Electric power transaction intelligent algorithm platform based on block chain consensus

Abstract

The invention relates to the field of electricity transaction, in particular to an electricity transaction intelligent algorithm platform based on block chain consensus. The method comprises the following steps: acquiring a real-time green electricity encrypted data set, uploading the real-time green electricity encrypted data set to a block chain node, and generating an unactivated digital green certificate data set; based on the non-activated digital green certificate data set, generating a grid-connected verification instruction, updating the non-activated digital green certificate data set to a tradable state, and generating a tradable digital green certificate data set; obtaining a power grid dynamic parameter set, and generating a green certificate time pricing data set through a space-time right dynamic pricing strategy based on the power grid dynamic parameter set and the tradable digital green certificate data set; and according to the green certificate time pricing data set, the digital green certificate is cancelled after verification in the power utilization process of the user, and a green power consumption traceability report is output. In the green electricity transaction process, fair and orderly development of the green electricity transaction market is promoted.

Description

Technical Field

[0001] This application relates to the field of power trading, and in particular to an intelligent algorithm platform for power trading based on blockchain consensus. Background Art

[0002] The green electricity value adjustment strategy of the existing green electricity trading platform has fundamental flaws and cannot be dynamically adjusted according to the influencing factors generated by green electricity resources during the platform transaction process, resulting in a serious deviation between the price of green electricity resources and the true value of energy. Summary of the Invention

[0003] The present application provides an intelligent algorithm platform for electricity trading based on blockchain consensus to solve the above-mentioned technical problems. The method includes: obtaining a real-time green electricity encrypted data set, uploading the real-time green electricity encrypted data set to a blockchain node, and generating an unactivated digital green certificate data set; based on the unactivated digital green certificate data set, generating a grid connection verification instruction, updating the unactivated digital green certificate data set to a tradable state, and generating a tradable digital green certificate data set; obtaining a power grid dynamic parameter set, and based on the power grid dynamic parameter set and the tradable digital green certificate data set, generating a green certificate real-time pricing data set through a spatiotemporal equity dynamic pricing strategy; according to the green certificate real-time pricing data set, the digital green certificate is cancelled during the user's electricity consumption process, and a green electricity consumption traceability report is output.

[0004] Through this solution, we first obtain the encrypted real-time green electricity data from the intelligent monitoring equipment of the distributed green power station and upload it to the blockchain node to generate an unactivated digital green certificate containing basic information of green electricity but without trading qualifications; then the power grid company verification node generates a grid connection instruction according to the standard, and after verification by the smart contract, the unactivated green certificate is updated to a tradable green certificate with a compliance mark; then the real-time parameters of the power grid are obtained, combined with the tradable green certificate, and a real-time pricing data set is generated through a spatiotemporal equity strategy; finally, after the user purchases electricity, the smart contract cancels the green certificate, and the blockchain node integrates the data of the entire process to output a consumption traceability report and send it to the user. Real-time encryption and blockchain storage ensure the authenticity and security of green electricity data, solving the problems of data falsification and privacy leakage in traditional transactions; automated grid connection verification improves transaction efficiency and compliance, unifies standards and shortens cycles, and ensures grid security; dynamic pricing of spatiotemporal rights optimizes green electricity resource allocation, flexibly responds to market supply and demand to increase the renewable energy absorption rate; green certificate verification mechanisms and absorption traceability reports enhance market trust and participation, eliminate fraud, and clearly present the entire green electricity process; ultimately, it helps the low-carbon transformation of the energy structure and promotes the fair and orderly development of the green electricity trading market.

[0005] Optionally, the real-time green electricity encrypted data set is uploaded to the blockchain node to generate an unactivated digital green certificate data set, including: the real-time green electricity encrypted data set includes a power generation data stream, millisecond-level precision timestamp data and geographic coordinate data of the green electricity generator set; according to the real-time green electricity encrypted data set, a preset green certificate generation verification logic is triggered: based on the millisecond-level precision timestamp data, the fluctuation characteristics of the power generation data stream in the corresponding time interval are analyzed to see whether they comply with the preset green electricity output mode; based on the geographic coordinate data, whether the green electricity generator set is located in the preset trusted green electricity area list; when the fluctuation characteristics of the power generation data stream comply with the green electricity output mode and the green electricity generator set is located in the trusted green electricity area list, the blockchain node determines that the verification is successful; after the blockchain node verifies that it is successful, the real-time green electricity encrypted data set is encapsulated into a real-time green electricity encrypted data unit with a unique identifier, and the real-time green electricity encrypted data unit is recorded in the blockchain corresponding to the blockchain node to generate the unactivated digital green certificate data set.

[0006] Through this solution, the fluctuation characteristics of the power generation data flow and the geographic coordinates are double-verified, and false green electricity data is filtered out from the source to ensure that the generated inactivated digital green certificate corresponds to the real green electricity production process, providing a reliable certificate basis for subsequent green electricity transactions and reducing transaction disputes caused by false data; utilizing the distributed storage and tamper-proof characteristics of blockchain nodes, the verified green electricity data is permanently recorded in the ledger, combined with millisecond-level timestamps and geographic coordinates to achieve traceability of the entire process from green electricity production to green certificate generation, meeting the regulatory authorities' verification needs for green electricity compliance, and providing users with transparent green electricity source information.

[0007] Optionally, based on the inactivated digital green certificate dataset, a grid connection verification instruction is generated, the inactivated digital green certificate dataset is updated to a tradable state, and a tradable digital green certificate dataset is generated, including: NFT encoding of each green certificate in the inactivated digital green certificate dataset, and generation of a grid connection verification instruction including the green certificate NFT code, the corresponding cumulative power generation value, geographic location information and the planned grid connection time point; according to the grid connection verification instruction, analyzing whether the cumulative power generation value is physically transmittable at the planned grid connection time point; when the cumulative power generation value is transmittable, returning a verification pass signal to the blockchain node; the blockchain node responds to the verification pass signal and updates the status attribute corresponding to the green certificate NFT code from inactivated to tradable; based on all the green certificate NFT codes whose status is updated to tradable, the tradable digital green certificate dataset is generated.

[0008] Through this solution, based on the verification of physical transmission feasibility, the green electricity corresponding to the tradable green certificate will have the actual grid-connected capability, avoiding the transaction of "virtual green certificate", ensuring that the environmental value of green electricity obtained by the buyer is supported by real power delivery, and enhancing the credibility of green electricity transactions; the green certificate NFT coding is combined with blockchain technology to fundamentally eliminate problems such as green certificate forgery and repeated transactions, provide the green electricity market with transparent and traceable digital certificates, and maintain fair competition order in the market; based on the feasibility analysis of the planned grid-connected time point and grid load, the green electricity grid-connected plan is matched with the grid dispatching demand, reducing power abandonment, improving the green electricity absorption rate, and promoting the efficient use of new energy.

[0009] Optionally, the green certificate real-time pricing data set is generated based on the grid dynamic parameter set and the tradable digital green certificate data set through a spatiotemporal equity dynamic pricing strategy, including: determining the basic layer value based on the product of the accumulated power generation value and the preset unit fixed emission reduction amount, and assigning the basic layer value to each of the green certificate NFT codes in the tradable digital green certificate data set; analyzing the grid dynamic parameter set to obtain the dynamic layer value adjustment factor, determining the dynamic layer value, and assigning the dynamic layer value to each of the green certificate NFT codes in the tradable digital green certificate data set; multiplying the basic layer value of each green certificate NFT code by the corresponding dynamic layer value adjustment factor to generate the real-time equity price of the green certificate NFT code; and aggregating the real-time equity prices of all the green certificate NFT codes to generate the green certificate real-time pricing data set.

[0010] Through this plan, the basic layer value converts the environmental emission reduction contribution of green electricity into a tradable value indicator, and the dynamic layer value adjustment factor reflects the actual utility of green electricity under different time and space conditions. The combination of the two enables the green certificate price to reflect the environmental attributes and adapt to market dynamics, realizing the unity of "green value" and "market value"; the time and space rights dynamic pricing strategy guides green electricity to be consumed first in key scenarios such as peak power periods and load centers through price signals, reducing the phenomenon of green electricity abandonment. At the same time, the price is lowered in valley power periods or areas with loose supply and demand, encouraging users to consume more, balancing the grid load and improving the stability of grid operation.

[0011] Optionally, the analysis of the grid dynamic parameter set to obtain the dynamic layer value adjustment factor includes: the grid dynamic parameter set includes the real-time total output value of the regional grid, the real-time grid congestion level and the real-time grid load status; analyzing the real-time total output value of the regional grid to determine the real-time environmental increase / decrease coefficient of the regional grid cleanliness corresponding to the green certificate NFT code to the basic layer value; analyzing the real-time grid congestion level to determine the spatial compensation coefficient required for the equivalent transmission loss from the geographical location corresponding to the green certificate NFT code to the target load center; analyzing the real-time grid load status to determine the time increase / decrease coefficient required for the current moment to be in the peak or valley period of the grid; multiplying the real-time environmental increase / decrease coefficient, the spatial compensation coefficient and the time increase / decrease coefficient to generate the dynamic layer value adjustment factor of each green certificate NFT code.

[0012] Through this plan, by comprehensively considering the real-time environment of the power grid, spatial transmission costs and temporal load status, the price of green certificates is no longer limited to the static basic environmental value, but can reflect market dynamics in real time, more accurately match the actual value of green electricity, and avoid resource mismatch caused by pricing deviations; the environmental coefficient dynamically responds to changes in the carbon intensity of the power grid, thereby increasing the premium rate of green certificates during high-pollution periods and truly reflecting the marginal benefits of emission reduction; the introduction of the spatial compensation coefficient balances the transmission cost of long-distance green electricity, improves the market competitiveness of clean energy in remote areas, promotes the efficient flow of green electricity from the production end to the load center, and optimizes the configuration range of clean energy; the time coefficient makes green certificates more valuable during peak hours and more price-competitive during off-peak hours, encouraging users to consume more green electricity during off-peak hours and reasonably control electricity consumption during peak hours, thereby alleviating the peak-valley load difference of the power grid and improving the stability of power grid operation.

