Energy transaction optimization management method based on privacy protection block chain

Abstract

The invention relates to the technical field of transaction optimization, in particular to an energy transaction optimization management method based on a privacy protection block chain, and the method comprises the following steps: collecting data of energy transaction participants and transaction records, encrypting the transaction records through employing a digital signature, checking the safety of energy transaction information, and generating an encrypted transaction data set. According to the invention, the delay of information transmission is reduced through the operation of capturing and updating the block chain account book in real time, so that the market response is more agile, the method is very important for the rapidly changing energy market, and the classification and risk assessment of transactions not only help to identify abnormal transactions, but also further enhance the ability to prevent potential fraud activities, thereby improving the safety of the system. By optimizing the transaction verification rule and the execution path, the energy transaction processing efficiency is improved, the energy distribution is optimized, the resource allocation is more reasonable, and the market competitiveness and sustainability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of transaction optimization, and in particular to an energy transaction optimization management method based on a privacy-preserving blockchain. Background Art

[0002] The field of transaction optimization technology involves improving the efficiency and effectiveness of trading systems, especially in energy markets such as electricity and natural gas. The key goal is to predict market demand, adjust supply, manage risks, and optimize pricing strategies through advanced algorithms and data analysis. Transaction optimization also includes real-time monitoring of market dynamics in order to quickly respond to price changes and supply and demand imbalances. With the advancement of technology, blockchain technology has been introduced into transaction optimization because it provides transparent, secure, and decentralized record keeping features, which are particularly important in complex trading networks.

[0003] Among them, the energy transaction optimization management method of privacy-preserving blockchain refers to the method of using blockchain technology to improve the energy transaction process, ensure the security and privacy of transaction data, and optimize energy distribution and pricing. By using the distributed ledger technology of blockchain to record each transaction, it ensures that the data cannot be tampered with and transparent, and introduces encryption technology to protect user privacy. It can not only reduce fraud and improve transaction efficiency, but also help market participants make more accurate supply and demand forecasts and achieve optimal allocation of resources. It is widely used in power trading markets, renewable energy certificate transactions, and cross-border energy transactions.

[0004] Although existing transaction optimization technologies have achieved automation and efficiency improvement in many aspects, they are still insufficient in real-time data processing and security. For example, in the electricity and natural gas markets, the response speed to real-time market dynamics and the strength of security protection measures cannot fully meet the rapidly changing market needs. The lack of sufficient real-time monitoring and data security measures leads to untimely response to market trends, missed optimal trading opportunities or inability to effectively prevent fraud. Technological lags will lead to a decline in market efficiency, uneven distribution of energy resources, and affect the healthy development of the entire market and the implementation of energy policies. Insufficient data protection measures lead to user data leakage, increase the operational risks of market participants, and further inhibit market vitality. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and propose an energy transaction optimization management method based on privacy-preserving blockchain.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: an energy transaction optimization management method based on privacy protection blockchain, comprising the following steps:

[0007] S1: Collect data on energy transaction participants and transaction records, encrypt transaction records using digital signatures, verify the security of energy transaction information, and generate encrypted transaction data sets;

[0008] S2: Based on the encrypted transaction data set, energy transaction activities in the privacy-protected blockchain are captured in real time, transaction data is collected and synchronously updated to the ledger on the blockchain, and real-time monitoring data is obtained;

[0009] S3: using the real-time monitoring data, classifying and marking energy transactions on the blockchain, identifying abnormal transactions that do not conform to the rules, marking risky transactions through abnormal pattern analysis, and obtaining risky transaction marking results;

[0010] S4: According to the risk transaction marking result, optimize the energy transaction verification rules, adjust the difficulty and incentive mechanism of blockchain verification, avoid risky transactions, and obtain adjusted transaction verification rules;

[0011] S5: Based on the adjusted transaction verification rules, the energy transaction execution path is optimized, compliance transactions are processed preferentially, the processing efficiency of energy transactions is optimized on the basis of protecting privacy, and a transaction execution efficiency table is obtained;

[0012] S6: Using the transaction execution efficiency table, identify energy transactions and usage, record the energy type, transaction frequency and transaction size of the participants, analyze and optimize energy transaction strategies, and generate energy usage results.

[0013] As a further solution of the present invention, the encrypted transaction data set includes transaction timestamp, participant identification, transaction amount and energy type, the real-time monitoring data includes energy transaction time, identities of both parties to the transaction, transaction volume and transaction value, the risk transaction marking results include transaction ID, risk level, transaction participants and abnormal behavior type, the adjusted transaction verification rules include new verification thresholds, reward ratios and risk control parameters, the transaction execution efficiency table includes transaction response time, transaction success rate and priority processing transaction ratio, and the energy usage results include total energy consumption, energy saving efficiency and optimized energy allocation strategy.

