A new energy power transaction method and system based on blockchain and artificial intelligence

Abstract

The application provides a new energy power transaction method based on blockchain and artificial intelligence, which comprises the following steps: constructing an intelligent contract analysis model, connecting with external data sources, and adjusting existing intelligent contract terms in real time; predicting real-time new energy power generation and analyzing historical transaction data between participants to obtain real-time prediction results and individualized transaction characteristics of each participant; screening the intelligent contract terms according to the individualized transaction characteristics to generate exclusive contract terms meeting the needs of the participants, and dynamically matching the supply and demand sides through an artificial intelligence algorithm; and constructing a risk assessment model for new energy power transactions to evaluate real-time transaction data during the transaction process of the supply and demand sides and automatically generate corresponding countermeasures. The application combines blockchain and artificial intelligence technology to improve the efficiency and risk control capability of new energy power transactions.

Description

Technical Field

[0001] This invention relates to the field of new energy power trading technology, and in particular to a new energy power trading method and system based on blockchain and artificial intelligence. Background Technology

[0002] Energy is the foundation of modern society, supporting economic and social operations. New energy sources, in their clean and renewable form, have become a global focus in the energy sector. New energy power trading specifically refers to medium- and long-term power transactions using green electricity products as the underlying asset, designed to meet the demand of electricity users for purchasing and consuming green electricity. However, traditional power market trading mechanisms have revealed several shortcomings in addressing the challenges of the rapidly growing new energy industry, including information asymmetry, high transaction costs, and low efficiency. Information asymmetry makes it difficult for market participants to obtain accurate market information, increasing uncertainty and risk in transactions. Transactions involve multiple intermediaries and complex settlement processes, resulting in high transaction costs and limiting market activity and development potential. Furthermore, the centralized data structure of traditional trading mechanisms is vulnerable to single points of failure and data tampering, lacking transparency and trust, thus affecting fair competition and healthy market development. Summary of the Invention

[0003] The purpose of this invention is to provide a new energy power trading method and system based on blockchain and artificial intelligence, thereby improving the efficiency and risk control capabilities of new energy power trading by combining blockchain and artificial intelligence technologies.

[0004] To achieve the above objectives, the first aspect of this invention provides a new energy power trading method based on blockchain and artificial intelligence, the method comprising:

[0005] By analyzing historical smart contracts using artificial intelligence algorithms, a smart contract analysis model is constructed. This model is then connected to external data sources to make real-time adjustments to the terms of existing smart contracts.

[0006] By combining artificial intelligence algorithms with meteorological data and historical power generation data, real-time new energy power generation is predicted, and historical transaction data among participants is analyzed to obtain real-time prediction results and personalized transaction characteristics of each participant.

[0007] The smart contract terms are screened, combined, and optimized based on the participants' personalized transaction characteristics to generate exclusive contract terms that meet the participants' needs.

[0008] Based on real-time forecast results and exclusive contract terms tailored to the needs of each participant, a risk assessment model for new energy power trading is constructed by dynamically matching supply and demand through artificial intelligence algorithms. This model evaluates real-time transaction data during the trading process between supply and demand parties and automatically generates corresponding response strategies based on the evaluation results.

[0009] Furthermore, existing smart contract terms will be adjusted in real time, specifically including:

[0010] Collect historical smart contract data, extract key information from the historical smart contract data, and construct a smart contract knowledge graph based on the extracted key information;

[0011] Based on the implicit relationships, potential logical loopholes, and possible improvement directions of the terms learned through the reasoning of the smart contract knowledge graph, a smart contract analysis model is constructed.

[0012] The smart contract analysis model is connected to external data sources to analyze the data updated in real time from the external data sources, and the existing smart contract terms are adjusted in real time based on the analysis results.

[0013] Furthermore, generate customized contract terms that meet the needs of the participants, specifically including:

[0014] Based on the smart contract knowledge graph, candidate contract terms that meet the needs of participants are selected according to their personalized transaction characteristics.

[0015] The candidate contract terms are combined to obtain a draft contract;

[0016] The draft contract is input into the smart contract analysis model for evaluation and optimization, generating exclusive contract terms that meet the needs of the participants.

[0017] Furthermore, the draft contract will be optimized, specifically including:

[0018] The combined contract terms are represented as chromosomes, with each term as a gene. The value of a gene is whether the term is selected or a specific parameter value of the term.

[0019] A certain number of chromosomes are randomly generated as the initial population, and each chromosome represents a possible combination of contract terms.