[0013] Optionally, the analysis of the real-time total output value of the regional power grid and the determination of the real-time environmental gain / loss coefficient of the cleanliness of the regional power grid corresponding to the green certificate NFT code on the base layer value include: determining the real-time green power output value of the green power generator in the current period according to the real-time total output value of the regional power grid and the power generation data stream; determining the real-time cleanliness percentage of the regional power grid according to the ratio of the real-time green power output value to the real-time total output value of the regional power grid; comparing the real-time cleanliness percentage of the regional power grid with the preset cleanliness benchmark threshold according to a preset cleanliness reference threshold, judging the deviation direction of the real-time cleanliness percentage of the regional power grid relative to the preset cleanliness benchmark threshold, and determining the deviation amplitude value; when the real-time cleanliness percentage of the regional power grid is higher than the preset cleanliness benchmark threshold, generating a real-time environmental gain coefficient with a value greater than 1, and dynamically adjusting to increase the real-time environmental gain coefficient according to the deviation amplitude value; when the real-time cleanliness percentage of the regional power grid is lower than the preset cleanliness benchmark threshold, generating a real-time environmental loss coefficient with a value less than 1, and dynamically adjusting to decrease the real-time environmental loss coefficient according to the deviation amplitude value.

[0014] Through this solution, the value of green certificates is dynamically linked to the cleanliness of the regional power grid through real-time environmental increase / decrease coefficients, accurately reflecting the actual environmental contribution of green electricity in different power grid environments and avoiding value distortion; guiding green electricity generators to areas with low cleanliness, maximizing the emission reduction benefits of green electricity, while avoiding excessive green electricity investment in areas with high cleanliness, and improving the overall utilization efficiency of energy resources.

[0015] Optionally, the analysis of the real-time grid congestion level and the determination of the spatial compensation coefficient required for the equivalent transmission loss from the geographical location corresponding to the green certificate NFT code to the target load center include: extracting the geographical location information of the target load center according to the grid connection verification instruction; analyzing the tolerance threshold of the current grid congestion state to the power transmission distance based on the real-time grid congestion level, and dynamically generating the maximum effective transmission radius; determining the actual spatial distance from the green power generator group to the target load center according to the geographical coordinate data of the green power generator group and the geographical location information of the target load center; dynamically comparing the actual spatial distance with the maximum effective transmission radius: when the actual spatial distance is less than or equal to the maximum effective transmission radius, generating the spatial compensation coefficient with a reference value of 1; when the actual spatial distance is greater than the maximum effective transmission radius, according to the ratio of the excess distance to the maximum effective transmission radius, according to the preset nonlinear attenuation rule, with the value 1 as the reference, dynamically lowering the spatial compensation coefficient.

[0016] Through this plan, the dynamic adjustment of the spatial compensation coefficient can be used to guide green electricity transactions towards lower losses and higher efficiency: for green electricity that is close to the load center and has little impact on grid congestion, its spatial compensation coefficient is better and the green certificate price is more competitive, so it is consumed first; for green electricity that is far away but has alleviated grid congestion, its compensation coefficient is increased and it can also obtain reasonable pricing; this efficiency-first, dynamic balance mechanism can promote the precise matching of green electricity resources and load demand, reduce ineffective transmission losses, improve the overall green electricity consumption efficiency, and assist in the transformation of the energy structure.

[0017] Optionally, the analysis of the real-time grid load status and determination of the time gain / decrease coefficient required for the current moment to be in the peak or valley period of the grid includes: extracting the precise millisecond time point corresponding to green electricity generation based on the timestamp data with millisecond precision; determining the real-time load value corresponding to the precise millisecond time point based on the real-time grid load status and the precise millisecond time point; dynamically evaluating the change trend of the real-time load value within a preset time window to determine the direction of load change: when the load change direction indicates that the load continues to rise, generating a time gain coefficient with a value greater than 1, and dynamically adjusting and increasing the time gain coefficient based on the deviation amplitude value of the number of real-time load values ​​that continuously rise within the preset time window and the preset rise intensity threshold; when the load change direction indicates that the load continues to fall, generating a time loss coefficient with a value less than 1, and dynamically adjusting and decreasing the time loss coefficient based on the deviation amplitude value of the number of real-time load values ​​that continuously fall within the preset time window and the preset fall intensity threshold.

[0018] Through this solution, based on millisecond-level timestamps and real-time load status analysis, the time coefficient can match the instantaneous changes in grid load in real time, avoiding pricing deviations caused by coarse time granularity and allowing the value of green electricity at different time points to be truly reflected. By distinguishing the direction of load changes and combining the deviation amplitude value adjustment coefficient, it ensures that the power generator's income matches the actual contribution of green electricity to the grid. At the same time, it also allows electricity users to obtain reasonable green electricity prices based on the actual load status, balancing the interests of both parties to the transaction.

[0019] Optionally, according to the green certificate real-time pricing data set, the digital green certificate is cancelled during the user's electricity consumption process, and a green electricity consumption traceability report is output, including: obtaining the target user's cancellation request for the specific green certificate NFT code in the green certificate real-time pricing data set, and analyzing the target cancellation electricity value specified in the cancellation request; verifying whether the current status of the specific green certificate NFT code is a tradable state; when the verification passes, based on the target cancellation electricity value, marking the cumulative power generation value corresponding to the specific green certificate NFT code as cancelled on the blockchain node; collecting the real-time equity prices, corresponding cumulative power generation values, consumption time points and the geographical location information of all cancelled green certificate NFT codes to generate a green electricity consumption traceability report.

[0020] Through this solution, the closed-loop integrity of green electricity transactions is guaranteed, and the entire process of green certificates from generation, pricing to final consumption by users is closed, ensuring that green electricity rights and interests can be effectively converted into users' actual electricity needs, and promoting a virtuous cycle in the green electricity market; the clarity and security of rights and interests are improved, and the risk of repeated trading and misappropriation of green electricity rights and interests is avoided through the status marking and tamper-proof characteristics on the blockchain, and the ownership of each written-off right is clarified, reducing transaction disputes; supervision and compliance support are strengthened, and the green electricity consumption traceability report provides regulatory authorities with traceable and transparent green electricity consumption data, meeting the regulatory requirements of environmental protection policies and energy regulations on green electricity transactions, and helping to achieve clean energy development goals.

[0021] Optionally, the platform also includes: based on the green electricity consumption traceability report, when completing the transaction settlement, linking the real-time exchange rate data of the carbon market; dynamically calculating the carbon quota tonnage that can be exchanged for the tradable digital green certificate represented by the green certificate NFT code of the unit according to the real-time exchange rate data of the carbon market; on the blockchain node, according to the preset exchange ratio, the green certificate rights corresponding to the green certificate NFT code of the completed transaction are transferred to the corresponding carbon quota tonnage, and recorded in the transaction party's account in the carbon market.

[0022] Through this plan, green certificate rights will be converted into carbon quotas, so that the environmental value of green electricity can be directly reflected in economic value. Enterprises that purchase green electricity can not only obtain electricity energy, but also obtain additional income through carbon quota trading, thereby encouraging more enterprises to participate in green electricity trading and expand the scale of green electricity consumption; breaking the separation of the two markets, forming a complete value chain of "green electricity production-green certificate trading-carbon quota conversion", promoting the cross-market flow of environmental rights and interests, and realizing the synergistic effect of "1+1>2". BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0024] Figure 1 A schematic diagram of an application scenario provided in one embodiment of the present application; Figure 2 A flowchart of an intelligent algorithm platform for power trading based on blockchain consensus is provided in accordance with one embodiment of the present application. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0027] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0028] However, the green electricity value adjustment strategy of the existing green electricity trading platform has fundamental flaws and cannot be dynamically adjusted according to the influencing factors generated by green electricity resources during the platform transaction process, resulting in a serious deviation between the price of green electricity resources and the true value of energy.

[0029] Based on this, this application provides an intelligent algorithm platform for power trading based on blockchain consensus. First, the real-time green electricity data encrypted by the intelligent monitoring equipment of the distributed green power station is obtained and uploaded to the blockchain node to generate an unactivated digital green certificate containing basic green electricity information but no trading qualifications; then the grid company verification node generates a grid connection instruction according to the standard, and after verification by the smart contract, the unactivated green certificate is updated to a tradable green certificate with a compliance mark; then the real-time parameters of the power grid are obtained, combined with the tradable green certificate, and a real-time pricing data set is generated through a spatiotemporal equity strategy; finally, after the user purchases electricity, the smart contract cancels the green certificate, and the blockchain node integrates the full process data to output the consumption traceability report and send it to the user. Real-time encryption and blockchain storage ensure the authenticity and security of green electricity data, solving the problems of data falsification and privacy leakage in traditional transactions; automated grid connection verification improves transaction efficiency and compliance, unifies standards and shortens cycles, and ensures grid security; dynamic pricing of spatiotemporal rights optimizes green electricity resource allocation, flexibly responds to market supply and demand to increase the renewable energy absorption rate; green certificate verification mechanisms and absorption traceability reports enhance market trust and participation, eliminate fraud, and clearly present the entire green electricity process; ultimately, it helps the low-carbon transformation of the energy structure and promotes the fair and orderly development of the green electricity trading market.

[0030] Figure 1 This is a schematic diagram of an application scenario provided by this application. During green electricity trading, the method provided by this application can dynamically adjust the price of green electricity resources based on the influencing factors generated during platform transactions, accurately controlling the true value of green electricity resources and energy.

[0031] Specifically, the method of the present application is applied to any server, which communicates with the new energy power station monitoring system and the power dispatching system, and obtains the real-time green electricity encrypted data set provided by the new energy power station monitoring system and the power grid dynamic parameter set provided by the power dispatching system through the server. First, the real-time green electricity data encrypted by the intelligent monitoring equipment of the distributed green power station is obtained and uploaded to the blockchain node to generate an unactivated digital green certificate containing basic green electricity information but no trading qualifications; then the power grid company verification node generates a grid connection instruction according to the standard, and after verification by the smart contract, the unactivated green certificate is updated to a tradable green certificate with a compliance mark; then the real-time parameters of the power grid are obtained, combined with the tradable green certificate, and a real-time pricing data set is generated through a spatiotemporal equity strategy; finally, after the user purchases electricity, the smart contract cancels the green certificate, and the blockchain node integrates the full-process data to output the consumption traceability report and send it to the user. The specific implementation method can refer to the following embodiments.