[0014] As a further solution of the present invention, data of energy transaction participants and transaction records are collected, transaction records are encrypted using digital signatures, and the security of energy transaction information is checked. The steps of generating an encrypted transaction data set are specifically as follows:

[0015] S101: Collect data on energy trading participants and transaction records, including energy types, transaction volumes, transaction prices, and transaction times, and classify and organize the information to obtain original transaction data sets;

[0016] S102: encrypting the transaction data using the original transaction data set, applying a digital signature to each record, checking the security of the transaction data, and verifying the encrypted data to generate an encrypted transaction record;

[0017] S103: Analyze the integrity of the transaction record through the encrypted transaction record, verify the consistency of the digital signature of the transaction record, and perform a security level assessment on the encrypted record to generate an encrypted transaction data set.

[0018] As a further solution of the present invention, based on the encrypted transaction data set, energy transaction activities in the privacy-protected blockchain are captured in real time, transaction data is collected and synchronously updated to the ledger on the blockchain, and the steps of obtaining real-time monitoring data are specifically as follows:

[0019] S201: Based on the encrypted transaction data set, using the privacy-preserving blockchain network, capturing energy transaction activities in the network, including transaction initiation time, participant identification and transaction volume, initializing screening and classification of the data, and generating real-time transaction capture records;

[0020] S202: Execute data synchronization based on the real-time transaction capture record, update the transaction information to the public ledger of the blockchain in real time, check the timeliness of each transaction record, and generate a synchronization update record;

[0021] S203: Through the synchronous update record, the updated energy transaction data in the blockchain ledger is managed, the integrated data is monitored in real time, the regularity and abnormal patterns of transaction activities are detected, the compliance and security of the transaction are verified, and real-time monitoring data is generated.

[0022] As a further solution of the present invention, the real-time monitoring data is used to classify and mark energy transactions on the blockchain, identify abnormal transactions that do not conform to the rules, and mark risky transactions through abnormal pattern analysis. The steps of obtaining risky transaction marking results are specifically as follows:

[0023] S301: Using the real-time monitoring data, managing energy transactions recorded on the blockchain, classifying the data according to energy type, transaction scale and transaction frequency, and generating classified transaction data;

[0024] S302: Based on the classified transaction data, perform transaction pattern analysis, evaluate the behavior pattern of each type of transaction, identify energy transaction records that deviate from the normal pattern, including abnormal transaction frequency or scale, mark them as potential risk transactions, and generate abnormal transaction records;

[0025] S303: Based on the abnormal transaction records, the transaction records marked as risk are analyzed, the risk factors of energy transactions are identified, the non-compliance with existing transaction rules is identified, and the risk levels and participant information are sorted out to generate risk transaction marking results.

[0026] As a further solution of the present invention, according to the risk transaction marking result, the energy transaction verification rules are optimized, the difficulty and incentive mechanism of blockchain verification are adjusted, and risk transactions are avoided. The steps of obtaining the adjusted transaction verification rules are specifically as follows:

[0027] S401: Based on the risky transaction marking result, analyze the pattern and problem points of the risky transaction, identify the key factors of the transaction verification rules that need to be optimized, including transaction frequency limit and amount threshold, and generate rule requirement analysis results;

[0028] S402: Using the rule demand analysis results, design new energy transaction verification rules, adjust the transaction verification of the blockchain, optimize the sensitivity to risky transaction types and adjust the incentive mechanism to avoid the attractiveness of risky transactions, and generate a new draft of transaction verification rules;

[0029] S403: Adopt the new draft transaction verification rules, test the effectiveness and practicality of the new rules on the blockchain network, analyze the test results, verify that the new rules avoid the identified risky transactions, and generate adjusted transaction verification rules.

[0030] As a further solution of the present invention, based on the adjusted transaction verification rules, the energy transaction execution path is optimized, compliant transactions are processed preferentially, and the processing efficiency of energy transactions is optimized on the basis of protecting privacy. The steps of obtaining the transaction execution efficiency table are specifically as follows:

[0031] S501: Based on the adjusted transaction verification rules, a simulated annealing algorithm is used to optimize the transaction execution path by adjusting the data flow and processing sequence, optimizing the processing time and resource usage, and generating an optimized transaction execution path;

[0032] S502: According to the optimized transaction execution path, a priority processing mechanism is implemented to screen energy transactions, verify that compliant transactions are prioritized for verification and processing, and non-compliant transactions are delayed, optimize the transaction processing efficiency of the blockchain network through hierarchical processing, and generate transaction priority classification records;

[0033] S503: Using the transaction priority classification record, monitor and evaluate the real-time performance of energy transaction processing, record the transaction response time, success rate and implementation effect of processing priority, and generate a transaction execution efficiency table.

[0034] As a further solution of the present invention, the formula of the simulated annealing algorithm is as follows:

[0035]

[0036] Among them, P(e z ) is the acceptance probability of the new transaction execution path, e z For the efficiency and cost of real-time transaction execution path, e ′ z is the efficiency and cost of the new transaction execution path, T z is the real-time control parameter, α z is the weight coefficient, β z is the adjustment coefficient, γ z is the bias term.