[0020] Define a fitness function to measure the quality of the contract terms combination scheme represented by each chromosome. Based on the value of the fitness function, a selection strategy is adopted to select chromosomes with a fitness value greater than the preset fitness value from the current population as parent individuals to participate in crossover and mutation operations.

[0021] Repeatedly perform selection, crossover, and mutation operations to generate a new generation of population, calculate the fitness value of each individual in the new generation, replace the current population with the new generation, and continue iterative updates until the termination condition is met, and output exclusive contract terms that meet the needs of the participants.

[0022] Furthermore, real-time forecasting of new energy power generation capacity includes:

[0023] Acquire real-time meteorological data and historical power generation data of corresponding new energy power generation devices. The historical power generation data includes historical operating data and corresponding historical meteorological data. Extract features from the historical operating data and the corresponding historical meteorological data to obtain historical first power generation feature data and historical second power generation feature data.

[0024] The correlation feature data between the historical first power generation feature data and the historical second power generation feature data is extracted. The historical first power generation feature data, the historical second power generation feature data and the correlation feature data are input into the preset power prediction model for training, and the power prediction model of the new energy power plant is obtained.

[0025] The power prediction model includes a historical power generation data analysis sub-model and a power prediction sub-model. Real-time meteorological data and the latest historical power generation data of the corresponding new energy power generation devices are input into the power prediction model to predict the power generation data of the new energy power plant in the future target time period.

[0026] Furthermore, historical transaction data among participants is analyzed, specifically including:

[0027] Complete transaction records among participants are obtained from the new energy power trading platform. These records are analyzed, and a transaction network diagram is constructed. Network analysis is used to identify participants' trading strategies, trading preferences, risk preferences, and credit status.

[0028] Participants are clustered based on their trading strategies, trading preferences, risk appetite, and credit history.

[0029] Correlation analysis was performed on the transaction characteristics of participants in the completed clustering to obtain the personalized transaction characteristics of each participant.

[0030] Furthermore, artificial intelligence algorithms are used to dynamically match supply and demand, specifically including:

[0031] Build a transaction matching model, input the exclusive contract terms required by both the supply and demand sides into the transaction matching model, and output an initial matching scheme;

[0032] It monitors market dynamics and changes in the situation of both parties in real time, dynamically adjusts the matching strategy based on the initial matching plan, and searches for the optimal matching plan according to preset rules.

[0033] Furthermore, corresponding response strategies are automatically generated, specifically including:

[0034] Establish a transaction risk assessment model, take real-time transaction data and risk indicators of participants based on the optimal matching scheme as input to the model, assess the risk of each transaction in real time, and output the risk assessment results;

[0035] Analyze and mine historical trading cases and response strategies to build a risk response strategy library;

[0036] Based on the risk assessment results, combined with the risk response strategy library and real-time market conditions, corresponding response strategies are automatically generated, and the generated response strategies are automatically executed through smart contracts.

[0037] A second aspect of this invention provides a new energy power trading system based on blockchain and artificial intelligence, the system comprising:

[0038] The smart contract optimization module is used to analyze historical smart contracts through artificial intelligence algorithms, build smart contract analysis models, connect smart contract analysis models with external data sources, and make real-time adjustments to existing smart contract terms.

[0039] The real-time market monitoring module is used to predict the real-time power generation of new energy sources by combining meteorological data and historical power generation data with artificial intelligence algorithms, and to analyze the historical transaction data among participants to obtain real-time prediction results and personalized transaction characteristics of each participant.

[0040] The smart contract matching module is used to filter, combine, and optimize smart contract terms based on the personalized transaction characteristics of participants, and generate exclusive contract terms that meet the needs of participants. Based on real-time prediction results and exclusive contract terms for each participant's needs, it dynamically matches supply and demand sides through artificial intelligence algorithms.

[0041] The risk assessment and response module is used to build a risk assessment model for new energy power trading, evaluate real-time transaction data during the transaction process between supply and demand parties, and automatically generate corresponding response strategies based on the assessment results.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] This invention provides a new energy power trading method and system based on blockchain and artificial intelligence. By constructing a smart contract analysis model and connecting to external data sources, the system adjusts smart contract terms in real time, enabling automatic updates to contract terms based on changes in the external environment during the new energy power trading process. It analyzes historical transaction data among participants to obtain personalized transaction characteristics for each participant. An AI decision-making model then filters, combines, and optimizes smart contract terms based on these personalized characteristics, generating customized contract terms for each participant to meet their specific needs. AI algorithms dynamically match supply and demand sides, quickly and efficiently connecting power suppliers and demanders. A newly constructed new energy power trading risk assessment model evaluates real-time transaction data between participants, promptly identifying potential risks and automatically generating corresponding response strategies based on the assessment results, automatically triggering allocation strategies to ensure the stability and reliability of the transaction. This invention improves the efficiency and risk control capabilities of new energy power trading by combining blockchain and artificial intelligence technologies. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0045] Figure 1 This invention provides a schematic diagram of a new energy power trading method based on blockchain and artificial intelligence.