[0032] Figure 2 This is a flowchart of an intelligent algorithm platform for power trading based on blockchain consensus provided by an embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201. Obtain a real-time green electricity encrypted data set, upload the real-time green electricity encrypted data set to the blockchain node, and generate an unactivated digital green certificate data set.

[0033] A real-time green electricity encrypted dataset refers to a collection of encrypted, real-time data related to green electricity (e.g., renewable energy generation such as solar, wind, and hydropower). This data includes power generation data streams, millisecond-level timestamps, and the geographic coordinates of green electricity generators. This data originates from the monitoring system of new energy power stations. A blockchain node refers to a computer or server participating in a blockchain network, providing data storage, transaction verification, and consensus-building functions. It is the fundamental component of a blockchain network. An inactivated digital green certificate dataset refers to a collection of digital green energy certificates generated based on real-time green electricity encrypted data that has not yet undergone grid-connection compliance verification.

[0034] Specifically, in traditional green electricity trading, the lack of data credibility and traceability is a core bottleneck restricting the development of the industry: on the one hand, key data such as green electricity generation and purity has long relied on manual recording or centralized platform storage, which poses a serious risk of data tampering. In other words, some companies may falsely report renewable energy generation to obtain subsidies, or even disguise thermal power as green electricity, resulting in damage to the fairness of the green electricity market; on the other hand, the lack of encryption protection during data transmission makes the commercial secrets of power generation companies (such as equipment efficiency and power generation plans) easily stolen, affecting the enthusiasm of companies to participate in green electricity trading. In addition, as a special commodity, the green attributes of green electricity need to be traceable throughout the process. However, under the traditional model, data is scattered among multiple entities such as power stations and power grid companies, forming "green electricity data islands". Users find it difficult to verify the authenticity of the green electricity they purchase, resulting in a sluggish consumer willingness. In this step, the intelligent monitoring equipment of distributed green power stations collects data such as power generation and equipment operating status in real time, and after encryption, forms a real-time green power encrypted data set. The power generation enterprise uploads this data set to its blockchain node through an interface. After the node verifies the integrity of the data encryption, it automatically generates an unactivated digital green certificate data set containing a unique identifier and synchronizes it to the multi-node storage of the blockchain network. Generating an unactivated digital green certificate data set gives green electricity a unique digital identity, laying the foundation for subsequent compliance verification and transactions. It solves the pain points of "unreliable data and no basis for traceability" in traditional green power transactions. It is the prerequisite for building a transparent and trustworthy green power market and plays a vital role in promoting the large-scale development of green power transactions.

[0035] S202. Generate a grid connection verification instruction based on the inactivated digital green certificate dataset, update the inactivated digital green certificate dataset to a tradable state, and generate a tradable digital green certificate dataset.

[0036] Grid connection verification instructions can be used to verify whether the green electricity corresponding to an inactivated digital green certificate meets the grid connection standards. They include the green certificate NFT code, the corresponding cumulative power generation value, geographic location information, and the planned grid connection time. A tradable digital green certificate dataset can be a collection of digital green certificates that have been verified for grid connection and are eligible for trading, adding verification pass identification, compliance rating, and other information to the inactivated green certificates.

[0037] Specifically, green electricity must meet strict grid connection standards before being connected to the grid. This is a core prerequisite for ensuring the safe and stable operation of the grid. If green electricity parameters such as voltage and frequency are unstable, grid load fluctuations and even large-scale power outages may occur after connection. Furthermore, some green electricity may be mixed with non-renewable energy (such as coal mixed with biomass power generation). If it were to enter the trading market directly, this would violate the environmental protection properties of green electricity and mislead users. Furthermore, the traditional grid connection verification model has significant drawbacks: first, it relies on manual review, which is extremely inefficient; second, the review standards are inconsistent across regions; and third, the review process is opaque. In this step, the blockchain verification node regularly scans the inactive digital green certificate dataset and generates verification instructions for each green certificate, including the grid connection standard threshold. The smart contract automatically executes the instructions, compares the green electricity data with the threshold, and marks the green certificate as "tradable" after verification. The verification information is added to generate a tradable digital green certificate dataset. "Generate grid connection verification instructions" and "update to tradable status" are automatically completed through blockchain smart contracts without human intervention, shortening the verification cycle to a few hours and greatly improving transaction efficiency. It not only ensures the safe operation of the power grid, but also standardizes the green electricity market access mechanism. It is the only way for green electricity to enter the legal trading link, and is of key significance to maintaining market order and protecting user rights.

[0038] S203. Obtain a grid dynamic parameter set, and based on the grid dynamic parameter set and the tradable digital green certificate data set, generate a green certificate real-time pricing data set through a spatiotemporal equity dynamic pricing strategy.

[0039] The grid dynamic parameter set can be a set of parameters reflecting the real-time operating status of the grid, including the real-time total output of the regional grid, the real-time grid congestion level, and the real-time grid load status. This data is sourced from the power dispatch system. A spatiotemporal equity dynamic pricing strategy can be a strategy that combines time (peak / off-peak periods), space (areas with tight / loose supply and demand), and green electricity environmental equity (emission reductions) to determine real-time prices. The core principle is "higher prices when demand is high, lower prices when supply is high, and premiums when environmental value is high." A green certificate real-time pricing dataset can be a set of real-time prices for tradable digital green certificates generated based on the spatiotemporal equity dynamic pricing strategy.

[0040] Specifically, the value of green electricity has significant temporal and spatial differences and environmental externalities. The traditional fixed pricing model cannot reflect its true value and has become an important obstacle to the consumption of green electricity. From a temporal perspective, the supply value of green electricity during peak electricity consumption periods (such as summer evenings) is much higher than that during off-peak periods, but fixed prices cannot incentivize power generation companies to increase output during peak periods, resulting in a mismatch between supply and demand. From a spatial perspective, the transportation cost of green electricity in power-shortage areas is lower and its practical value is higher, but fixed prices cannot reflect regional differences, resulting in waste of resources. From an environmental perspective, the emission reduction effects of different green electricity sources vary significantly (such as photovoltaic emission reductions are higher than wind power), but fixed prices cannot reflect their environmental value, weakening the production motivation of high-purity green electricity. The rigidity of this pricing mechanism directly leads to: insufficient green electricity supply during peak periods, forcing the power grid to rely on thermal power supplementation, which violates the "carbon reduction" goal; there is an excess of green electricity during off-peak periods, and a large amount of renewable energy is abandoned because it cannot be consumed. This step generates a real-time pricing dataset for green certificates by automatically calculating the price of green certificates (increases during peak hours and decreases during off-peak hours, adjusting for regional supply and demand and environmental value) using a spatiotemporal equity dynamic pricing strategy. This process uses the power grid monitoring system's dynamic parameters, combined with information such as the purity and emission reduction of tradable digital green certificates, to generate a real-time pricing dataset for green certificates. By "obtaining a dynamic grid parameter set," changes in grid supply and demand are captured in real time, providing a data foundation for pricing. The "spatiotemporal equity dynamic pricing strategy" overcomes the limitations of traditional pricing by incorporating time, space, and environmental factors into the model, enabling green certificate prices to flexibly respond to market changes. The combination of these two approaches is a key means of optimizing the allocation of green electricity resources and increasing the absorption rate of renewable energy.

[0041] S204. Based on the green certificate real-time pricing data set, the digital green certificate is verified during the user's electricity consumption process, and a green electricity consumption traceability report is output.

[0042] The green electricity consumption traceability report can be a report that records the entire process of green electricity from power generation, trading to consumption, including the real-time equity price of the green certificate NFT code, the corresponding cumulative value of power generation, the consumption time point and the geographic location information.

[0043] Specifically, the closed-loop management of green electricity transactions relies on a strict rights redemption mechanism and full-process traceability. The traditional model has obvious flaws in these two aspects: On the one hand, if digital green certificates are not cancelled, fraudulent practices such as "selling one certificate to multiple buyers" and "reusing" may occur. In other words, some companies may sell the rights corresponding to the same green electricity multiple times, resulting in users paying fees but not receiving actual green electricity, seriously undermining market trust. On the other hand, the traceability of green electricity relies on manual records or centralized platform archiving. The data is scattered and easy to tamper with. Users cannot confirm the true source and environmental attributes of the green electricity they purchased. For example, they cannot verify whether the electricity corresponding to a green certificate actually comes from a photovoltaic power station or thermal power sold as green electricity. In this step, when a user uses electricity, a confirmation instruction is sent to the blockchain, and the smart contract automatically cancels the corresponding green certificate and marks the status. After cancellation, the blockchain node integrates the data from the entire process to generate a green electricity consumption traceability report and sends it to the user. By verifying the digital green certificate and utilizing the tamper-proof nature of the blockchain, we can ensure that each green certificate can only be used once, technically eliminating the "multiple sales of one certificate" and ensuring the uniqueness and fairness of transactions; the output of the green electricity consumption traceability report integrates the entire process data of power generation, verification, transaction, and verification. Users can clearly view the source, circulation, and environmental contribution of green electricity through the report.

[0044] Through this solution, we first obtain the encrypted real-time green electricity data from the intelligent monitoring equipment of the distributed green power station and upload it to the blockchain node to generate an unactivated digital green certificate containing basic information of green electricity but without trading qualifications; then the power grid company verification node generates a grid connection instruction according to the standard, and after verification by the smart contract, the unactivated green certificate is updated to a tradable green certificate with a compliance mark; then the real-time parameters of the power grid are obtained, combined with the tradable green certificate, and a real-time pricing data set is generated through a spatiotemporal equity strategy; finally, after the user purchases electricity, the smart contract cancels the green certificate, and the blockchain node integrates the data of the entire process to output a consumption traceability report and send it to the user. Real-time encryption and blockchain storage ensure the authenticity and security of green electricity data, solving the problems of data falsification and privacy leakage in traditional transactions; automated grid connection verification improves transaction efficiency and compliance, unifies standards and shortens cycles, and ensures grid security; dynamic pricing of spatiotemporal rights optimizes green electricity resource allocation, flexibly responds to market supply and demand to increase the renewable energy absorption rate; green certificate verification mechanisms and absorption traceability reports enhance market trust and participation, eliminate fraud, and clearly present the entire green electricity process; ultimately, it helps the low-carbon transformation of the energy structure and promotes the fair and orderly development of the green electricity trading market.