[0037] As a further solution of the present invention, the steps of using the transaction execution efficiency table to identify energy transactions and usage, record the energy type, transaction frequency and transaction scale of the participants, analyze and optimize the energy transaction strategy, and generate energy usage results are specifically as follows:

[0038] S601: Based on the transaction execution efficiency table, a cluster analysis algorithm is used to perform a cluster analysis of energy type, transaction frequency, and transaction scale, evaluate the impact of energy transactions on the overall energy market, and generate energy transaction feature analysis results;

[0039] S602: Using the energy transaction characteristic analysis results, evaluate the energy transaction strategy, analyze the transaction area that needs to be optimized, and generate a strategy optimization plan by adjusting energy pricing and transaction conditions to match market demand;

[0040] S603: According to the strategy optimization plan, implement the new energy trading strategy, monitor the execution effect, record the response of transaction participants and the market acceptance, analyze the performance and effect of the new strategy in real-time transactions, verify the response speed and efficiency of the strategy, and generate energy usage results.

[0041] As a further solution of the present invention, the formula of the cluster analysis algorithm is as follows:

[0042]

[0043] Among them, S i is the sum of variance within the cluster, x is a data point, μ i is cluster C i The mean value of the data points, w i is the weight of the data point, λ is the regularization coefficient, ∥C i ∥ is the number of clusters.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, the security and efficiency of the energy trading process are optimized through improved data encryption and real-time monitoring logic. By digitally signing and encrypting each transaction data, the security of information is ensured. While providing data that cannot be tampered with, the transparency of the entire blockchain is also enhanced. The operation of capturing and updating blockchain ledgers in real time reduces the delay in information transmission, making the market more responsive, which is extremely important for the rapidly changing energy market. Classifying and risk-assessing transactions not only helps identify abnormal transactions, but also further enhances the ability to prevent potential fraudulent activities. Detailed supervision of trading activities promotes more efficient and fair market operations, enhances the overall health of the market and the confidence of participants. By optimizing transaction verification rules and execution paths, not only the processing efficiency of energy transactions is improved, but also energy distribution is optimized, making resource allocation more reasonable, and enhancing the competitiveness and sustainability of the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0047] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0048] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0049] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0050] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0051] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0055] See also Figure 1 The present invention provides a technical solution, a method for optimizing energy trading management based on a privacy-preserving blockchain, comprising the following steps:

[0056] S1: Collect data on energy trading participants and transaction records, record the transaction time, quantity and type of participants, encrypt transaction records with digital signatures, check the security of energy trading information, and generate encrypted transaction data sets;

[0057] S2: Based on the encrypted transaction data set, through the nodes of the privacy-protected blockchain network, the energy transaction activities in the network are captured in real time, the transaction data is collected and updated synchronously to the ledger on the blockchain to obtain real-time monitoring data;

[0058] S3: Use real-time monitoring data to classify and mark energy transactions on the blockchain, analyze the types and volumes of energy in transactions, identify abnormal transactions that do not conform to the rules, mark risky transactions through abnormal pattern analysis, and obtain risky transaction marking results;

[0059] S4: According to the risky transaction marking results, optimize the energy transaction verification rules, avoid risky transactions by adjusting the difficulty and incentive mechanism of blockchain verification, execute transactions that comply with the new rules, and obtain the adjusted transaction verification rules;

[0060] S5: Based on the adjusted transaction verification rules, optimize the energy transaction execution path, give priority to compliant transactions, check transaction execution and data synchronization, optimize the processing efficiency of energy transactions on the blockchain while protecting privacy, and obtain the transaction execution efficiency table;

[0061] S6: Use the transaction execution efficiency table to identify energy transactions and usage, record the energy type, transaction frequency and transaction size of the participants, analyze and optimize energy trading strategies, and generate energy usage results.

[0062] The encrypted transaction data set includes transaction timestamp, participant identification, transaction amount and energy type. The real-time monitoring data includes energy transaction time, identities of both parties to the transaction, transaction volume and transaction value. The risk transaction marking results include transaction ID, risk level, transaction participants and abnormal behavior type. The adjusted transaction verification rules include new verification thresholds, reward ratios and risk control parameters. The transaction execution efficiency table includes transaction response time, transaction success rate and priority processing transaction ratio. The energy usage results include total energy consumption, energy saving efficiency and optimized energy allocation strategy.

[0063] See also Figure 2 , collect data on energy transaction participants and transaction records, encrypt transaction records using digital signatures, and verify the security of energy transaction information. The specific steps for generating encrypted transaction data sets are:

[0064] S101: Collect data on energy trading participants and transaction records, including energy types, transaction volumes, transaction prices, and transaction times, classify and organize the information, and obtain the execution process of the original transaction data set as follows;

[0065] Collecting data on energy trading participants and transaction records, including energy types, transaction volumes, transaction prices, and transaction times, is the first step in establishing a foundation for effective data processing and analysis. In order to ensure the accuracy and availability of data, detailed data classification and organization must be carried out to convert raw data into a data set with a unified format and easy to analyze. This includes not only data collection, but also preliminary data cleaning, such as removing invalid or duplicate records and verifying data integrity and consistency. Classification and organization of data also involves labeling data according to transaction type, energy type, and transaction time, so as to facilitate subsequent data query and in-depth analysis to obtain the original transaction data set.

