stable and efficient technical support model. Summary of the Invention

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a blockchain-based method, system, device, and medium for collaborative data governance and intelligent optimization of electricity and carbon emissions, addressing the technical disconnect and insufficient data collaboration between power systems and carbon emission management, which leads to a series of technical deficiencies, specifically manifested as follows: First, there is a disconnect between carbon emission data and power generation, resulting in poor transmission of technological constraints. Under the existing mechanism, technical parameters such as the carbon emission intensity of generating units lack dynamic correlation with power dispatching schemes. The physical impact of carbon emissions from high-energy-consuming units is not incorporated into output decisions through technical means, and the environmental value of low-carbon units is difficult to reflect through dispatching optimization.

[0006] Second, the lack of a cross-system technical framework creates barriers to data exchange. Power dispatching systems and carbon emission monitoring systems belong to different technical systems, with independent data acquisition standards, interface protocols, and storage formats. This forces power generation companies to repeatedly input data into multiple systems, increasing technical integration costs and the risk of data inconsistencies. Furthermore, the lack of technical-level coordination and verification between cross-system output plans and carbon emission constraints can easily lead to mismatches between actual power output and carbon emission limits, impacting system operational efficiency.

[0007] Third, there is insufficient technical evidence for environmental rights, and credible means of traceability are lacking. Electricity and carbon emission data are stored in various systems, and key information such as power generation and carbon emission reduction are not technically linked. The recording of environmental rights (such as carbon emission reduction corresponding to green electricity) lacks unified technical standards and tamper-proof evidence, which can easily lead to problems such as double counting and data tampering, undermining market fairness based on technical facts.

[0008] Fourth, the technical support for risk prevention and control is weak, and the dynamic adjustment capability is insufficient. Current technologies lack real-time monitoring and intelligent intervention technologies for deviations between unit output and carbon emissions. When actual carbon emissions exceed technical quotas, there is a lack of automated output adjustment mechanisms, making it difficult to mitigate compliance risks in advance through technical means. Furthermore, early warning of abnormal fluctuations in cross-system data relies on manual verification, resulting in delayed responses and impacting system operational stability.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a blockchain-based method for collaborative data governance and intelligent optimization of electricity carbon, including: real-time acquisition of multi-source electricity carbon data and data preprocessing; Based on the processed multi-source data of electricity carbon, a correlation model between the electricity carbon data is established to quantify the correlation between unit power generation and carbon emissions, and to obtain carbon emission reduction data. Based on the aforementioned multi-source data on electricity carbon and the correlation between unit power generation and carbon emissions, a large-scale collaborative decision-making model for electricity carbon is constructed using a dual-objective optimization function, and the optimal unit output scheme is output through deep learning. Based on the carbon emission reduction data, a blockchain hash identifier is generated for each green electricity transaction, and the contract is automatically verified. If a performance risk is detected, an early warning is triggered and optimization suggestions are sent.

[0010] As a preferred embodiment of the blockchain-based collaborative data governance and intelligent optimization method for electricity carbon as described in this invention, the real-time acquisition of multi-source electricity carbon data and the data preprocessing include: The multi-source data on electricity carbon includes electricity data, carbon market data, and external data. The data preprocessing includes data cleaning, data desensitization, and data fusion to eliminate data redundancy and conflicts and generate unified data standards.

[0011] As a preferred embodiment of the blockchain-based collaborative data governance and intelligent optimization method for electricity carbon as described in this invention, the establishment of a correlation model among electricity carbon data includes: A model relating carbon emission intensity to power output is constructed to obtain the total actual carbon emissions of the unit by using the unit carbon emission intensity and actual power generation. A multi-constraint collaborative model is constructed, which includes electricity supply and demand balance constraints and total carbon emission constraints. Construct an environmental rights quantification model to obtain the carbon emission reduction of green electricity trading by using the actual green electricity generation and the baseline emission intensity; The beneficial effects of this preferred scheme are that it directly links carbon emission intensity with unit output, prioritizes the dispatch of new energy and low emission units, promotes the consumption of renewable energy, and accelerates the decarbonization of the power system.