[0045] In some embodiments, the real-time green electricity encrypted data set includes a power generation data stream, millisecond-level precision timestamp data, and geographic coordinate data of the green electricity generator set; based on the real-time green electricity encrypted data set, the preset green certificate generation verification logic is triggered: based on the millisecond-level precision timestamp data, the fluctuation characteristics of the power generation data stream in the corresponding time interval are analyzed to see whether they conform to the preset green electricity output mode; based on the geographic coordinate data, the green electricity generator set is verified to be located in the preset trusted green electricity area list; when the fluctuation characteristics of the power generation data stream conform to the green electricity output mode and the green electricity generator set is located in the trusted green electricity area list, the blockchain node determines that the verification is successful; after the blockchain node verification is successful, the real-time green electricity encrypted data set is encapsulated into a real-time green electricity encrypted data unit with a unique identifier, and the real-time green electricity encrypted data unit is recorded in the corresponding blockchain node on the blockchain to generate an unactivated digital green certificate data set.

[0046] The power generation data stream can be the sequence of electric energy data generated by a green power generator per unit time, representing the core data reflecting actual green power output. Millisecond-accurate timestamp data can be millisecond-accurate timestamps applied to the power generation data stream at the time of collection, accurately recording the point in time when green power was generated. The geographic coordinate data of a green power generator can be the longitude and latitude of the location of a green power generator (e.g., a photovoltaic power station or wind farm), representing key data identifying the location of green power production. The green power output pattern can be the typical temporal characteristics of power generation during normal operation of different types of green power generators (e.g., photovoltaic or wind farms). The trusted green power area list can be a set of pre-defined, verified legal green power production areas, used to verify that a green power generator is located within a compliant production area. A real-time green power encrypted data unit can be an independent data unit formed by encapsulating a single set of data (including power generation at a specific moment, timestamp, and geographic coordinates) from a real-time green power encrypted data set. It is the basic unit of blockchain storage.

[0047] Specifically, the core premise of green electricity trading is the authenticity of the "green electricity" identity, that is, the traded electricity does come from renewable energy (such as wind power, photovoltaics), rather than non-clean energy such as thermal power. If there is a lack of strict verification and storage mechanism for green electricity data, "fake green electricity" may be mixed into transactions, undermining the fairness of the green electricity market; the unactivated digital green certificate data set is a necessary transition form for digital green certificates from "data" to "tradable assets". Only after verification by blockchain nodes and generation of unactivated digital green certificates can they be converted to a tradable state through subsequent grid connection verification instructions. If this step is skipped and unverified green electricity data is directly used for transactions, it may cause a large amount of invalid or false data to enter the market, increasing transaction risks and the probability of disputes. To address the above issues, in this step, the generator set sensors first collect power generation data, equipment geographic coordinates and millisecond-level precision timestamp data in real time, and generate an encrypted data stream through the hardware encryption module. The data is then transmitted to the edge computing node via the 5G private network, and a real-time green electricity encrypted data set is formed after the device digital signature is attached. Then, the data set is uploaded to the blockchain node, automatically triggering the preset green certificate generation and verification logic, in which the timestamp analysis module extracts the power generation data stream and analyzes its fluctuation characteristics (such as power change rate, intermittent period), compares it with the preset green power output pattern library and outputs a similarity score (such as wind power similarity>90%), and the geographic coordinate verification module The block parses the unit coordinates and performs geo-fence matching with the list of trusted green electricity areas stored in the blockchain; the blockchain node determines that the verification is successful if and only if the timestamp verification score reaches or exceeds the set threshold (such as similarity > 90%) and the coordinates are in the trusted list. The node then generates a unique hash identifier for the data set (such as 0x3a7b...c21d), and packages the hash value, original encrypted data, timestamp, and coordinates into a real-time green electricity encrypted data unit, writes it into a new block of the blockchain, and marks the status as "inactivated"; finally, the blockchain network broadcasts the new block, and each node synchronously stores the data unit to generate a globally traceable but non-tradable inactivated digital green certificate data set.

[0048] Through this solution, the fluctuation characteristics of the power generation data flow and the geographic coordinates are double-verified, and false green electricity data is filtered out from the source to ensure that the generated inactivated digital green certificate corresponds to the real green electricity production process, providing a reliable certificate basis for subsequent green electricity transactions and reducing transaction disputes caused by false data; utilizing the distributed storage and tamper-proof characteristics of blockchain nodes, the verified green electricity data is permanently recorded in the ledger, combined with millisecond-level timestamps and geographic coordinates to achieve traceability of the entire process from green electricity production to green certificate generation, meeting the regulatory authorities' verification needs for green electricity compliance, and providing users with transparent green electricity source information.

[0049] In some embodiments, each green certificate in the unactivated digital green certificate data set is NFT-encoded to generate a grid-connected verification instruction containing the green certificate NFT code, the corresponding cumulative power generation value, the geographic location information, and the planned grid-connected time point; according to the grid-connected verification instruction, whether the cumulative power generation value is physically transmittable at the planned grid-connected time point is analyzed; when the cumulative power generation value is transmittable, a verification pass signal is returned to the blockchain node; the blockchain node responds to the verification pass signal and updates the status attribute of the corresponding green certificate NFT code from unactivated to tradable; based on all green certificate NFT codes whose status has been updated to tradable, a tradable digital green certificate data set is generated.

[0050] The Green Certificate NFT code can be a unique identifier generated by encoding an unactivated digital Green Certificate into a non-fungible token (NFT). This, combined with blockchain technology, ensures the uniqueness, immutability, and traceability of Green Certificates, ensuring each Green Certificate corresponds to a unique amount of green electricity production and preventing duplicate transactions or counterfeiting. The cumulative power generation value can refer to the total amount of green electricity produced by the green power generator corresponding to the unactivated digital Green Certificate within a specific time period and is the core quantification of the Green Certificate's value. Geographic location information can refer to the actual geographic coordinates of the green power generator, used to determine the production location of green electricity and analyze its physical transmission path to the grid connection node. The planned grid connection time can refer to the specific time when green electricity is scheduled to be connected to the grid. This is preset by the green electricity producer based on the power generation plan and grid scheduling requirements to match the grid's transmission capacity at that time. Physical transmission feasibility refers to the actual possibility of green electricity being transmitted from the generator's location through the grid to the grid connection node at the planned grid connection time. This is determined based on a comprehensive assessment of factors such as the grid topology, transmission line capacity, and grid load at that time. The verification pass signal can be a confirmation signal sent by the power grid dispatching system or a trusted third-party verification node to the blockchain node when the analysis confirms that the accumulated power generation value is physically transmittable at the planned grid connection time point, which is used to trigger the green certificate status update.

[0051] Specifically, the core of green electricity trading is the simultaneous delivery of the environmental value of green electricity and physical electricity. If only the production-side verification is completed (generating an unactivated green certificate) and it is not confirmed whether the green electricity can actually be connected to the grid and transmitted, there may be a risk that the green certificate has been traded but the corresponding green electricity cannot be delivered. For example, although the green certificate generated by a wind power project has passed the production verification, the green electricity cannot actually be connected to the grid because the transmission line is fully loaded at the planned grid-connected time. At this time, the green certificate traded is essentially a "virtual certificate", which violates the physical properties of green electricity trading; and digital green certificates, as value carriers, must have unique identification and anti-tampering characteristics. If NFT encoding is not performed, multiple green certificates may be generated for the same green electricity, or the green certificate information may be tampered with (such as false reporting of power generation), which will undermine market fairness. To address the above issues, this step first traverses the unactivated digital green certificate dataset and performs NFT encoding operations on each green certificate: extracting its power generation data stream (such as 10MW power generation data of a wind farm at 15:30:00.000), geographic location information (such as 39.90°N 116.41°E) and planned grid connection time (such as 2025-06-01 14:00:00), calling the smart contract coding engine to generate a unique NFT code (such as "0x8a5d...c3f2") and bind the data to encapsulate it into a grid connection verification instruction set; then the power grid dispatching system receives the instruction and performs physical transmission feasibility verification: first, based on the geographic location, locate the grid access node (such as East China Power Grid Node A), retrieve its real-time transmission capacity (such as 500MW), and then compare the planned grid connection time of the green certificate with the grid load forecast curve (such as verifying whether it is in the evening peak congestion period of 18:00-20:00). Finally, the node absorption capacity is calculated by combining the cumulative value of power generation (such as 50MWh) and the line loss model (such as ±3% loss rate); if all three checks are passed (such as transmission margin > 60MWh, non-congested period, and acceptable loss), a digital signature verification pass signal is generated and returned to the blockchain node; finally, the blockchain node listens to the signal, calls the updateStatus() function of the smart contract, updates the matching NFT encoding status attribute from "inactivated" to "tradable", and records the change timestamp (such as 2025-05-20 09:15:23) and signal hash value (such as "sha256:7d4a...b9e1") in the distributed ledger, aggregating all updated status green certificates to generate a tradable digital green certificate dataset.

[0052] Through this solution, based on the verification of physical transmission feasibility, the green electricity corresponding to the tradable green certificate will have the actual grid-connected capability, avoiding the transaction of "virtual green certificate", ensuring that the environmental value of green electricity obtained by the buyer is supported by real power delivery, and enhancing the credibility of green electricity transactions; the green certificate NFT coding is combined with blockchain technology to fundamentally eliminate problems such as green certificate forgery and repeated transactions, provide the green electricity market with transparent and traceable digital certificates, and maintain fair competition order in the market; based on the feasibility analysis of the planned grid-connected time point and grid load, the green electricity grid-connected plan is matched with the grid dispatching demand, reducing power abandonment, improving the green electricity absorption rate, and promoting the efficient use of new energy.