[0066] S102: Using the original transaction data set, encrypt the transaction data, apply a digital signature to each record, check the security of the transaction data, and verify the encrypted data. The execution process of generating an encrypted transaction record is as follows;

[0067] The original transaction data set is used for secure encryption processing. In the current digital age, data security is extremely important, especially when it comes to financial transactions. Advanced encryption technology is used to apply digital signatures to each record to ensure the security and integrity of the data during transmission or storage. The application of digital signatures not only enhances the security of data, but also provides a means to verify that the data has not been tampered with. The encryption process of each record includes the use of public key encryption technology and hash algorithms to ensure that even if the data is illegally accessed, the information cannot be interpreted or tampered with, and the encrypted transaction record is generated using the formula:

[0068]

[0069] Among them, C b Indicates encrypted transaction records, S b is a digital signature, D b is the data to be encrypted, k is the security strength parameter, hash is the hash function, and ⊕ represents the bit operation of data and signature.

[0070] S103: By encrypting the transaction record, analyzing the integrity of the transaction record, verifying the consistency of the digital signature of the transaction record, and performing a security level assessment on the encrypted record, the execution process of generating an encrypted transaction data set is as follows;

[0071] By encrypting transaction records, verifying the integrity and consistency of transaction records, and verifying digital signatures is a key step to ensure that the data has not been tampered with since encryption. Evaluate the security level of encrypted records to ensure that all data meets the predetermined security standards. This includes checking the strength of the encryption algorithm and whether there are potential security vulnerabilities. Through detailed analysis, it can ensure that each record meets the highest data protection standards, providing a solid security foundation for subsequent data use, and generating encrypted transaction data sets. The formula used is:

[0072]

[0073] Among them, E c represents the encrypted transaction dataset, S c,a is the ath signature in the digital signature set, C c,a is the ath data in the encrypted data set, n c is the total number of data items.

[0074] See also Figure 3 , based on the encrypted transaction data set, real-time capture of energy transaction activities in the privacy-protected blockchain, collection of transaction data and synchronous update to the ledger on the blockchain, the steps to obtain real-time monitoring data are as follows:

[0075] S201: Based on the encrypted transaction data set, the privacy-preserving blockchain network is used to capture energy transaction activities in the network, including transaction initiation time, participant identification, and transaction volume, and the data is initially screened and classified to generate real-time transaction capture records. The execution process is as follows;

[0076] Based on the encrypted transaction data set, the privacy-preserving blockchain network is used to capture energy transaction activities in the encrypted transaction data set in real time. This includes recording the initiation time, participant identification, and transaction volume of each transaction. By initializing the screening and classification of the data, it can be ensured that only relevant and valid data is recorded in the blockchain. This not only improves data processing efficiency, but also enhances data security and privacy, because all data processed through the blockchain are strictly encrypted and verified to generate real-time transaction capture records. The formula used is:

[0077]

[0078] Among them, R d represents real-time transaction capture records, T d,i ,I d,i and Q d,i Represent the initiation time, participant identification and transaction volume of the bth transaction, α i is the weight coefficient, n d is the total number of transactions.

[0079] S202: According to the real-time transaction capture record, data synchronization is performed, the transaction information is updated in real time to the public ledger of the blockchain, the timeliness of each transaction record is checked, and the execution process of generating the synchronization update record is as follows;

[0080] According to the real-time transaction capture records, the data is updated synchronously, and each captured transaction information is updated in real time to the public ledger of the blockchain. The process includes verifying the timestamp of each record to ensure timeliness. All network participants can access the latest transaction data, while ensuring the transparency and immutability of the data, and generating synchronous update records.

[0081] S203: By synchronously updating records, managing the updated energy transaction data in the blockchain ledger, real-time monitoring of the integrated data, detecting the regularity and abnormal patterns of transaction activities, verifying the compliance and security of transactions, and generating the execution process of real-time monitoring data is as follows;

[0082] By synchronously updating records, managing and monitoring the updated energy transaction data in the blockchain ledger, the integrated data is monitored in real time, including detecting the regularity and abnormal patterns of transaction activities, and verifying the compliance and security of transactions. By monitoring activities, it is possible to ensure that the transaction process complies with all regulatory requirements, and to promptly detect and handle abnormal or potential security issues, and generate real-time monitoring data, using the formula:

[0083]

[0084] Among them, V g Indicates real-time monitoring data, M g,jrepresents the abnormal pattern score of the bth transaction, and J is the set of all monitored transactions.