[0046] Figure 2 This invention provides a schematic diagram of a new energy power trading system based on blockchain and artificial intelligence. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0048] Reference Figure 1 The first aspect of this embodiment provides a new energy power trading method based on blockchain and artificial intelligence, the method comprising:

[0049] By analyzing historical smart contracts using artificial intelligence algorithms, a smart contract analysis model is constructed. This model is then connected to external data sources to make real-time adjustments to the terms of existing smart contracts.

[0050] By combining artificial intelligence algorithms with meteorological data and historical power generation data, real-time renewable energy power generation is predicted, and historical transaction data among participants is analyzed to obtain real-time prediction results and personalized transaction characteristics for each participant.

[0051] The smart contract terms are screened, combined, and optimized based on the participants' personalized transaction characteristics to generate exclusive contract terms that meet the participants' needs. Based on real-time prediction results and the exclusive contract terms for each participant's needs, the supply and demand sides are dynamically matched through artificial intelligence algorithms.

[0052] A risk assessment model for new energy power trading is constructed to evaluate real-time transaction data during the trading process between supply and demand parties, and to automatically generate corresponding response strategies based on the assessment results.

[0053] In this embodiment, artificial intelligence algorithms are used to analyze a large amount of historical smart contracts and related business data to construct a smart contract analysis model, learning its clause logic, structural patterns, and common problems. By connecting the smart contract analysis model to external data sources, such as government regulatory websites, industry news platforms, and market data databases, information such as changes in laws and regulations, updates to industry standards, and market trends can be obtained in real time. The smart contract analysis model automatically analyzes and proposes modifications to the smart contract clauses, and updates the smart contract promptly after review and approval by relevant parties. For example, when relevant tax regulations are adjusted, the smart contract analysis model immediately detects this change and automatically updates the tax calculation clauses in the contract to ensure the legality and adaptability of the contract.

[0054] New energy power generation is significantly affected by meteorological conditions, resulting in fluctuating and uncertain power output. By combining meteorological data and historical power generation data, artificial intelligence algorithms can more accurately predict real-time new energy power generation. For example, predicting changes in wind speed or solar radiation intensity in advance can forecast power generation, allowing power generation companies to make advance trading arrangements in the electricity market and avoid transaction defaults or economic losses due to discrepancies between power generation and expectations. Electricity market participants include power generation companies, power suppliers, and power users. By analyzing historical trading data, the personalized trading characteristics of each participant can be identified. For example, based on trading price preferences, trading volume demands, and trading time preferences, customized trading schemes and contract terms can be provided to different participants, achieving optimal allocation of electricity resources.

[0055] By generating customized contract terms for different participants based on their individual transaction characteristics, the system can precisely meet the needs of each participant. For example, for electricity users who prioritize cost control, contract terms including more favorable electricity prices can be generated; for users with extremely high requirements for power supply stability, corresponding guarantee clauses and compensation mechanisms can be set. A risk assessment model is constructed to evaluate real-time transaction data during the transaction process, promptly identifying potential risk factors such as market price fluctuations, power supply interruptions, and transaction defaults. Through real-time risk monitoring and analysis, and by automatically generating corresponding response strategies based on the assessment results, the system can quickly respond to various risk situations and reduce risk losses. For example, when a significant market price fluctuation is detected, the system can automatically adjust the transaction price or volume to reduce the impact of market risk on participants; when a potential power supply shortage is predicted, backup power sources can be activated in advance or electricity load can be adjusted to ensure the stability of the power supply.

[0056] Real-time adjustments to existing smart contract terms, specifically including:

[0057] Collect historical smart contract data, extract key information from the historical smart contract data, and build a smart contract knowledge graph based on the extracted key information.

[0058] Based on the implicit relationships, potential logical loopholes, and possible improvement directions among the terms of a smart contract, a smart contract analysis model is constructed.

[0059] The smart contract analysis model is connected to external data sources to analyze the data updated in real time from the external data sources, and the existing smart contract terms are adjusted in real time based on the analysis results.