[0012] As a preferred embodiment of the blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions as described in this invention, the construction of a large-scale collaborative decision-making model for electricity and carbon emissions includes: A dual-objective optimization function and inter-provincial power transmission constraints were constructed, and the model was trained using carbon dioxide data. An optimization weighting factor is introduced, which is automatically optimized based on the completion of emission reduction targets. That is, the value of the optimization weighting factor is automatically adjusted according to the deviation between the actual emission reduction and the target emission reduction. If the actual emission reduction is lower than the target emission reduction, the optimization weighting factor is increased to strengthen the output priority of low-carbon units. If the actual emission reduction is higher than the target emission reduction, the optimization weighting factor is decreased. The beneficial effect of this preferred scheme is that it dynamically identifies the risk of deviation between unit output and carbon emissions through real-time data monitoring and large-scale model early warning algorithms, and adjusts the output scheme accordingly.

[0013] As a preferred embodiment of the blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions as described in this invention, the large-scale collaborative decision-making model for electricity and carbon emissions is represented as follows: ; in, Indicates the basic cost of electricity generation. This indicates the actual power generation of the generating unit. Indicates the impact coefficient of carbon emission technology. This represents the optimization weight factor. It represents the intensity of carbon emissions per unit.

[0014] As a preferred embodiment of the blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions described in this invention, the environmental rights quantification model is expressed as follows: ; in, This represents the carbon emission reduction from green electricity trading. Indicates the baseline emission intensity. Indicates carbon emission intensity per unit. This indicates the actual power generation of the unit.

[0015] As a preferred embodiment of the blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions described in this invention, the method includes: generating a blockchain hash identifier for each green electricity transaction and performing automatic contract verification, including: The blockchain hash identifier Represented as: ; Where t is the timestamp, i is the unit code, and H represents the hash function. This indicates the actual power generation of the generating unit. This indicates the carbon emission reduction from green electricity trading; Set up a rights-related verification contract. When green electricity rights are used for green certificates or carbon offsets, the contract will be executed automatically. By comparing on-chain data, it will ensure that the carbon emission reduction corresponding to the blockchain hash identifier has not been reused. After the verification is passed, the contract will automatically mark the blockchain hash identifier as used and update the carbon quota offset record in a synchronous manner.

[0016] Secondly, this invention provides a blockchain-based system for collaborative data governance and intelligent optimization of electricity and carbon emissions, comprising: The data acquisition and processing module is used to acquire multi-source data of the electrocarbon battery in real time and perform data preprocessing. The first construction module is used to establish a correlation model between the electricity carbon data based on the processed electricity carbon multi-source data, so as to quantify the correlation between unit power generation and carbon emissions, and obtain carbon emission reduction data. The second construction module is used to construct a large-scale collaborative decision-making model for electricity carbon based on the multi-source data of electricity carbon and the correlation between unit power generation and carbon emissions, using a dual-objective optimization function, and output the optimal unit output scheme through deep learning; The optimization monitoring module is used to generate a blockchain hash identifier for each green electricity transaction based on the carbon emission reduction data, and to automatically verify the contract. If a performance risk is detected, an early warning is triggered and optimization suggestions are sent.

[0017] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the blockchain-based method for collaborative data governance and intelligent optimization of electricity and carbon.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the blockchain-based electrocarbon collaborative data governance and intelligent optimization method.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a technical system based on power-carbon collaborative data governance and intelligent optimization, this invention achieves precise transmission of carbon emission technical constraints, secure interaction of cross-system data, reliable traceability of environmental rights, and dynamic prevention and control of technical risks, ultimately promoting deep technical integration and efficient collaborative operation of power systems and carbon emission management. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the overall process of a blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions, as described in one embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram illustrating the specific process of a blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions, as described in one embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of a blockchain-based collaborative data governance and intelligent optimization system for electricity and carbon emissions, as described in one embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram illustrating the specific structure of a blockchain-based collaborative data governance and intelligent optimization system for electricity and carbon emissions, as described in one embodiment of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figures 1-4 As one embodiment of the present invention, a blockchain-based method for collaborative data governance and intelligent optimization of electricity and carbon emissions is provided, such as... Figure 1 and Figure 2 As shown, it includes: S101 collects multi-source data on carbon dioxide in real time and performs data preprocessing; S102. Based on the processed multi-source data of electricity carbon, establish a correlation model between electricity carbon data to quantify the correlation between unit power generation and carbon emissions, and obtain carbon emission reduction data. S103, based on multi-source data on electricity carbon and the correlation between unit power generation and carbon emissions, adopts a dual-objective optimization function to construct a large-scale collaborative decision-making model for electricity carbon, and outputs the optimal unit output scheme through deep learning; S104 generates a blockchain hash identifier for each green electricity transaction based on carbon emission reduction data and performs automatic contract verification. If a performance risk is detected, an early warning is triggered and optimization suggestions are sent.