[0085] See also Figure 4 , using real-time monitoring data, classify and mark energy transactions on the blockchain, identify abnormal transactions that do not conform to the rules, and mark risky transactions through abnormal pattern analysis. The specific steps for obtaining risky transaction marking results are:

[0086] S301: Use real-time monitoring data to manage energy transactions recorded on the blockchain, classify data according to energy type, transaction scale and transaction frequency, and generate classified transaction data. The execution process is as follows;

[0087] Using real-time monitoring data to manage energy transactions recorded on the blockchain, by classifying transaction data, different types of energy transaction dynamics can be analyzed and understood more effectively. Classification is based on energy type, transaction size, and transaction frequency. It allows data to be organized into a structure that is easy to manage and analyze, improving the efficiency and quality of data processing. Through meticulous classification, it can lay the foundation for further in-depth analysis and potential risk assessment, and generate classified transaction data. The formula used is:

[0088]

[0089] Among them, C h Represents classified transaction data, E h,i 、V h,i and F h,i represent the energy type, transaction size and transaction frequency of the transaction in clause b, respectively, and w h,i is the weight coefficient determined according to the transaction type, n d is the total number of transactions.

[0090] S302: Based on the classified transaction data, perform transaction pattern analysis, evaluate the behavior pattern of each type of transaction, identify energy transaction records that deviate from the normal pattern, including abnormal transaction frequency or scale, and mark them as potential risk transactions. The execution process of generating abnormal transaction records is as follows;

[0091] Based on the classified transaction data, transaction pattern analysis is performed. By evaluating the behavioral patterns of each type of transaction, energy transaction records that deviate from the normal pattern can be identified, especially those that show abnormal transaction frequency or scale. Abnormal records indicate potential risks and are therefore marked as risky transactions. It is essential to prevent fraud and manage risks. The abnormal transaction records are generated using the formula:

[0092]

[0093] Among them, A k Indicates abnormal transaction records, V k,jand F k,j They represent the size and frequency of the b-th transaction respectively, and J is the set of all monitored transactions.

[0094] S303: Based on the abnormal transaction records, the transaction records marked as risk are analyzed, the risk factors of energy transactions are identified, the non-compliance with the existing transaction rules is identified, and the risk level and participant information are sorted out to generate the risk transaction marking results. The execution process is as follows;

[0095] Based on abnormal transaction records, in-depth analysis is conducted to identify key risk factors in risky transaction records, including situations that are inconsistent with existing transaction rules. Information on participants involved in risky transactions and corresponding risk levels will also be sorted out to generate risky transaction marking results. The formula used is:

[0096]

[0097] Among them, B l represents the risk transaction marking result, exp is the indicator function, N l , P l and R l Respectively represent the situation that does not comply with the transaction rules, the participant information and the risk level, dR l Represents a small risk assessment interval.

[0098] See also Figure 5 ,According to the risk transaction marking results, the energy transaction verification rules are optimized, the difficulty and incentive mechanism of blockchain verification are adjusted, and risk transactions are avoided. The specific steps for obtaining the adjusted transaction verification rules are as follows:

[0099] S401: Based on the risky transaction marking results, the risky transaction patterns and problem points are analyzed, and the key factors of the transaction verification rules that need to be optimized are identified, including transaction frequency limits and amount thresholds. The execution process of generating rule requirement analysis results is as follows;

[0100] Based on the risky transaction marking results, the risky transaction patterns and problem points are deeply analyzed to identify the key factors that affect transaction security, especially the transaction frequency limit and amount threshold. By analyzing the factors, the shortcomings of the current verification rules can be clarified and necessary adjustments and optimization suggestions can be made. This helps ensure that the rule adjustments can effectively respond to potential transaction risks and enhance overall transaction security, and generate rule demand analysis results.

[0101] S402: Using the results of the rule demand analysis, design new energy transaction verification rules, adjust the transaction verification of the blockchain, optimize the sensitivity to risky transaction types and adjust the incentive mechanism to avoid the attractiveness of risky transactions. The execution process of generating a new draft of transaction verification rules is as follows;

[0102] Using the results of the rule demand analysis, new energy transaction verification rules are designed. In the process, the transaction verification mechanism of the blockchain is adjusted to optimize the sensitivity to risky transaction types and adjust the incentive mechanism to reduce the attractiveness of risky transactions. The newly designed transaction verification rules take into account multiple risk factors and transaction characteristics to ensure that non-compliance and potential fraud can be effectively avoided, and a new draft of transaction verification rules is generated.

[0103] S403: adopt the new draft transaction verification rules, test the effectiveness and practicality of the new rules on the blockchain network, analyze the test results, verify that the new rules avoid the identified risky transactions, and generate the execution process of the adjusted transaction verification rules as follows;

[0104] Adopt a new draft of transaction verification rules, implement and test the newly designed draft of transaction verification rules on the blockchain network, test the effectiveness and practicality of the new rules through actual network operations, ensure that they can operate stably in different trading environments, and effectively avoid identified risky transactions, and generate adjusted transaction verification rules.