[0060] In this embodiment, historical smart contract data is collected and a smart contract knowledge graph is constructed to more accurately understand and analyze key information in smart contract terms. Based on the smart contract knowledge graph, the implicit relationships between terms are reasoned and learned, thereby discovering potential logical vulnerabilities and ensuring compliance with relevant laws, regulations, and industry standards. Potential logical vulnerabilities can lead to problems such as financial losses and transaction disputes during contract execution. Early detection and improvement of these vulnerabilities can enhance the security of smart contracts and reduce transaction risks. The smart contract analysis model, through connection with external data sources, adjusts contract terms in real time, ensuring that smart contracts always meet the latest compliance requirements and avoiding legal issues caused by non-compliance.

[0061] Specifically, a large amount of historical smart contract data, including smart contract code, transaction records, and development documents, is acquired through various channels such as blockchain explorers, smart contract databases, and developer communities related to new energy power trading. The collected smart contract data is cleaned and formatted to remove invalid information and duplicate content, and to standardize the text format. Natural language processing tools and technologies, such as lexical analysis, syntactic analysis, and named entity recognition, are used to extract key information from the smart contract text. This extracted key information is then labeled and categorized to form a structured data format. Based on the key information of the smart contracts, a knowledge graph structure is designed, including nodes and edges. Nodes represent entities such as contract subjects and terms, while edges represent relationships between entities, such as "containment" and "association."

[0062] By leveraging information from the knowledge graph and employing machine learning and inference algorithms, the implicit relationships, potential logical flaws, and possible improvement directions among smart contract terms are learned. For example, by analyzing the correlations between terms, it can be discovered that certain combinations of terms may lead to contradictions or loopholes in contract execution. The smart contract analysis model is connected to external data sources related to renewable energy power trading, and the real-time updated data from these external data sources is analyzed. Combined with information from the smart contract knowledge graph, existing smart contract terms are adjusted in real time. For instance, when market price fluctuations or changes in weather conditions may affect renewable energy power generation and trading, the price or delivery terms in the smart contract are modified promptly based on the analysis results, enabling the contract to adapt to the new trading environment and requirements.

[0063] Generate customized contract terms that meet the needs of the participants, specifically including:

[0064] Based on the smart contract knowledge graph, candidate contract terms that meet the needs of participants are selected according to their personalized transaction characteristics.

[0065] The candidate contract terms are combined to obtain a draft contract.

[0066] The draft contract is input into the smart contract analysis model for evaluation and optimization, generating exclusive contract terms that meet the needs of the participants.

[0067] In this embodiment, based on the personalized transaction characteristics of participants, terms are selected and combined from the smart contract knowledge graph to generate customized contracts that precisely meet their needs, improving participant satisfaction. Both electricity users and power generation companies can obtain contract terms that suit their specific circumstances and requirements. For example, small electricity users can customize small-scale electricity trading and flexible billing contracts, while new energy power generation companies can customize contracts that include consumption guarantees and subsidy settlements. Natural language processing technology is used to quickly combine candidate terms into drafts, changing the time-consuming and inefficient manual drafting process and accelerating the launch of new energy electricity trading. The automatic evaluation and optimization of draft contracts quickly identifies and corrects problems, reducing repeated manual review and modification, making the transaction process smoother and more efficient.

[0068] Specifically, natural language processing technology is used to combine and adjust the selected candidate terms to generate a preliminary draft contract. This draft contract is then input into a smart contract analysis model for comprehensive evaluation, including the completeness, logical consistency, compliance, and potential risks of the terms. For example, it checks for contradictory terms, compliance with relevant laws and regulations, and coverage of all necessary transaction details. Based on the evaluation results, the draft contract is optimized and adjusted, including modifying the content of the terms, supplementing missing terms, and adjusting the order of terms, to ensure that the final customized contract terms meet the needs of the participants and are legally and commercially feasible. The optimized draft contract is then reviewed again to ensure its accuracy and applicability, ultimately generating customized contract terms that meet the needs of the participants. This is then deployed and executed through the smart contract platform. Once generated, the contract can be used in new energy power trading to ensure the smooth progress of transactions.

[0069] The draft contract has been optimized, specifically including:

[0070] The combined contract terms are represented as chromosomes, with each term as a gene. The value of a gene is either whether the term is selected or a specific parameter value of the term.

[0071] A certain number of chromosomes are randomly generated as the initial population, and each chromosome represents a possible combination of contract terms.

[0072] Define a fitness function to measure the quality of the contract terms combination scheme represented by each chromosome. Based on the value of the fitness function, a selection strategy is adopted to select chromosomes with a fitness value greater than the preset fitness value from the current population as parent individuals to participate in crossover and mutation operations.