[0053] In some embodiments, the base layer value is determined based on the product of the cumulative value of power generation and the preset unit fixed emission reduction amount, and the base layer value is assigned to each green certificate NFT code in the tradable digital green certificate data set; the dynamic parameter set of the power grid is analyzed to obtain the dynamic layer value adjustment factor, the dynamic layer value is determined, and the dynamic layer value is assigned to each green certificate NFT code in the tradable digital green certificate data set; the base layer value of each green certificate NFT code is multiplied by the corresponding dynamic layer value adjustment factor to generate the real-time equity price of the green certificate NFT code; the real-time equity prices of all green certificate NFT codes are aggregated to generate a green certificate real-time pricing data set.

[0054] The fixed unit emission reduction can refer to a fixed reference value for the amount of carbon emissions that green electricity can reduce per unit of electricity generated compared to traditional fossil fuel generation (such as thermal power). It is a fundamental quantitative indicator for measuring the environmental value of green electricity. The base layer value can be the inherent environmental value of the green certificate that does not fluctuate with real-time market conditions, reflecting the fundamental contribution of green electricity to reducing carbon emissions and promoting low-carbon transformation. The dynamic layer value adjustment factor can be a coefficient used to quantify the impact of dynamic grid parameters on the value of the green certificate, reflecting the market adaptability of the green certificate under different environmental rights (such as the ratio of green to thermal power output), spatial (such as areas of tight / relaxed power supply and demand), and temporal (such as peak / off-peak power periods). The dynamic layer value can refer to the portion of the green certificate's value that is affected by the real-time operating status of the grid, reflecting its actual contribution to grid stability and power supply and demand balance under specific temporal and spatial conditions. The real-time equity price can be the actual trading price of the green certificate's NFT code at a specific moment, comprehensively reflecting the green certificate's fundamental environmental value and real-time market adaptability.

[0055] Specifically, the core of green electricity trading is to achieve the unity of green electricity's environmental value and market value, and the pricing mechanism is the key to connecting the supply and demand sides and ensuring fair and efficient transactions. Traditional green electricity pricing methods have significant limitations: on the one hand, prices are determined only based on power generation or fixed subsidies, ignoring the core environmental value of green electricity (such as its contribution to emission reduction), resulting in the environmental value being underestimated; on the other hand, the spatiotemporal dynamic characteristics of power grid operation (such as differences in peak and valley periods, and regional supply and demand imbalances) are not taken into account, resulting in prices that cannot reflect the actual utility of green electricity in different scenarios, which in turn leads to problems such as low green electricity absorption efficiency and insufficient market activity. To address the above issues, this step first extracts the cumulative value of power generation bound to the green certificate NFT code (such as 50MWh) from the tradable digital green certificate data set, calls the preset unit fixed emission reduction parameter (such as 0.8 tons of CO2 / MWh), and multiplies the two to generate the base layer value (such as 50×0.8=40 tons of CO2 emission reduction equivalent); then obtains the power grid dynamic parameter set in real time, including the regional net load fluctuation rate (such as current load / forecast load=1.15), the key section blocking coefficient (such as actual flow / safety limit=0.95) and the new energy consumption priority index (such as the proportion of green electricity in the dispatch instruction is 30%), and analyzes it through the rule engine. Analysis parameters: If the regional net load volatility is greater than the threshold (such as >1.1) and the blocking coefficient is close to the critical value (such as >0.9), an upward floating factor (such as 1.25) is generated; if the absorption priority is high and the load fluctuation is smooth (such as volatility <0.9), a downward floating factor (such as 0.8) is generated; then the base layer value is multiplied by the dynamic layer value adjustment factor (such as 40×1.25=50 environmental equity units), and converted into legal currency through the on-chain exchange rate contract (such as 1 unit = 10 yuan); finally, all green certificate NFT codes are traversed to generate a real-time equity price mapping relationship, which is encapsulated into a key-value pair data structure to form a green certificate real-time pricing data set.

[0056] Through this plan, the basic layer value converts the environmental emission reduction contribution of green electricity into a tradable value indicator, and the dynamic layer value adjustment factor reflects the actual utility of green electricity under different time and space conditions. The combination of the two enables the green certificate price to reflect the environmental attributes and adapt to market dynamics, realizing the unity of "green value" and "market value"; the time and space rights dynamic pricing strategy guides green electricity to be consumed first in key scenarios such as peak power periods and load centers through price signals, reducing the phenomenon of green electricity abandonment. At the same time, the price is lowered in valley power periods or areas with loose supply and demand, encouraging users to consume more, balancing the grid load and improving the stability of grid operation.

[0057] In some embodiments, the grid dynamic parameter set includes the real-time total output value of the regional grid, the real-time grid congestion level and the real-time grid load status; the real-time total output value of the regional grid is analyzed to determine the real-time environmental increase / decrease coefficient of the regional grid cleanliness corresponding to the green certificate NFT code to the basic layer value; the real-time grid congestion level is analyzed to determine the spatial compensation coefficient required for the equivalent transmission loss from the geographical location of the corresponding green certificate NFT code to the target load center; the real-time grid load status is analyzed to determine the time increase / decrease coefficient required for the current peak or valley period of the grid; the real-time environmental increase / decrease coefficient, the spatial compensation coefficient and the time increase / decrease coefficient are multiplied to generate the dynamic layer value adjustment factor of each green certificate NFT code.

[0058] The real-time total output value of a regional power grid can be the total generated power of the power grid in a specific region at a specific moment, that is, the total real-time output of all power generation equipment in the region (including thermal power, wind power, photovoltaic power, etc.). The real-time grid congestion level can be a quantitative indicator that represents the degree of power transmission obstruction caused by excessive power or equipment capacity limitations on transmission lines or key nodes within the power grid. The real-time grid load status can be the total amount and distribution of electricity demand carried by the power grid at a specific moment, mainly used to distinguish whether the current time is during peak hours (high electricity demand) or off-peak hours (low electricity demand). The cleanliness of the regional power grid can be an indicator that measures the proportion of clean energy output (such as wind power, photovoltaic power, and hydropower) in the total output of a specific regional power grid, reflecting the low-carbon and environmentally friendly nature of the regional power grid's power generation structure. The real-time environmental gain / loss coefficient can be a coefficient that adjusts the environmental value of the green certificate base layer based on the proportion of clean energy in the real-time total output value of the regional power grid. When the proportion of clean energy is high, the coefficient is greater than 1 (gain); when the proportion of clean energy is low, the coefficient is less than 1 (loss). The equivalent transmission loss can be the equivalent ratio or quantified value of the electric energy lost during transmission, calculated by comprehensively considering factors such as the transmission distance between the geographical location of the green power generator and the target load center and the grid congestion level. The spatial compensation coefficient can be a coefficient that corrects the spatial cost of the green certificate base layer value by taking into account the transmission loss from the geographical location of the green power generator to the target load center. That is, the longer the transmission distance and the greater the loss, the smaller the coefficient. The time gain / loss coefficient can be a coefficient that corrects the time value of the green certificate base layer value based on the real-time grid load status (peak / off-peak period). During peak periods, when electricity demand is tight, the coefficient is greater than 1 (gain); during off-peak periods, when electricity demand is loose, the coefficient is less than 1 (loss).

[0059] Specifically, in the electricity market, the state of the power grid (such as the proportion of clean energy, congestion, and load levels) changes in real time, and the value of green certificates should also be adjusted dynamically. If a single dimension is used for adjustment, it will cause system imbalance: relying solely on environmental coefficients will ignore the physical constraints of the power grid, and only using spatial compensation will lead to value distortion across time periods. Therefore, it must be achieved through a three-factor multiplication operation. If there is a lack of a comprehensive dynamic layer value adjustment factor, the pricing of green certificates will lag behind market changes, and there may be unfair phenomena such as high-value green electricity being traded at low prices or low-value green electricity being traded at high prices, which will undermine the enthusiasm of green electricity producers and consumers. However, through the comprehensive calculation of real-time environmental increase / decrease coefficients, spatial compensation coefficients, and time increase / decrease coefficients, it can be ensured that the price of green certificates reflects market dynamics in real time and protects the fair rights and interests of both parties to the transaction. In response to the above problems, this step first separates the fossil energy and clean energy output data based on the real-time total output value of the regional power grid in the power grid dynamic parameter set, calculates the proportion of clean energy output, and generates a real-time environmental gain / decrease coefficient based on this. For example, when the proportion of clean energy is higher than the regional benchmark, the coefficient will increase by a certain value (such as 0.1) every time it exceeds a certain proportion (such as 10%), and vice versa. It will decrease by a certain value (such as 0.15); then, using the geographical coordinates of the generator set in the green certificate NFT code, call the power grid digital twin system to determine its electrical distance to the target load center, and calculate the approved loss rate in combination with the real-time power grid congestion level (such as each increase in the congestion level by 1 level corresponds to a 3% increase in the loss rate), and then generate a spatial compensation coefficient, whose value is 1 plus the approved loss rate; then, analyze the real-time power grid load status, When the load rate reaches or exceeds a specific high threshold (such as 85%), it is determined to be a peak period, and the time gain coefficient is 1 plus (real-time load rate minus the high threshold) multiplied by a coefficient (such as 0.2); when the load rate is lower than a specific low threshold (such as 40%), it is determined to be a valley period, and the time loss coefficient is 1 minus (the low threshold minus the real-time load rate) multiplied by a coefficient (such as 0.1). The coefficient remains at 1.0 during the flat period; finally, for each green certificate NFT code in the tradable digital green certificate data set, the calculated real-time environmental gain / loss coefficient, space compensation coefficient and time gain / loss coefficient are multiplied, that is, the dynamic layer value adjustment factor = real-time environmental gain / loss coefficient × space compensation coefficient × time gain / loss coefficient, so as to batch output the dynamic layer value adjustment factors of all green certificates.