[0105] See also Figure 6 Based on the adjusted transaction verification rules, the energy transaction execution path is optimized, compliant transactions are given priority, and the processing efficiency of energy transactions is optimized on the basis of protecting privacy. The specific steps for obtaining the transaction execution efficiency table are as follows:

[0106] S501: Based on the adjusted transaction verification rules, the simulated annealing algorithm is used to optimize the configuration of the transaction execution path by adjusting the data flow and processing order, optimize the processing time and resource usage, and generate the execution process of the optimized transaction execution path as follows;

[0107] Based on the adjusted transaction verification rules, the transaction execution path is optimized by dynamically adjusting the data flow and processing order. The simulated annealing algorithm is a heuristic optimization algorithm that simulates the metal annealing process to find the global optimal solution. It is particularly suitable for solving large-scale and complex optimization problems. In the blockchain network, it means that the processing time and resource usage can be optimized, the overall load of the blockchain can be effectively reduced, the speed and efficiency of transaction processing can be improved, the best processing path and order for each transaction can be determined, and an optimized transaction execution path can be generated.

[0108] The formula for the simulated annealing algorithm is as follows:

[0109]

[0110] Among them, P(e z ) is the acceptance probability of the new transaction execution path, e z For the efficiency and cost of real-time transaction execution path, e ′z is the efficiency and cost of the new transaction execution path, T z is the real-time control parameter, α z is the weight coefficient, β z is the adjustment coefficient, γ z is the bias term.

[0111] The execution flow is as follows:

[0112] Calculate the energy difference e between the current solution and the new solution z ′ -e z , multiply the energy difference by the adjustment coefficient β z , plus the bias term γ z , divide the result by the current temperature T z , the value calculated by applying the exponential function, multiplied by the weight coefficient α z To obtain the acceptance probability, the process considers additional adjustment factors to more accurately control the acceptance probability of the new solution, the weight coefficient α z The establishment of can be determined through historical data analysis to ensure the consistency of the algorithm in different application scenarios, and adjust the coefficient β z and the bias term γ z It can also be obtained through experimental tuning to match specific dynamics and transaction characteristics.

[0113] S502: According to the optimized transaction execution path, a priority processing mechanism is implemented to screen energy transactions, verify that compliant transactions are prioritized for verification and processing, and non-compliant transactions are delayed. The transaction processing efficiency of the blockchain network is optimized through layered processing, and the execution process of generating transaction priority classification records is as follows;

[0114] According to the optimized transaction execution path, a priority processing mechanism is implemented to screen energy transactions, ensuring that compliant transactions are verified and processed first, while non-compliant transactions are appropriately delayed. Through the hierarchical processing mechanism, the transaction processing efficiency of the blockchain network can be optimized, unnecessary resource waste can be reduced, and the response speed to compliant and high-priority transactions can be improved. It not only enhances the overall efficiency of the network, but also improves the management ability of risky transactions, generates transaction priority classification records, and adopts the formula:

[0115]

[0116] Among them, Q q Indicates the priority classification record of the transaction, α q and β q is the adjustment factor, x i Indicates the compliance score of the transaction.

[0117] S503: Using transaction priority classification records, monitoring and evaluating the real-time performance of energy transaction processing, recording the transaction response time, success rate and implementation effect of processing priority, and generating the transaction execution efficiency table. The execution process is as follows;

[0118] Use transaction priority classification records to monitor and evaluate the real-time performance of energy transaction processing, focusing on indicators such as transaction response time, success rate, and the implementation effect of processing priorities. Through data, we can understand in detail the performance of the priority processing mechanism in actual operation and ensure that it can effectively operate according to the predetermined rules. Help further adjust and optimize transaction processing strategies to ensure efficient and fair energy transactions, and generate a transaction execution efficiency table using the formula:

[0119]

[0120] Among them, R r Represents the transaction execution efficiency table, res r,j is the response time of the bth transaction, pri r,j is the corresponding priority, n d is the transaction quantity.

[0121] See also Figure 7 , using the transaction execution efficiency table, identifying energy transactions and usage, recording the energy type, transaction frequency and transaction size of the participants, analyzing and optimizing the energy transaction strategy, and generating energy usage results in the following steps:

[0122] S601: Based on the transaction execution efficiency table, a cluster analysis algorithm is used to perform cluster analysis on energy type, transaction frequency and transaction scale, evaluate the impact of energy transactions on the overall energy market, and generate energy transaction feature analysis results. The execution process is as follows;

[0123] Based on the transaction execution efficiency table, cluster analysis of energy type, transaction frequency and transaction scale is carried out. Cluster analysis can help identify patterns and trends in energy transactions, evaluate the impact of energy transactions of different types and scales on the overall energy market, reveal the relationship between market dynamics and transaction behavior, provide a scientific basis for energy market regulation and strategy formulation, and generate energy transaction characteristic analysis results.

[0124] The formula for the cluster analysis algorithm is as follows:

[0125]

[0126] Among them, S i is the sum of variance within the cluster, x is a data point, μ i is cluster C i The mean value of the data points, w i is the weight of the data point, λ is the regularization coefficient, ∥Ci ∥ is the number of clusters.