[0073] Repeatedly perform selection, crossover, and mutation operations to generate a new generation of population, calculate the fitness value of each individual in the new generation, replace the current population with the new generation, and continue iterative updates until the termination condition is met, and output exclusive contract terms that meet the needs of the participants.

[0074] In this embodiment, the combinations of contract terms are diverse, and traditional methods struggle to exhaust all possibilities and find the optimal solution. By representing contract terms as chromosomes, and using the selection of a term or parameter value as gene values, a genetic algorithm framework can effectively explore the vast and complex combination space and discover potential high-quality solutions. A fitness function is defined to quantify the quality of contract term combinations. Chromosomes with better performance are selected as parent individuals to participate in genetic operations based on their fitness values. This gradually eliminates inferior solutions while preserving and passing on the characteristics of high-quality solutions. Crossover and mutation operations continuously introduce new features and diversity, preventing the user from getting trapped in local optima, and ultimately outputting exclusive contract terms that meet the needs of the participants. Participants' needs are often complex and diverse, involving the synergistic effects of multiple terms and parameters. Genetic algorithms can handle complex optimization problems with multiple objectives and constraints, comprehensively considering the mutual influence between different terms and their matching degree with the participants' needs, thereby generating contract term combinations that meet individual requirements and are optimal overall.

[0075] Real-time forecasting of renewable energy power generation includes:

[0076] Real-time meteorological data and historical power generation data of corresponding new energy power generation devices are acquired. The historical power generation data includes historical operating data and corresponding historical meteorological data. Feature extraction is performed on the historical operating data and the corresponding historical meteorological data to obtain historical first power generation feature data and historical second power generation feature data.

[0077] The correlation features between the historical first power generation feature data and the historical second power generation feature data are extracted. The historical first power generation feature data, the historical second power generation feature data and the correlation features are input into the preset power prediction model for training, and the power prediction model of the new energy power plant is obtained.

[0078] The power prediction model includes a historical power generation data analysis sub-model and a power prediction sub-model. Real-time meteorological data and the latest historical power generation data of the corresponding new energy power generation devices are input into the power prediction model to predict the power generation data of the new energy power plant in the future target time period.

[0079] In this embodiment, by combining real-time meteorological data and historical power generation data from new energy power generation devices, various factors affecting power generation are captured more comprehensively. Feature extraction is performed on historical operational data and historical meteorological data respectively, and correlation algorithms are used to extract correlation features between them, more accurately identifying key factors that significantly affect power generation. New energy power generation is highly volatile and uncertain due to meteorological conditions. By acquiring meteorological data in real time and combining it with historical data for prediction, the impact of meteorological changes on power generation can be reflected in a timely manner, thereby helping power dispatching departments better plan power production and distribution, optimize grid operation, and reduce power supply instability caused by the volatility of new energy power generation. The extracted feature data is input into a preset power prediction model for training, continuously optimizing model parameters and improving the model's predictive performance. Simultaneously, the power prediction model is decomposed into a historical power generation data analysis sub-model and a power prediction sub-model, more effectively handling different types of input data and improving the overall performance of the model.

[0080] Specifically, real-time meteorological data, including wind speed, wind direction, temperature, humidity, and solar radiation, is acquired from meteorological stations and satellites. Historical power generation data, including power output, equipment operating status, and fault records, is obtained from the monitoring system of new energy power generation devices. After cleaning, denoising, handling missing values, and format conversion of the collected data, features are extracted from both historical operating data and historical meteorological data. Correlation algorithms, such as Pearson correlation coefficient and Spearman rank correlation coefficient, are used to calculate the correlation between historical power generation feature data and extract correlation features that have a significant impact on power output. A power prediction model is built based on a neural network, and the extracted feature data is input into the power prediction model for training. In actual operation, real-time meteorological data and the latest historical power generation data of the corresponding new energy power generation devices are input into the trained power prediction model to predict the power output data of the new energy power plant in the future target time period. For example, predicting the hourly power output in the next 24 hours provides a reference for power dispatch.

[0081] Analyzing historical transaction data among participants, specifically including:

[0082] Complete transaction records among participants are obtained from the new energy power trading platform. These records are analyzed, and a transaction network diagram is constructed. Network analysis is used to identify participants' trading strategies, trading preferences, risk preferences, and credit status.

[0083] Participants are clustered based on their trading strategies, trading preferences, risk appetite, and credit history.

[0084] Correlation analysis was performed on the transaction characteristics of participants in the completed clustering to obtain the personalized transaction characteristics of each participant.