[0027] It should be noted that this invention, by constructing a technical system based on electricity-carbon collaborative data governance and intelligent optimization, achieves precise transmission of carbon emission technical constraints, secure interaction of cross-system data, reliable traceability of environmental rights, and dynamic prevention and control of technical risks, ultimately promoting deep technical integration and efficient collaborative operation of the power system and carbon emission management.

[0028] In a preferred embodiment, real-time acquisition of multi-source electrocarbon data and data preprocessing include: Electricity carbon multi-source data includes electricity data, carbon market data, and external data; Data preprocessing includes data cleaning, data desensitization, and data fusion to eliminate data redundancy and conflicts and generate unified data standards.

[0029] Specifically, standardized interfaces are used to collect electricity market data (power generation, unit parameters, power load, etc.), carbon market data (carbon allowance holdings, carbon emission intensity, carbon trading records, etc.), and external data (emission reduction targets, meteorological data, etc.), supporting compatibility between structured and unstructured data. Data redundancy and conflicts are eliminated through cleaning, desensitization, and fusion technologies, generating unified data standards. For example, the unit for carbon emission intensity is standardized as tCO2e / MWh, and the time granularity is standardized to the hourly level. Simultaneously, data sharing is established, and a cross-entity data interaction channel is built based on privacy computing technology to achieve real-time data synchronization and authorized queries, ensuring consistent data source support for decision-making, rights tracing, and other functions.

[0030] It should be noted that this step involves building an integrated electricity and carbon data platform to achieve standardized processing, real-time flow, and unified management of multi-source data, providing high-quality data input for intelligent applications.

[0031] In a preferred embodiment, establishing a correlation model between carbon dioxide data includes: A model relating carbon emission intensity to power output is constructed to obtain the total actual carbon emissions of the unit by using the unit carbon emission intensity and actual power generation. Construct a multi-constraint collaborative model, which includes electricity supply and demand balance constraints and total carbon emission constraints; An environmental rights quantification model is constructed to obtain the carbon emission reduction of green electricity trading by using the actual green electricity generation and the baseline emission intensity.

[0032] It should be noted that this step is based on power system optimization theory, carbon footprint tracing technology and intelligent decision-making algorithms to quantify the correlation between electricity and carbon data. That is, it uses mathematical models to quantify the technical correlation between unit power generation and carbon emissions, providing a data foundation for intelligent decision-making.

[0033] Specifically, in the carbon emission intensity-output correlation model, let the actual total carbon emissions of unit i be... The carbon emission intensity per unit (Unit: tCO2e / MWh) and actual power generation (Unit: MWh) is directly related; this model only reflects the physical emission characteristics of the unit, and is expressed as:

[0034] Among them, unit carbon emission intensity It is calculated as the carbon emissions from coal combustion per unit time divided by the electricity generated.

[0035] A multi-constraint collaborative model is constructed to establish a dual constraint relationship at the technical level. The power supply and demand technical balance constraint (based on the physical constraints of grid security operation) is expressed as follows:

[0036] in, This indicates the electricity load demand, which can be obtained from historical electricity load demand and newly planned major load demand. The total carbon emission technology constraint (the upper limit constraint on technology based on emission reduction targets) is expressed as:

[0037] in, This indicates the carbon quota for electricity allocation.

[0038] In a preferred embodiment, the environmental rights quantification model is expressed as follows:

[0039] in, This represents the carbon emission reduction from green electricity trading. This represents the baseline emission intensity; the model can reflect differences in emission reductions. Indicates carbon emission intensity per unit. This indicates the actual power generation of the unit.