[0127] The execution flow is as follows:

[0128] Assign each data point a weight w determined by business importance (e.g., transaction size) i To ensure that important transactions have a greater influence in the analysis, the sum of the squares of the Euclidean distances from the weighted data points in each cluster to the cluster center is calculated. At the same time, the regularization term λ·∥C is introduced i ∥ is used to control the number of clusters and avoid the generation of too many small clusters. By adjusting the value of λ, the quantity and quality of clusters can be balanced to ensure that meaningful clustering results are obtained in terms of business logic. The weight coefficient w i It can be determined through historical statistical analysis of transaction data, and the regularization coefficient λ can be optimized through cross-validation method to ensure the best clustering effect.

[0129] S602: Using the energy transaction characteristic analysis results, evaluate the energy transaction strategy, analyze the transaction area that needs to be optimized, and generate the strategy optimization plan by adjusting energy pricing and transaction conditions to match market demand. The execution process is as follows;

[0130] By using the results of energy trading characteristics analysis to evaluate the current energy trading strategy, we can identify trading areas in the market with unbalanced demand or insufficient response. We analyze some areas and find out that they need to adjust energy pricing and trading conditions to better match market demand. Strategy optimization can not only improve market efficiency, but also enhance consumer satisfaction and the overall stability of the market, and generate strategy optimization solutions.

[0131] S603: According to the strategy optimization plan, implement the new energy trading strategy, monitor the execution effect, record the response of transaction participants and the market acceptance, analyze the performance and effect of the new strategy in real-time transactions, verify the response speed and efficiency of the strategy, and generate the execution process of energy use results as follows;

[0132] According to the strategy optimization plan, the new energy trading strategy is implemented and monitored in real time, including the response of trading participants and the market acceptance. Monitoring helps analyze the performance of the new strategy in actual trading, including the response speed and efficiency of the strategy. Through data, it can be verified whether the new strategy has achieved the expected market effect and whether further adjustment or optimization is needed to generate energy usage results. The formula used is:

[0133]

[0134] Among them, T t Represents the energy usage result, R t,j and A t,jRespectively represent the response and market acceptance of the bth transaction, n d is the total number of transactions.

[0135] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing energy trading based on privacy-preserving blockchain, characterized in that: The following steps are involved: Collect data on energy trading participants and transaction records, encrypt transaction records using digital signatures, verify the security of energy trading information, and generate encrypted transaction data sets; Based on the encrypted transaction data set, energy transaction activities in the privacy-protected blockchain are captured in real time, transaction data is collected and synchronously updated to the ledger on the blockchain, and real-time monitoring data is obtained; Using the real-time monitoring data, classify and mark energy transactions on the blockchain, identify abnormal transactions that do not conform to the rules, mark risky transactions through abnormal pattern analysis, and obtain risky transaction marking results; According to the risky transaction marking results, the energy transaction verification rules are optimized, the difficulty and incentive mechanism of blockchain verification are adjusted, risky transactions are avoided, and the adjusted transaction verification rules are obtained; Based on the adjusted transaction verification rules, the energy transaction execution path is optimized, compliant transactions are processed preferentially, the processing efficiency of energy transactions is optimized on the basis of protecting privacy, and a transaction execution efficiency table is obtained; The transaction execution efficiency table is used to identify energy transactions and usage, record the energy type, transaction frequency and transaction size of the participants, analyze and optimize energy transaction strategies, and generate energy usage results.

2. The energy transaction optimization management method based on privacy protection blockchain according to claim 1 is characterized in that: The encrypted transaction data set includes transaction timestamp, participant identification, transaction amount and energy type; the real-time monitoring data includes energy transaction time, identities of both parties to the transaction, transaction volume and transaction value; the risk transaction marking results include transaction ID, risk level, transaction participants and abnormal behavior type; the adjusted transaction verification rules include new verification thresholds, reward ratios and risk control parameters; the transaction execution efficiency table includes transaction response time, transaction success rate and priority processing transaction ratio; the energy usage results include total energy consumption, energy saving efficiency and optimized energy allocation strategy.

3. The energy transaction optimization management method based on privacy protection blockchain according to claim 1 is characterized in that: Collect data on energy trading participants and transaction records, encrypt transaction records using digital signatures, and verify the security of energy trading information. The specific steps for generating encrypted transaction data sets are as follows: Collect data on energy trading participants and transaction records, including energy types, transaction volumes, transaction prices, and transaction times, and classify and organize the information to obtain the original transaction data set; Using the original transaction data set, encrypting the transaction data, applying a digital signature to each record, checking the security of the transaction data, and verifying the encrypted data to generate an encrypted transaction record; Through the encrypted transaction records, the integrity of the transaction records is analyzed, the consistency of the digital signature of the transaction records is verified, and the security level of the encrypted records is evaluated to generate an encrypted transaction data set.