[0060] Through this plan, by comprehensively considering the real-time environment of the power grid, spatial transmission costs and temporal load status, the price of green certificates is no longer limited to the static basic environmental value, but can reflect market dynamics in real time, more accurately match the actual value of green electricity, and avoid resource mismatch caused by pricing deviations; the environmental coefficient dynamically responds to changes in the carbon intensity of the power grid, thereby increasing the premium rate of green certificates during high-pollution periods and truly reflecting the marginal benefits of emission reduction; the introduction of the spatial compensation coefficient balances the transmission cost of long-distance green electricity, improves the market competitiveness of clean energy in remote areas, promotes the efficient flow of green electricity from the production end to the load center, and optimizes the configuration range of clean energy; the time coefficient makes green certificates more valuable during peak hours and more price-competitive during off-peak hours, encouraging users to consume more green electricity during off-peak hours and reasonably control electricity consumption during peak hours, thereby alleviating the peak-valley load difference of the power grid and improving the stability of power grid operation.

[0061] In some embodiments, the real-time green electricity output value of the green electricity generator set in the current time period is determined based on the real-time total output value of the regional power grid and the power generation data stream; the real-time cleanliness percentage of the regional power grid is determined based on the ratio of the real-time green electricity output value to the real-time total output value of the regional power grid; according to a preset cleanliness reference threshold, the real-time cleanliness percentage of the regional power grid is compared with the preset cleanliness reference threshold to determine the deviation direction of the real-time cleanliness percentage of the regional power grid relative to the preset cleanliness reference threshold, and determine the deviation amplitude value; when the real-time cleanliness percentage of the regional power grid is higher than the preset cleanliness reference threshold, a real-time environmental gain coefficient with a value greater than 1 is generated, and the real-time environmental gain coefficient is dynamically adjusted to increase according to the deviation amplitude value; when the real-time cleanliness percentage of the regional power grid is lower than the preset cleanliness reference threshold, a real-time environmental loss coefficient with a value less than 1 is generated, and the real-time environmental loss coefficient is dynamically adjusted to decrease according to the deviation amplitude value.

[0062] The real-time green power output value can be the actual power generated by a green power generator during the current period and is a key indicator for measuring the contribution of green power to the regional power grid. The real-time cleanliness percentage of the regional power grid can refer to the proportion of green power generation to total power generation in the regional power grid during the current period and is used to quantify the cleanliness of the regional power grid. The preset cleanliness benchmark threshold can be a benchmark value set based on regional energy policies, environmental goals, and historical power grid cleanliness data. It serves as a reference standard for determining whether the regional power grid meets the cleanliness standards and can be dynamically adjusted based on regional conditions. The real-time environmental gain factor can be an adjustment factor generated when the real-time cleanliness percentage of the regional power grid exceeds the preset cleanliness benchmark threshold. A value greater than 1 is used to amplify the base value of green certificates, reflecting the additional environmental contribution of green power in a clean power grid. The real-time environmental loss factor can be an adjustment factor generated when the real-time cleanliness percentage of the regional power grid falls below the preset cleanliness benchmark threshold. A value less than 1 is used to reduce the base value of green certificates, reflecting the reduced environmental contribution of green power in a clean power grid.

[0063] Specifically, the base layer value of the green certificate is determined by the product of the cumulative value of power generation and the preset unit fixed emission reduction. It only reflects the inherent environmental value of green electricity, but cannot reflect the impact of the real-time operating status of the power grid on the value of the green certificate. For example, the environmental contribution of the same amount of green certificates in a power grid with a high proportion of clean energy is relatively more significant; while in a power grid with a high proportion of thermal power, its environmental value will be partially offset. If the price is only based on the base layer value, the value of the green certificate will be out of line with the actual environmental benefits. Therefore, it is necessary to dynamically adjust it through the real-time environmental increase / decrease coefficient. To address the above problems, this step first obtains the encrypted power generation data stream from the blockchain node, decrypts it and extracts the real-time green power output value of the target area in the current period (such as 150MW). At the same time, the total output value of the regional power grid (such as 500MW) is pulled from the power grid dispatching center API in real time, and input into the smart contract after verification; the smart contract then executes the cleanliness percentage calculation: real-time green power output value ÷ total output value of the regional power grid × 100% (such as 150 / 500×100%=30%); then calls the preset cleanliness benchmark threshold ( Deviation analysis is performed on the calculated result (such as 30%) - if the calculated result is higher than the threshold (such as 35%), it is marked as a positive deviation and the deviation amplitude (such as 5%) is calculated to generate a real-time environmental gain coefficient greater than 1 (such as 1+(5 / 30)×0.5≈1.08); if it is lower than the threshold (such as 25%), it is marked as a negative deviation and the amplitude (such as 5%) is calculated to generate a real-time environmental loss coefficient less than 1 (such as 1-(5 / 30)×0.5≈0.92); finally, the generated coefficient is written into the corresponding green certificate NFT metadata field for platform call.

[0064] Through this solution, the value of green certificates is dynamically linked to the cleanliness of the regional power grid through real-time environmental increase / decrease coefficients, accurately reflecting the actual environmental contribution of green electricity in different power grid environments and avoiding value distortion; guiding green electricity generators to areas with low cleanliness, maximizing the emission reduction benefits of green electricity, while avoiding excessive green electricity investment in areas with high cleanliness, and improving the overall utilization efficiency of energy resources.

[0065] In some embodiments, according to the grid connection verification instruction, the geographic location information of the target load center is extracted; based on the real-time grid congestion level, the tolerance threshold of the current grid congestion state to the power transmission distance is analyzed, and the maximum effective transmission radius is dynamically generated; according to the geographic coordinate data of the green power generator set and the geographic location information of the target load center, the actual spatial distance from the green power generator set to the target load center is determined; the actual spatial distance is dynamically compared with the maximum effective transmission radius: when the actual spatial distance is less than or equal to the maximum effective transmission radius, a spatial compensation coefficient with a reference value of 1 is generated; when the actual spatial distance is greater than the maximum effective transmission radius, according to the ratio of the excess distance to the maximum effective transmission radius, according to the preset nonlinear attenuation rule, with the value 1 as the reference, the spatial compensation coefficient is dynamically lowered.

[0066] The target load center can be a core geographic location in a concentrated electricity consumption area (such as an industrial park or urban business district). The maximum effective transmission radius can be the maximum distance threshold at which transmission losses remain within an acceptable range when transmitting electricity from a generator to the load center under the current grid congestion state. This value changes dynamically with the real-time grid congestion level.

[0067] Specifically, the geographical locations of green electricity generating units have significant differences: some units may be close to the load center (such as photovoltaic power stations around the city), with short transmission distances and small losses; some units may be located in remote areas (such as desert wind power bases), with long transmission distances and large losses. If this geographical difference is not taken into account and green certificates are directly priced using a unified standard, it will lead to the unfair phenomenon that the value of green certificates of units near the load center is overestimated and the value of green certificates of units far from the load center is underestimated, which will undermine the enthusiasm for green electricity development in remote areas. At the same time, grid congestion is a common problem in power transmission: when the load on a certain line of the power grid is too high, the power transmission capacity will be limited. At this time, even if the distance between the generator set and the load center is relatively close, the actual transmission loss may increase significantly due to congestion. On the contrary, if the grid congestion level is low and the transmission channel is unobstructed, the loss may be within an acceptable range even if the distance is slightly far. To address the above issues, this step first extracts the geographic location information (such as longitude and latitude coordinates) of the target load center from the grid connection verification instruction, and obtains the geographic coordinate data of the generator set bound to the green certificate NFT code from the blockchain node; then, based on the real-time grid congestion level (such as congestion level 1 indicates unobstructed, and congestion level 5 indicates severe congestion), dynamically generates the maximum effective transmission radius (for example, congestion level 1 corresponds to 500km, and congestion level 3 corresponds to 300km); then, calls the geographic information system to calculate the actual spatial distance from the generator set to the target load center, and dynamically compares the actual distance with the current maximum effective transmission radius: if the actual distance is less than or equal to the maximum radius (such as the actual distance is 280km and the maximum radius is 300km), the spatial compensation coefficient is directly assigned to 1.0; if the actual distance is greater than the maximum radius (such as the actual distance is 360km and the maximum radius is 300km, the excess ratio is 20%), then the preset nonlinear attenuation rule is applied according to the excess ratio for adjustment (for example, when the excess ratio does not exceed 0.3, the coefficient is reduced to 0.9, and when the excess ratio exceeds 0.3, the coefficient is reduced to 0.5).

[0068] Through this plan, the dynamic adjustment of the spatial compensation coefficient can be used to guide green electricity transactions towards lower losses and higher efficiency: for green electricity that is close to the load center and has little impact on grid congestion, its spatial compensation coefficient is better and the green certificate price is more competitive, so it is consumed first; for green electricity that is far away but has alleviated grid congestion, its compensation coefficient is increased and it can also obtain reasonable pricing; this efficiency-first, dynamic balance mechanism can promote the precise matching of green electricity resources and load demand, reduce ineffective transmission losses, improve the overall green electricity consumption efficiency, and assist in the transformation of the energy structure.

[0069] In some embodiments, based on timestamp data with millisecond precision, the precise millisecond time point corresponding to green electricity generation is extracted; based on the real-time grid load status and the precise millisecond time point, the real-time load value corresponding to the precise millisecond time point is determined; the changing trend of the real-time load value within a preset time window is dynamically evaluated to determine the direction of load change: when the load change direction indicates that the load continues to rise, a time gain coefficient with a value greater than 1 is generated, and the time gain coefficient is dynamically adjusted to increase according to the deviation amplitude value of the number of real-time load values ​​that continuously rise within the preset time window and the preset rising intensity threshold; when the load change direction indicates that the load continues to fall, a time loss coefficient with a value less than 1 is generated, and the time loss coefficient is dynamically adjusted to decrease according to the deviation amplitude value of the number of real-time load values ​​that continuously fall within the preset time window and the preset falling intensity threshold.