[0040] In a preferred embodiment, constructing a large-scale collaborative decision-making model for electricity and carbon emissions includes: A dual-objective optimization function and inter-provincial power transmission constraints were constructed, and the model was trained using carbon dioxide data. An optimization weighting factor is introduced, which is automatically optimized based on the achievement of emission reduction targets. That is, the value of the optimization weighting factor is automatically adjusted according to the deviation between the actual emission reduction and the target emission reduction. If the actual emission reduction is lower than the target emission reduction, the optimization weighting factor is increased to strengthen the output priority of low-carbon units. If the actual emission reduction is higher than the target emission reduction, the optimization weighting factor is decreased.

[0041] It should be noted that the large-scale collaborative decision-making model for electricity carbon emissions is introduced. Using electricity carbon data parameters as input, it outputs the optimal unit output scheme through deep learning. The electricity carbon data parameters include unit parameters and system operating parameters. The unit parameters are power generation and carbon emission intensity per unit. The system operating parameters are the electricity demand values. Annual carbon allowance total Parameters are dynamically adjusted, and optimization weighting factors are introduced. The large model automatically optimizes based on the progress of emission reduction targets. When emission reduction progress lags behind, Increase the weight of carbon emission intensity in the optimization process.

[0042] In a preferred embodiment, a technology-oriented, bi-objective optimization function-based large-scale model for coordinated electric-carbon decision-making is constructed and automatically solved by the large-scale model. The large-scale electric-carbon coordinated decision-making model is expressed as follows:

[0043] in, Indicates the basic cost of electricity generation. This indicates the actual power generation of the generating unit. Indicates the cost of generating electricity from the unit. This represents the impact coefficient of carbon emission reduction technology, dynamically adjusted based on regional carbon market costs and the maturity of power system emission reduction technologies. The initial reference value is 1.2-1.5. If the regional carbon price increases by 5%, it will be adjusted upwards by 0.1; if the iteration of emission reduction technologies reduces the unit emission reduction cost by 10%, it will be adjusted downwards by 0.08. This indicates the optimization weight factor (initial value set to 0.6). It represents the intensity of carbon emissions per unit.

[0044] The newly added constraint condition for inter-provincial power transmission technology is expressed as follows: ,in, Indicates inter-provincial transaction volume. The transmission loss technical coefficient is set based on the physical characteristics of the power grid and is optimized. Lowering the technical barriers to inter-provincial power transmission, the final output is the optimal power output scheme for the generating units. This involves verifying whether the output meets the constraints; if not, the model optimization is repeated, and adjustments are made accordingly. and Re-optimize; if satisfied, calculate the deviation between the actual emission reduction and the target emission reduction, and update the next round based on the deviation value. The initial value is used to form an adaptive adjustment mechanism to ensure that the algorithm stays aligned with emission reduction targets in the long term.

[0045] Specifically, the deviation calculation formula is deviation rate δ = (actual emission reduction - target emission reduction) / target emission reduction × 100%; when δ < 0, the weighting factor is increased by Δω = 0.05 × |δ| (the single increase shall not exceed 10% of the initial value, and the cumulative maximum increase shall be 40% of the initial value); when δ > 0, the weighting factor is decreased by Δω = 0.03 × δ (the single decrease shall not exceed 8% of the initial value, and the cumulative minimum decrease shall be 20% of the initial value).

[0046] In a preferred embodiment, a blockchain hash identifier is generated for each green electricity transaction, and automatic contract verification is performed, including: Blockchain hash identifier Represented as:

[0047] Where t is the timestamp, i is the unit code, and H represents the hash function. This represents the carbon emission reduction from green electricity trading. This indicates the actual power generation of the generating unit; Set up a rights-related verification contract. When green electricity rights are used for green certificates or carbon offsets, the contract will be executed automatically. By comparing on-chain data, it will ensure that the carbon emission reduction corresponding to the blockchain hash identifier has not been reused. After the verification is passed, the contract will automatically mark the blockchain hash identifier as used and update the carbon quota offset record in a synchronous manner.