[0085] In this embodiment, historical transaction data is analyzed using network analysis to construct a transaction network graph, identifying transaction strategies, preferences, risk appetite, and credit status, and visually displaying the transaction relationships between participants. A clustering algorithm is used to group participants, enabling refined management of participant groups. Based on the clustering results, an association rule mining algorithm is applied to conduct in-depth analysis of the transaction characteristics of each cluster group, thereby obtaining the personalized transaction characteristics of each participant and providing a basis for personalized services and precision marketing.

[0086] Specifically, complete transaction records among participants are obtained from the new energy power trading platform database, covering information such as the trading parties, time, electricity volume, price, and whether there is a default. The transaction records are denoised, missing values ​​are filled in, and format conversion is performed to ensure data quality and consistency, laying the foundation for subsequent analysis. A transaction network graph is constructed with participants as nodes and transaction relationships as edges. Edge weights are set based on transaction frequency and electricity volume to intuitively present transaction activity and closeness. Network analysis is used to mine participants' trading strategies and preferences. Node degree centrality and betweenness centrality are calculated to identify active participants and those with mediating advantages. Community detection algorithms are used to reveal trading groups, reflecting trading preferences and patterns. Simultaneously, credit assessment models are combined to analyze creditworthiness, identifying participants with high default risk. Clustering algorithms are selected to group participants based on trading strategies, preferences, risk appetite, and creditworthiness. Apriori or FP-Growth algorithms are used to mine association rules of participant transaction characteristics. Personalized transaction characteristics are extracted based on these association rules, such as a participant having large transaction volume, conservative risk appetite, and high credit, forming a comprehensive personalized feature profile.

[0087] Dynamically matching supply and demand through artificial intelligence algorithms, specifically including:

[0088] Construct a transaction matching model by inputting the exclusive contract terms required by both the supply and demand sides into the transaction matching model, and output an initial matching scheme.

[0089] It monitors market dynamics and changes in the situation of both parties in real time, dynamically adjusts the matching strategy based on the initial matching plan, and searches for the optimal matching plan according to preset rules.

[0090] In this embodiment, by constructing a transaction matching model that comprehensively considers the needs of both supply and demand sides and inputting exclusive contract terms, an initial matching scheme can be quickly generated. The introduction of a reinforcement learning agent enables the matching process to adapt to market dynamics and changes in the trading parties in real time, continuously adjusting the matching strategy to maintain efficient matching capabilities in a constantly changing market environment. The new energy power market is highly uncertain and dynamic; the needs of both supply and demand sides and market conditions may change at any time. Through a dynamic matching mechanism, these changes can be responded to quickly, improving the market's adaptability and flexibility, and promoting the healthy development of the market.

[0091] Specifically, a transaction matching model and a reinforcement learning agent are integrated into a new energy power trading platform to form a complete dynamic matching system. This system acquires dynamic market data and real-time information from both parties, such as electricity price fluctuations and changes in supply and demand. The reinforcement learning agent monitors market data and information from both parties in real time, making decisions based on the current state and preset rules, and dynamically adjusting the initial matching scheme. The system then pushes the new matching scheme to both parties to facilitate the transaction. Simultaneously, the adjusted matching results are recorded for further training and optimization of the agent.

[0092] Automatically generate corresponding response strategies, including:

[0093] A transaction risk assessment model is established, which takes real-time transaction data and risk indicators of participants based on the optimal matching scheme as input to the model, assesses the risk of each transaction in real time, and outputs the risk assessment results.

[0094] We analyze and mine historical transaction cases and response strategies to build a risk response strategy library.

[0095] Based on the risk assessment results, combined with the risk response strategy library and real-time market conditions, corresponding response strategies are automatically generated, and the generated response strategies are automatically executed through smart contracts.

[0096] In this embodiment, a transaction risk assessment model is established using machine learning algorithms to evaluate the risk of each transaction in real time. Simultaneously, cluster analysis and association rule mining are used to construct a risk response strategy library, enabling real-time monitoring and rapid response to transaction risks, thereby improving transaction security and stability and reducing risk losses. Response strategies are automatically generated and executed based on real-time market conditions, ensuring timely and accurate responses and improving transaction efficiency. Furthermore, by analyzing and mining historical transaction cases and response strategies, comprehensive and effective solutions can be provided for various risk scenarios, enhancing the system's risk response capabilities.

[0097] Reference Figure 2The second aspect of this embodiment provides a new energy power trading system based on blockchain and artificial intelligence, the system comprising:

[0098] The smart contract optimization module is used to analyze historical smart contracts through artificial intelligence algorithms, build smart contract analysis models, connect smart contract analysis models with external data sources, and make real-time adjustments to existing smart contract terms.