[0070] The precise millisecond time point can be the specific millisecond moment of actual green electricity generation, extracted from millisecond-precision timestamp data. It serves as the time benchmark for linking green electricity generation behavior with grid load status. The real-time load value can be the specific numerical value corresponding to the real-time grid load status at the precise millisecond time point. It is an indicator that quantifies the magnitude of grid load. The load change direction can refer to the overall trend of the real-time load value within a preset time window, including "continuous increase" and "continuous decrease." The preset increase intensity threshold can be a pre-set benchmark value for measuring the intensity of load increase. It is derived based on historical load increase data and reflects the average intensity of load increase under normal conditions on the grid. The time gain coefficient can be a coefficient greater than 1 generated when the load change direction is continuous increase. It is used to reflect the higher value of green electricity during peak hours in green electricity pricing and is a component of the dynamic layer value adjustment factor. The preset decrease intensity threshold can be a pre-set benchmark value for measuring the intensity of load decrease. It is derived based on historical load decrease data and reflects the average intensity of load decrease under normal conditions on the grid. The time reduction coefficient can be a coefficient with a value less than 1 generated when the load change direction is continuously declining. It is used to reflect the relatively low value of green electricity in off-peak periods in green electricity pricing, and is also a component of the dynamic layer value adjustment factor.

[0071] Specifically, the power generation of green electricity (such as wind power, photovoltaic power, etc.) is subject to fluctuations due to natural conditions, and the load demand of the power grid also shows regular changes over time (such as peak industrial electricity consumption during the day and low residential electricity consumption at night). During peak load periods, the power grid has an urgent demand for electricity. The green electricity connected at this time can effectively alleviate the power supply pressure of the power grid and reduce the peak-shaving demand of traditional thermal power. Its environmental value and practical value are both higher; while in low load periods, the power grid has an oversupply of electricity, and the access of green electricity may face difficulties in absorption, and its value is relatively low. If the impact of time factors on the value of green electricity is ignored and a fixed pricing method is adopted, the value of green electricity will be underestimated during peak periods and overestimated during low load periods. This will not reflect the actual contribution of green electricity, but will also reduce the enthusiasm of power generators to increase green electricity supply during peak periods, which is not conducive to the efficient absorption of green electricity. To address the above issues, this step first obtains the precise millisecond time point of the green certificate from the blockchain node (for example, 2023-10-05T14:30:25.456Z), and simultaneously pulls the real-time grid load status data stream through the grid dispatching center API; then, based on the current time point, slides the preset time window (for example, 5 minutes) forward, extracts all millisecond load values ​​within the window, calculates the load difference between adjacent time points, and counts the number of consecutive positive differences (increase) or negative differences (decrease) to determine the direction of load change; if the load continues to rise, count the number of consecutively rising real-time load values ​​(for example, 15 times), Compare it with the preset rising intensity threshold (such as 10 times) and calculate the deviation (such as (15-10) / 10=0.5) to generate a time gain coefficient (such as 1+0.1×0.5=1.05); if the load continues to decrease, count the number of consecutive decreases (such as 12 times) and compare it with the preset decrease intensity threshold (such as 8 times) to calculate the deviation (such as (12-8) / 8=0.5) to generate a time loss coefficient (such as 1-0.05×0.5=0.975); finally, set the coefficient boundary constraints (such as the upper limit of the gain coefficient 2.0 and the lower limit of the loss coefficient 0.5) to prevent pricing distortion in extreme scenarios.

[0072] Through this solution, based on millisecond-level timestamps and real-time load status analysis, the time coefficient can match the instantaneous changes in grid load in real time, avoiding pricing deviations caused by coarse time granularity and allowing the value of green electricity at different time points to be truly reflected. By distinguishing the direction of load changes and combining the deviation amplitude value adjustment coefficient, it ensures that the power generator's income matches the actual contribution of green electricity to the grid. At the same time, it also allows electricity users to obtain reasonable green electricity prices based on the actual load status, balancing the interests of both parties to the transaction.

[0073] In some embodiments, a target user's cancellation request for a specific green certificate NFT code in a green certificate real-time pricing data set is obtained, and the target cancellation electricity value specified in the cancellation request is analyzed; it is verified whether the current status of the specific green certificate NFT code is a tradable state; when the verification passes, based on the target cancellation electricity value, the cumulative value of power generation corresponding to the specific green certificate NFT code is marked as cancelled on the blockchain node; the real-time equity prices, corresponding cumulative values ​​of power generation, consumption time points and geographical location information of all cancelled green certificate NFT codes are collected to generate a green electricity consumption traceability report.

[0074] The target verification electricity value can be the green electricity amount that the user specifies in the verification request and hopes to consume through this verification. It is the basis for judging whether the cumulative value of power generation corresponding to the green certificate meets the user's needs.

[0075] Specifically, from the perspective of the integrity of the transaction closed loop, the entire process of green electricity trading includes green certificate generation, status activation and pricing, and cancellation is a key step in ultimately realizing the green electricity rights from market circulation to actual use by users. If the cancellation link is missing, the green certificate will always remain in a "tradable" state and will not be able to complete the transformation from "commodity" to "service", resulting in a break in the transaction process. Users cannot actually enjoy green electricity rights, and the power generation of green electricity generators cannot be accurately matched with user needs, ultimately affecting the normal operation of the green electricity market; from the perspective of the clarity of rights ownership, digital green certificates achieve uniqueness through NFT encoding, but before cancellation, the corresponding cumulative value of power generation is still in a "tradable" state, and there may be a risk of repeated trading or misappropriation. The cancellation process can mark the cumulative value of power generation of a specific green certificate NFT code as "cancelled" on the blockchain node, which can clarify that this part of the rights and interests have belonged to the user who initiated the cancellation request, thereby avoiding disputes caused by unclear rights and interests. In response to the above problems, in this step, the user selects the target green certificate NFT code through the platform front end, enters the target write-off power value (such as 150.5kWh) and signs and submits it. The blockchain node receives the request and parses the green certificate NFT code and the target write-off power value; then the node queries the current status attribute field of the green certificate NFT code. If the status is not "tradable" (such as the status is written off), the request is rejected and an error code (such as STATUS_INVALID) is returned; if the verification passes, the smart contract executes the operation: reads the current cumulative value of power generation, compares the target write-off power value with the remaining power (if it exceeds the limit, the write-off fails), and updates the current value. The new cumulative value of power generation is the original cumulative value minus the target write-off value (for example, from 1000kWh to 849.5kWh), and a "written-off mark" is written on the blockchain to record the write-off amount and remaining power. After that, the node automatically captures the real-time equity price of the write-off (for example, 0.12 yuan / kWh), the target write-off value, the consumption time point (the blockchain timestamp when the request is processed, such as 2025-03-15T14:23:05.876Z) and the geographic location information (such as longitude and latitude [38.9072, -77.0369]), and generates a structured report according to the preset template (for example, JSON format containing NFT ID, power consumption, unit price, time and location). The report is hashed and stored on the chain, and a verifiable link is returned to the user.

[0076] Through this solution, the closed-loop integrity of green electricity transactions is guaranteed, and the entire process of green certificates from generation, pricing to final consumption by users is closed, ensuring that green electricity rights and interests can be effectively converted into users' actual electricity needs, and promoting a virtuous cycle in the green electricity market; the clarity and security of rights and interests are improved, and the risk of repeated trading and misappropriation of green electricity rights and interests is avoided through the status marking and tamper-proof characteristics on the blockchain, and the ownership of each written-off right is clarified, reducing transaction disputes; supervision and compliance support are strengthened, and the green electricity consumption traceability report provides regulatory authorities with traceable and transparent green electricity consumption data, meeting the regulatory requirements of environmental protection policies and energy regulations on green electricity transactions, and helping to achieve clean energy development goals.

[0077] In some embodiments, based on the green electricity consumption traceability report, when the transaction settlement is completed, the real-time exchange rate data of the carbon market is linked; according to the real-time exchange rate data of the carbon market, the carbon quota tonnage that can be exchanged for the tradable digital green certificate represented by the unit green certificate NFT code is dynamically calculated; on the blockchain node, according to the preset exchange ratio, the green certificate rights corresponding to the green certificate NFT code of the completed transaction are transferred to the corresponding carbon quota tonnage, and recorded in the transaction party's account in the carbon market.

[0078] Real-time carbon market exchange rate data can be the real-time exchange rate between carbon allowances and currencies in the carbon market, reflecting the current market value of carbon allowances. It is typically sourced from the real-time data interface of the carbon trading platform (such as the official data channel of the national carbon trading market or regional carbon markets). Carbon allowance tonnage refers to the number of tons of greenhouse gases (measured in carbon dioxide equivalent) allowed to be emitted in the carbon market. It is the core trading entity in the carbon market and is used to quantify the carbon emission rights of enterprises or institutions.

[0079] Specifically, from the perspective of quantifying environmental value, the core value of green electricity lies in the carbon emissions it reduces by replacing fossil energy power generation, and carbon quotas are currently the globally recognized standard tool for quantifying carbon emission rights. In green electricity trading, it is difficult to directly reflect the connection between its emission reduction benefits and the carbon market through "green certificate pricing" alone. For example, 1 megawatt-hour of green electricity can reduce carbon dioxide emissions by about 0.6 tons (slightly different in different regions due to differences in energy structure). However, if this emission reduction cannot be converted into tradable carbon quotas in the carbon market, its environmental value will remain at the "hidden" level; from the perspective of market collaborative development, there is a certain degree of separation between the current green electricity market and the carbon market: green electricity trading mainly focuses on the energy value of electricity itself, while the carbon market focuses on the trading of carbon emission rights. The value chain of the two has not been fully connected. This separation has limited the economic attractiveness of green electricity, that is, the benefits of companies purchasing green electricity only come from electricity cost savings or brand image improvement, and they cannot directly obtain benefits from the carbon market. In response to the above problems, in this step, when the green certificate transaction settlement is completed, the blockchain node obtains the exchange rate data of the authoritative carbon exchange in real time through the API interface (such as the carbon quota unit price of 100 yuan / ton), and writes it into the smart contract after verification and signature; then parses the green electricity consumption traceability report, extracts the consumption power (such as 1000kWh) and real-time equity price (such as 200 yuan) corresponding to the target green certificate NFT code, and first converts the consumption power into basic carbon according to the preset exchange ratio (such as 1MWh green electricity = 0.5 tons of carbon quota). The quota (such as 1000kWh×0.5 tons / MWh=0.5 tons), and then the green certificate equity value is converted into additional carbon quota (such as 200 yuan ÷ 100 yuan / ton = 2 tons) according to the real-time carbon price (such as 100 yuan / ton), and finally the total carbon quota tonnage is obtained (such as 0.5 tons + 2 tons = 2.5 tons); the smart contract automatically destroys the green certificate NFT code and triggers the on-chain transfer, and records the calculated result (such as 2.5 tons) in the blockchain account of the trading party in the carbon market, completing the lossless conversion of environmental rights.