[0048] Specifically, blockchain technology is used to achieve full-process technical evidence storage of environmental rights. The generated identification information is written into the blockchain distributed ledger to ensure immutability, and finally outputs carbon emission verification reports and blockchain rights evidence storage data.

[0049] It should be noted that the beneficial effects of the present invention are as follows: First, we will strengthen the transmission of carbon emission technology constraints. Through large-scale intelligent optimization algorithms, we will directly link carbon emission intensity with unit output, prioritize the scheduling of new energy and low-emission units, and force the transformation of high-energy-consuming units from a technical perspective, promote the consumption of renewable energy, and accelerate the decarbonization of the power system.

[0050] Second, improve the efficiency of cross-system data interaction. Relying on the integrated electricity and carbon data platform and privacy computing technology, we can achieve standardized cleaning, encrypted sharing and real-time synchronization of electricity and carbon emission data, reduce the repeated data collection and verification operations across departments, reduce the cost of enterprise technology docking and improve data flow efficiency.

[0051] Third, it ensures the credible traceability of environmental rights. Through blockchain hash identification and smart contract technology, it generates tamper-proof records for the transfer of green electricity emission reductions and carbon quotas, thereby technically realizing the uniqueness verification of rights and full-chain traceability, eliminating the problem of duplicate measurement, and maintaining the fairness of the market at the technical level.

[0052] Fourth, strengthen cross-market technology risk prevention and control. Based on real-time data monitoring and large-scale model early warning algorithms, dynamically identify the risk of deviation between unit output and carbon emissions. Buffer compliance risks through technical output scheme adjustments (such as limiting the output of units exceeding the quota) and improve the stability and anti-interference capability of system operation.

[0053] Fifth, we will promote the coordinated implementation of technical standards. By unifying data interfaces, rights and interests preservation rules, and optimizing algorithm logic, we will achieve coordinated operation of technical systems such as power dispatching and carbon emission monitoring, forming a synergy at the technical level, strengthening the technical support capabilities of enterprises for low-carbon transformation, and improving the technical efficiency of achieving emission reduction targets.

[0054] The above is an illustrative scheme of a blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions according to this embodiment. It should be noted that the technical solution of this blockchain-based collaborative data governance and intelligent optimization system for electricity and carbon emissions belongs to the same concept as the technical solution of the aforementioned blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions. Details not described in detail in the technical solution of the blockchain-based collaborative data governance and intelligent optimization system for electricity and carbon emissions in this embodiment can be found in the description of the technical solution of the aforementioned blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon emissions.

[0055] like Figure 3 and Figure 4 As shown, this embodiment also provides a blockchain-based collaborative data governance and intelligent optimization system for electricity and carbon emissions, including: The data acquisition and processing module is used to acquire multi-source data of the electrocarbon battery in real time and perform data preprocessing. The first construction module is used to establish a correlation model between the processed multi-source carbon data of electricity generation in order to quantify the correlation between unit power generation and carbon emissions, and to obtain carbon emission reduction data. The second construction module is used to build a large-scale collaborative decision-making model for electricity carbon based on multi-source data on electricity carbon and the correlation between unit power generation and carbon emissions, and output the optimal unit output scheme through deep learning. The optimized monitoring module generates a blockchain hash identifier for each green electricity transaction based on carbon emission reduction data and performs automatic contract verification. If a performance risk is detected, an early warning is triggered and optimization suggestions are sent.

[0056] Specifically, the overall architecture of the electricity-carbon collaborative data governance and intelligent optimization system, based on the collaborative operation of the electricity-carbon market, adopts a layered and progressive design. Through tool empowerment in the technology support layer, information integration in the data governance layer, and functional implementation in the intelligent application layer, it achieves technical support for data interaction, rights traceability, and decision optimization between the electricity and carbon markets. Simultaneously, core technologies such as blockchain notarization and large-scale model optimization are embedded in each stage to ensure the system's efficiency, security, and low-carbon orientation. This architecture, from foundation to core, is divided into a technology support layer, a data governance layer, and an intelligent application layer. Each layer forms an organic whole through technical interfaces and data links, jointly supporting the technical implementation of the entire electricity-carbon collaborative process.