[0099] The real-time market monitoring module is used to predict real-time renewable energy power generation by combining meteorological data and historical power generation data with artificial intelligence algorithms, and to analyze historical transaction data among participants to obtain real-time prediction results and personalized transaction characteristics of each participant.

[0100] The smart contract matching module is used to filter, combine, and optimize smart contract terms based on the personalized transaction characteristics of participants, generating exclusive contract terms that meet the needs of participants. Based on real-time prediction results and exclusive contract terms for each participant's needs, it dynamically matches supply and demand sides through artificial intelligence algorithms.

[0101] The risk assessment and response module is used to build a risk assessment model for new energy power trading, evaluate real-time transaction data during the transaction process between supply and demand parties, and automatically generate corresponding response strategies based on the assessment results.

[0102] In this embodiment, within the new energy power trading system, the smart contract optimization module first constructs a smart contract analysis model by analyzing historical smart contracts and then connects to external data sources to adjust existing smart contract terms in real time. The real-time market monitoring module combines meteorological data and historical power generation data to predict real-time new energy power generation, while simultaneously analyzing participants' historical trading data to identify each participant's personalized trading characteristics. The smart contract matching module filters, combines, and optimizes smart contract terms based on personalized trading characteristics, generating exclusive contract terms. It then combines real-time prediction results with these exclusive contract terms, using artificial intelligence algorithms to dynamically match supply and demand. Finally, the risk assessment and response module constructs a risk assessment model, evaluates trading data in real time, and automatically generates response strategies. These four modules work closely together to form an automated and efficient new energy power trading process.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A new energy power trading method based on blockchain and artificial intelligence, characterized in that, The method includes: By analyzing historical smart contracts using artificial intelligence algorithms, a smart contract analysis model is constructed. This model is then connected to external data sources to make real-time adjustments to the terms of existing smart contracts. Real-time adjustments to existing smart contract terms, specifically including: Collect historical smart contract data, extract key information from the historical smart contract data, and construct a smart contract knowledge graph based on the extracted key information; Based on the implicit relationships, potential logical loopholes, and improvement directions of the terms learned through the reasoning of the smart contract knowledge graph, a smart contract analysis model is constructed. Connect the smart contract analysis model with external data sources, analyze the data updated in real time from the external data sources, and adjust the existing smart contract terms in real time based on the analysis results; By combining artificial intelligence algorithms with meteorological data and historical power generation data, the real-time power generation of new energy sources is predicted, and the historical transaction data among participants is analyzed to obtain real-time prediction results and personalized transaction characteristics of each participant. The smart contract terms are screened, combined and optimized based on the participants' personalized transaction characteristics to generate exclusive contract terms that meet the participants' needs. Based on real-time prediction results and the exclusive contract terms for each participant's needs, the supply and demand sides are dynamically matched through artificial intelligence algorithms. Generate customized contract terms that meet the needs of the participants, specifically including: Based on the smart contract knowledge graph, candidate contract terms that meet the needs of participants are selected according to their personalized transaction characteristics. The candidate contract terms are combined to obtain a draft contract; The draft contract is input into the smart contract analysis model for evaluation, and the draft contract is optimized to generate exclusive contract terms that meet the needs of the participants. A risk assessment model for new energy power trading is constructed to evaluate real-time transaction data during the trading process between supply and demand parties, and to automatically generate corresponding response strategies based on the assessment results. Automatically generate corresponding response strategies, including: Establish a transaction risk assessment model, take real-time transaction data and risk indicators of participants based on the optimal matching scheme as input to the model, assess the risk of each transaction in real time, and output the risk assessment results; Analyze and mine historical trading cases and response strategies to build a risk response strategy library; Based on the risk assessment results, combined with the risk response strategy library and real-time market conditions, corresponding response strategies are automatically generated, and the generated response strategies are automatically executed through smart contracts.

2. The new energy power trading method based on blockchain and artificial intelligence according to claim 1, characterized in that, The draft contract has been optimized, specifically including: The combined contract terms are represented as chromosomes, with each term as a gene. The value of a gene is whether the term is selected or a specific parameter value of the term. A certain number of chromosomes are randomly generated as the initial population, and each chromosome represents a combination of contract terms. Define a fitness function to measure the quality of the contract terms combination scheme represented by each chromosome. Based on the value of the fitness function, a selection strategy is adopted to select chromosomes with a fitness value greater than the preset fitness value from the current population as parent individuals to participate in crossover and mutation operations. Repeatedly perform selection, crossover, and mutation operations to generate a new generation of population, calculate the fitness value of each individual in the new generation, replace the current population with the new generation, and continue iterative updates until the termination condition is met, and output exclusive contract terms that meet the needs of the participants.