[0080] Through this plan, green certificate rights will be converted into carbon quotas, so that the environmental value of green electricity can be directly reflected in economic value. Enterprises that purchase green electricity can not only obtain electricity energy, but also obtain additional income through carbon quota trading, thereby encouraging more enterprises to participate in green electricity trading and expand the scale of green electricity consumption; breaking the separation of the two markets, forming a complete value chain of "green electricity production-green certificate trading-carbon quota conversion", promoting the cross-market flow of environmental rights and interests, and realizing the synergistic effect of "1+1>2".

Claims

1. An intelligent algorithm platform for power trading based on blockchain consensus, characterized by: include: Obtain a real-time green electricity encrypted data set, upload the real-time green electricity encrypted data set to a blockchain node, and generate an unactivated digital green certificate data set; Based on the inactivated digital green certificate data set, a grid connection verification instruction is generated, the inactivated digital green certificate data set is updated to a tradable state, and a tradable digital green certificate data set is generated; a power grid dynamic parameter set is obtained, and based on the power grid dynamic parameter set and the tradable digital green certificate data set, a green certificate real-time pricing data set is generated through a spatiotemporal equity dynamic pricing strategy; according to the green certificate real-time pricing data set, the digital green certificate is cancelled during the user's electricity consumption process, and a green electricity consumption traceability report is output.

2. The platform according to claim 1, characterized in that The real-time green electricity encrypted data set is uploaded to the blockchain node to generate an unactivated digital green certificate data set, including: the real-time green electricity encrypted data set includes a power generation data stream, millisecond-level precision timestamp data and geographic coordinate data of the green electricity generator set; according to the real-time green electricity encrypted data set, a preset green certificate generation verification logic is triggered: based on the millisecond-level precision timestamp data, the fluctuation characteristics of the power generation data stream in the corresponding time interval are analyzed to see whether they comply with the preset green electricity output mode; based on the geographic coordinate data, whether the green electricity generator set is located in the preset trusted green electricity area list; when the fluctuation characteristics of the power generation data stream comply with the green electricity output mode and the green electricity generator set is located in the trusted green electricity area list, the blockchain node determines that the verification is successful; after the blockchain node verifies that it is successful, the real-time green electricity encrypted data set is encapsulated into a real-time green electricity encrypted data unit with a unique identifier, and the real-time green electricity encrypted data unit is recorded in the corresponding blockchain node on the blockchain to generate the unactivated digital green certificate data set.

3. The platform according to claim 2, characterized in that The method of generating a grid connection verification instruction based on the inactivated digital green certificate data set, updating the inactivated digital green certificate data set to a tradable state, and generating a tradable digital green certificate data set includes: performing NFT encoding on each green certificate in the inactivated digital green certificate data set, and generating a grid connection verification instruction including the green certificate NFT code, the corresponding cumulative power generation value, the geographic location information, and the planned grid connection time point; analyzing, according to the grid connection verification instruction, whether the cumulative power generation value has the physical transmission feasibility at the planned grid connection time point; when the cumulative power generation value is transmittable, returning a verification pass signal to the blockchain node; the blockchain node responds to the verification pass signal, and updates the status attribute corresponding to the green certificate NFT code from inactivated to tradable; and generating the tradable digital green certificate data set based on all the green certificate NFT codes whose status is updated to tradable.

4. The platform according to claim 3, characterized in that The green certificate real-time pricing data set is generated based on the power grid dynamic parameter set and the tradable digital green certificate data set through a spatiotemporal equity dynamic pricing strategy, including: determining the basic layer value based on the product of the accumulated power generation value and the preset unit fixed emission reduction amount, and assigning the basic layer value to each green certificate NFT code in the tradable digital green certificate data set; analyzing the power grid dynamic parameter set to obtain the dynamic layer value adjustment factor, determining the dynamic layer value, and assigning the dynamic layer value to each green certificate NFT code in the tradable digital green certificate data set; multiplying the basic layer value of each green certificate NFT code by the corresponding dynamic layer value adjustment factor to generate the real-time equity price of the green certificate NFT code; and aggregating the real-time equity prices of all the green certificate NFT codes to generate the green certificate real-time pricing data set.

5. The platform according to claim 4, characterized in that The analysis of the power grid dynamic parameter set to obtain the dynamic layer value adjustment factor includes: the power grid dynamic parameter set includes the real-time total output value of the regional power grid, the real-time power grid congestion level and the real-time power grid load status; analyzing the real-time total output value of the regional power grid to determine the real-time environmental increase / decrease coefficient of the regional power grid cleanliness corresponding to the green certificate NFT code to the basic layer value; analyzing the real-time power grid congestion level to determine the spatial compensation coefficient required for the equivalent transmission loss from the geographical location corresponding to the green certificate NFT code to the target load center; analyzing the real-time power grid load status to determine the time increase / decrease coefficient required for the current moment to be in the peak or valley period of the power grid; multiplying the real-time environmental increase / decrease coefficient, the spatial compensation coefficient and the time increase / decrease coefficient to generate the dynamic layer value adjustment factor of each green certificate NFT code.

6. The platform according to claim 5, characterized in that The analyzing the real-time total output value of the regional power grid and determining the real-time environmental gain / loss coefficient of the cleanliness of the regional power grid corresponding to the green certificate NFT code on the base layer value includes: determining the real-time green power output value of the green power generator in the current time period based on the real-time total output value of the regional power grid and the power generation data stream; determining the real-time cleanliness percentage of the regional power grid based on the ratio of the real-time green power output value to the real-time total output value of the regional power grid; comparing the real-time cleanliness percentage of the regional power grid with the preset cleanliness benchmark threshold according to a preset cleanliness reference threshold, determining the deviation direction of the real-time cleanliness percentage of the regional power grid relative to the preset cleanliness benchmark threshold, and determining the deviation amplitude value; when the real-time cleanliness percentage of the regional power grid is higher than the preset cleanliness benchmark threshold, generating a real-time environmental gain coefficient with a value greater than 1, and dynamically adjusting to increase the real-time environmental gain coefficient according to the deviation amplitude value; when the real-time cleanliness percentage of the regional power grid is lower than the preset cleanliness benchmark threshold, generating a real-time environmental loss coefficient with a value less than 1, and dynamically adjusting to decrease the real-time environmental loss coefficient according to the deviation amplitude value.

7. The platform according to claim 5, characterized in that The analysis of the real-time grid congestion level to determine the spatial compensation coefficient required for the equivalent transmission loss from the geographical location corresponding to the green certificate NFT code to the target load center includes: extracting the geographical location information of the target load center according to the grid connection verification instruction; analyzing the tolerance threshold of the current grid congestion state to the power transmission distance based on the real-time grid congestion level, and dynamically generating the maximum effective transmission radius; determining the actual spatial distance from the green power generator group to the target load center according to the geographical coordinate data of the green power generator group and the geographical location information of the target load center; dynamically comparing the actual spatial distance with the maximum effective transmission radius: when the actual spatial distance is less than or equal to the maximum effective transmission radius, generating the spatial compensation coefficient with a reference value of 1; when the actual spatial distance is greater than the maximum effective transmission radius, based on the ratio of the excess distance to the maximum effective transmission radius, according to the preset nonlinear attenuation rule, with the value 1 as the reference, dynamically lowering the spatial compensation coefficient.

8. The platform according to claim 5, characterized in that The analysis of the real-time grid load status to determine the time gain / decrease coefficient required for the current moment to be in the peak or valley period of the grid includes: extracting the precise millisecond time point corresponding to green electricity generation based on the millisecond-precision timestamp data; determining the real-time load value corresponding to the precise millisecond time point based on the real-time grid load status and the precise millisecond time point; dynamically evaluating the change trend of the real-time load value within a preset time window to determine the direction of load change: when the load change direction indicates that the load continues to rise, generating a time gain coefficient with a value greater than 1, and dynamically adjusting and increasing the time gain coefficient based on the deviation amplitude value between the number of real-time load values ​​that continuously rise within the preset time window and the preset rise intensity threshold; when the load change direction indicates that the load continues to fall, generating a time loss coefficient with a value less than 1, and dynamically adjusting and decreasing the time loss coefficient based on the deviation amplitude value between the number of real-time load values ​​that continuously fall within the preset time window and the preset fall intensity threshold.

9. The platform according to claim 5, characterized in that According to the green certificate real-time pricing data set, the digital green certificate is cancelled during the user's electricity consumption process, and a green electricity consumption traceability report is output, including: obtaining the target user's cancellation request for the specific green certificate NFT code in the green certificate real-time pricing data set, and analyzing the target cancellation electricity value specified in the cancellation request; verifying whether the current status of the specific green certificate NFT code is a tradable state; when the verification passes, based on the target cancellation electricity value, marking the cumulative power generation value corresponding to the specific green certificate NFT code as cancelled on the blockchain node; collecting the real-time equity price, the corresponding cumulative power generation value, the consumption time point and the geographical location information of all cancelled green certificate NFT codes to generate a green electricity consumption traceability report.

10. The platform according to claim 9, characterized in that The platform also includes: based on the green electricity consumption traceability report, when completing the transaction settlement, linking the real-time exchange rate data of the carbon market; dynamically calculating the carbon quota tonnage that can be exchanged for the tradable digital green certificate represented by the green certificate NFT code of the unit according to the real-time exchange rate data of the carbon market; on the blockchain node, according to the preset exchange ratio, the green certificate rights corresponding to the green certificate NFT code of the completed transaction are transferred to the corresponding carbon quota tonnage, and recorded in the transaction party's account in the carbon market.

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