[0057] The technical support layer, as the underlying technical carrier of the system, provides three core technical supports: blockchain, large-scale models, and privacy computing. It provides technical tools to ensure the upper-layer data governance and application functions. The blockchain platform adopts a consortium blockchain architecture, deploying distributed ledgers and smart contracts. It is responsible for the full lifecycle storage and tamper-proof traceability of electricity carbon rights (green electricity emission reductions and carbon quotas), and supports the access and consensus verification of multiple entities (power generation companies, trading centers, and regulatory agencies). The large-scale model engine is a collaborative decision-making model trained based on power system simulation data and historical carbon market data. It has dynamic optimization and trend prediction functions, providing intelligent algorithm support for unit output schemes and emission reduction target adaptation. It integrates federated learning and homomorphic encryption technology to achieve cross-market data sharing that is "usable but not visible." While protecting the privacy of carbon emission data of power generation companies and sensitive information of trading centers, it supports collaborative data analysis of multiple entities.

[0058] As a key link between the technology support layer and the intelligent application layer, the data governance layer constructs an integrated data platform for electricity and carbon, realizing standardized processing, real-time flow and unified management of multi-source data, and providing high-quality data input for intelligent applications.

[0059] The intelligent application layer, as the core unit for the implementation of system functions, consists of aspects such as rights traceability, collaborative optimization, and performance supervision. It relies on the tools of the technical support layer and the data of the data governance layer to achieve a functional closed loop. Specifically, during rights traceability, a unique identifier (hash ID) for electricity carbon rights is generated based on the blockchain platform, linked to green electricity trading records, carbon quota change data, and emission reduction calculation results. Smart contracts automatically verify the relevance of rights (e.g., the same green electricity emission reduction cannot be used repeatedly for green certificates and carbon quota offsets), outputting an immutable rights traceability report. A collaborative optimization mode is adopted, using real-time data from the data governance layer as input, and calling a large-scale model AI engine to run. The multi-objective optimization algorithm comprehensively considers the balance of power supply and demand, total carbon quota constraints, and emission reduction targets, and outputs the optimal power output plan for generating units (such as prioritizing the dispatch of new energy generating units and restricting the over-quota output of high-energy-consuming units), providing a technical decision-making basis for transaction matching; the performance supervision rules collect performance progress data (power delivery volume, quota clearance volume) from the data governance layer in real time, and verify the deviation between actual performance and plan through the rule engine (such as whether the product of power generation and carbon emission intensity is within the quota range). When performance risks are detected, early warnings are triggered and optimization suggestions are pushed (such as predicting quota gaps through large models and prompting enterprises to make adjustments in advance), ensuring the continuity of performance.

[0060] This embodiment also provides a computer device applicable to a blockchain-based method for collaborative data governance and intelligent optimization of electricity and carbon emissions, including: The system comprises a memory and a processor; the memory stores computer-executable instructions, and the processor executes these instructions to implement a blockchain-based method for collaborative data governance and intelligent optimization of electricity and carbon emissions, as proposed above.

[0061] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon as described above.

[0062] The storage medium proposed in this embodiment and the implementation of a blockchain-based collaborative data governance and intelligent optimization method for electricity and carbon based proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0063] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0064] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A blockchain-based electric-carbon collaborative data governance and intelligent optimization method, characterized in that, include: Real-time acquisition of multi-source data from electrocarbon dioxide and data preprocessing; Based on the processed multi-source data of electricity carbon, a correlation model between the electricity carbon data is established to quantify the correlation between unit power generation and carbon emissions, and to obtain carbon emission reduction data. Based on the aforementioned multi-source data on electricity carbon and the correlation between unit power generation and carbon emissions, a large-scale collaborative decision-making model for electricity carbon is constructed using a dual-objective optimization function, and the optimal unit output scheme is output through deep learning. Based on the carbon emission reduction data, a blockchain hash identifier is generated for each green electricity transaction, and the contract is automatically verified. If a performance risk is detected, an early warning is triggered and optimization suggestions are sent.

2. The blockchain-based electric-carb on-collaborative data governance and intelligent optimization method of claim 1, wherein, Real-time acquisition of multi-source data from electrocarbon dioxide sources, and data preprocessing including: The multi-source data on electricity carbon includes electricity data, carbon market data, and external data. The data preprocessing includes data cleaning, data desensitization, and data fusion to eliminate data redundancy and conflicts and generate unified data standards.