3. The new energy power trading method based on blockchain and artificial intelligence according to claim 1, characterized in that, Real-time forecasting of renewable energy power generation includes: Acquire real-time meteorological data and historical power generation data of corresponding new energy power generation devices. The historical power generation data includes historical operating data and corresponding historical meteorological data. Extract features from the historical operating data and the corresponding historical meteorological data to obtain historical first power generation feature data and historical second power generation feature data. The correlation feature data between the historical first power generation feature data and the historical second power generation feature data is extracted. The historical first power generation feature data, the historical second power generation feature data and the correlation feature data are input into the preset power prediction model for training, and the power prediction model of the new energy power plant is obtained. The power prediction model includes a historical power generation data analysis sub-model and a power prediction sub-model. Real-time meteorological data and the latest historical power generation data of the corresponding new energy power generation devices are input into the power prediction model to predict the power generation data of the new energy power plant in the future target time period.

4. The new energy power trading method based on blockchain and artificial intelligence according to claim 1, characterized in that, Analyzing historical transaction data among participants, specifically including: Complete transaction records among participants are obtained from the new energy power trading platform. These records are analyzed, and a transaction network diagram is constructed. Network analysis is used to identify participants' trading strategies, trading preferences, risk preferences, and credit status. Participants are clustered based on their trading strategies, trading preferences, risk appetite, and credit history. Correlation analysis was performed on the transaction characteristics of participants in the completed clustering to obtain the personalized transaction characteristics of each participant.

5. The new energy power trading method based on blockchain and artificial intelligence according to claim 1, characterized in that, Dynamically matching supply and demand through artificial intelligence algorithms, specifically including: Build a transaction matching model, input the exclusive contract terms required by both the supply and demand sides into the transaction matching model, and output an initial matching scheme; It monitors market dynamics and changes in the situation of both parties in real time, dynamically adjusts the matching strategy based on the initial matching plan, and searches for the optimal matching plan according to preset rules.

6. A new energy power trading system based on blockchain and artificial intelligence, characterized in that, The system includes: The smart contract optimization module is used to analyze historical smart contracts through artificial intelligence algorithms, build smart contract analysis models, connect smart contract analysis models with external data sources, and make real-time adjustments to existing smart contract terms. Real-time adjustments to existing smart contract terms, specifically including: Collect historical smart contract data, extract key information from the historical smart contract data, and construct a smart contract knowledge graph based on the extracted key information; Based on the implicit relationships, potential logical loopholes, and improvement directions of the terms learned through the reasoning of the smart contract knowledge graph, a smart contract analysis model is constructed. Connect the smart contract analysis model with external data sources, analyze the data updated in real time from the external data sources, and adjust the existing smart contract terms in real time based on the analysis results; The real-time market monitoring module is used to predict the real-time power generation of new energy sources by combining meteorological data and historical power generation data with artificial intelligence algorithms, and to analyze the historical transaction data among participants to obtain real-time prediction results and personalized transaction characteristics of each participant. The smart contract matching module is used to filter, combine, and optimize smart contract terms based on the personalized transaction characteristics of participants, and generate exclusive contract terms that meet the needs of participants. Based on real-time prediction results and exclusive contract terms for each participant's needs, it dynamically matches supply and demand sides through artificial intelligence algorithms. Generate customized contract terms that meet the needs of the participants, specifically including: Based on the smart contract knowledge graph, candidate contract terms that meet the needs of participants are selected according to their personalized transaction characteristics. The candidate contract terms are combined to obtain a draft contract; The draft contract is input into the smart contract analysis model for evaluation, and the draft contract is optimized to generate exclusive contract terms that meet the needs of the participants. The risk assessment and response module is used to build a risk assessment model for new energy power trading, assess real-time transaction data during the transaction process between supply and demand parties, and automatically generate corresponding response strategies based on the assessment results. Automatically generate corresponding response strategies, including: Establish a transaction risk assessment model, take real-time transaction data and risk indicators of participants based on the optimal matching scheme as input to the model, assess the risk of each transaction in real time, and output the risk assessment results; Analyze and mine historical trading cases and response strategies to build a risk response strategy library; Based on the risk assessment results, combined with the risk response strategy library and real-time market conditions, corresponding response strategies are automatically generated, and the generated response strategies are automatically executed through smart contracts.

Images