3. The blockchain-based electric-carb on-collaborative data governance and intelligent optimization method of claim 1, wherein, Establishing a correlation model between carbon data includes: A model relating carbon emission intensity to power output is constructed to obtain the total actual carbon emissions of the unit by using the unit carbon emission intensity and actual power generation. A multi-constraint collaborative model is constructed, which includes electricity supply and demand balance constraints and total carbon emission constraints. An environmental rights quantification model is constructed to obtain the carbon emission reduction of green electricity trading by using the actual green electricity generation and the baseline emission intensity.

4. The blockchain-based electric-carb on-collaborative data governance and intelligent optimization method of claim 1, wherein, The construction of a large-scale collaborative decision-making model for electricity and carbon includes: A dual-objective optimization function and inter-provincial power transmission constraints were constructed, and the model was trained using carbon dioxide data. An optimization weighting factor is introduced, which is automatically optimized based on the achievement of emission reduction targets. That is, the value of the optimization weighting factor is automatically adjusted according to the deviation between the actual emission reduction and the target emission reduction. If the actual emission reduction is lower than the target emission reduction, the optimization weighting factor is increased to strengthen the output priority of low-carbon units. If the actual emission reduction is higher than the target emission reduction, the optimization weighting factor is decreased.

5. The blockchain-based electric-carb on-collaborative data governance and intelligent optimization method of claim 4, wherein, The large-scale collaborative decision-making model for electricity and carbon is represented as follows: ； wherein, represents the base generation cost, represents the actual generation of the unit, represents the carbon emission technology impact coefficient, represents the optimization weight factor, represents the unit carbon emission intensity.

6. The blockchain-based method for collaborative data governance and intelligent optimization of electricity and carbon emissions as described in claim 3, characterized in that, The environmental rights quantification model is expressed as follows: ； in, This represents the carbon emission reduction from green electricity trading. Indicates the baseline emission intensity. Indicates carbon emission intensity per unit. This indicates the actual power generation of the unit.

7. The blockchain-based method for collaborative data governance and intelligent optimization of electricity and carbon emissions as described in claim 1, characterized in that, Generate a blockchain hash identifier for each green electricity transaction and perform automatic contract verification, including: The blockchain hash identifier Represented as: ； Where t is the timestamp, i is the unit code, and H represents the hash function. This indicates the actual power generation of the generating unit. This indicates the carbon emission reduction from green electricity trading; Set up a rights-related verification contract. When green electricity rights are used for green certificates or carbon offsets, the contract will be executed automatically. By comparing on-chain data, it will ensure that the carbon emission reduction corresponding to the blockchain hash identifier has not been reused. After the verification is passed, the contract will automatically mark the blockchain hash identifier as used and update the carbon quota offset record in a synchronous manner.

8. A blockchain-based system for collaborative data governance and intelligent optimization of electricity and carbon emissions, employing the blockchain-based method for collaborative data governance and intelligent optimization of electricity and carbon emissions as described in any one of claims 1 to 7, characterized in that... include: The data acquisition and processing module is used to acquire multi-source data of the electrocarbon battery in real time and perform data preprocessing. The first construction module is used to establish a correlation model between the electricity carbon data based on the processed electricity carbon multi-source data, so as to quantify the correlation between unit power generation and carbon emissions, and obtain carbon emission reduction data. The second construction module is used to construct a large-scale collaborative decision-making model for electricity carbon based on the multi-source data of electricity carbon and the correlation between unit power generation and carbon emissions, using a dual-objective optimization function, and output the optimal unit output scheme through deep learning; The optimization monitoring module is used to generate a blockchain hash identifier for each green electricity transaction based on the carbon emission reduction data, and to automatically verify the contract. If a performance risk is detected, an early warning is triggered and optimization suggestions are sent.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the blockchain-based electric carbon collaborative data governance and intelligent optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed by a processor, implement the steps of the blockchain-based electrocarbon collaborative data governance and intelligent optimization method as described in any one of claims 1 to 7.

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