4. The energy transaction optimization management method based on privacy protection blockchain according to claim 1 is characterized in that: Based on the encrypted transaction data set, energy transaction activities in the privacy-protected blockchain are captured in real time, transaction data is collected and synchronously updated to the ledger on the blockchain, and the steps for obtaining real-time monitoring data are as follows: Based on the encrypted transaction data set, using the privacy-preserving blockchain network, energy transaction activities in the network are captured, including transaction initiation time, participant identification and transaction volume, and the data is initially screened and classified to generate real-time transaction capture records; According to the real-time transaction capture record, data synchronization is performed to update the transaction information to the public ledger of the blockchain in real time, check the timeliness of each transaction record, and generate a synchronization update record; Through the synchronous update records, the updated energy transaction data in the blockchain ledger is managed, the integrated data is monitored in real time, the regularity and abnormal patterns of transaction activities are detected, the compliance and security of transactions are verified, and real-time monitoring data is generated.

5. The energy transaction optimization management method based on privacy protection blockchain according to claim 1 is characterized in that: The real-time monitoring data is used to classify and mark energy transactions on the blockchain, identify abnormal transactions that do not conform to the rules, and mark risky transactions through abnormal pattern analysis. The specific steps for obtaining risky transaction marking results are as follows: Using the real-time monitoring data, managing energy transactions recorded on the blockchain, classifying the data according to energy type, transaction scale and transaction frequency, and generating classified transaction data; Based on the classified transaction data, perform transaction pattern analysis, evaluate the behavior pattern of each type of transaction, identify energy transaction records that deviate from the normal pattern, including abnormal transaction frequency or scale, and mark them as potential risk transactions, and generate abnormal transaction records; Based on the abnormal transaction records, the transaction records marked as risk are analyzed, the risk factors of energy transactions are identified, the non-compliance with existing transaction rules is identified, and the risk levels and participant information are sorted out to generate risk transaction marking results.

6. The energy transaction optimization management method based on privacy protection blockchain according to claim 1 is characterized in that: According to the risky transaction marking results, the energy transaction verification rules are optimized, the difficulty and incentive mechanism of blockchain verification are adjusted, and risky transactions are avoided. The specific steps for obtaining the adjusted transaction verification rules are as follows: Based on the risky transaction marking results, analyze the patterns and problem points of risky transactions, identify key factors of transaction verification rules that need to be optimized, including transaction frequency limits and amount thresholds, and generate rule demand analysis results; Using the results of the rule demand analysis, design new energy transaction verification rules, adjust the transaction verification of the blockchain, optimize the sensitivity to risky transaction types and adjust the incentive mechanism to avoid the attractiveness of risky transactions, and generate a new draft of transaction verification rules; The new draft transaction verification rules are adopted to test the effectiveness and practicality of the new rules on the blockchain network, and the test results are analyzed to verify that the new rules avoid the identified risky transactions and generate adjusted transaction verification rules.

7. The energy transaction optimization management method based on privacy protection blockchain according to claim 1 is characterized in that: Based on the adjusted transaction verification rules, the energy transaction execution path is optimized, compliant transactions are processed preferentially, and the processing efficiency of energy transactions is optimized on the basis of protecting privacy. The specific steps for obtaining the transaction execution efficiency table are as follows: Based on the adjusted transaction verification rules, a simulated annealing algorithm is used to optimize the configuration of the transaction execution path by adjusting the data flow and processing sequence, optimize the processing time and resource usage, and generate an optimized transaction execution path; According to the optimized transaction execution path, a priority processing mechanism is implemented to screen energy transactions, verify that compliant transactions are prioritized for verification and processing, and non-compliant transactions are delayed. The transaction processing efficiency of the blockchain network is optimized through layered processing, and transaction priority classification records are generated; The transaction priority classification record is adopted to monitor and evaluate the real-time performance of energy transaction processing, record the transaction response time, success rate and implementation effect of processing priority, and generate a transaction execution efficiency table.

8. The energy transaction optimization management method based on privacy protection blockchain according to claim 7 is characterized in that: The formula of the simulated annealing algorithm is as follows: Among them, P(e z ) is the acceptance probability of the new transaction execution path, e z For the efficiency and cost of real-time transaction execution path, e ′ z is the efficiency and cost of the new transaction execution path, T z is the real-time control parameter, α z is the weight coefficient, β z is the adjustment coefficient, γ z is the bias term.

9. The energy transaction optimization management method based on privacy protection blockchain according to claim 1 is characterized in that: The steps of using the transaction execution efficiency table to identify energy transactions and usage, record the energy type, transaction frequency and transaction size of the participants, analyze and optimize the energy transaction strategy, and generate energy usage results are as follows: Based on the transaction execution efficiency table, a cluster analysis algorithm is used to perform a cluster analysis of energy types, transaction frequencies, and transaction sizes, evaluate the impact of energy transactions on the overall energy market, and generate energy transaction feature analysis results; Using the energy trading characteristics analysis results, evaluate the energy trading strategy, analyze the trading areas that need to be optimized, and generate a strategy optimization plan by adjusting energy pricing and trading conditions to match market demand; According to the strategy optimization plan, implement the new energy trading strategy, monitor the execution effect, record the response of trading participants and the market acceptance, analyze the performance and effect of the new strategy in real-time trading, verify the response speed and efficiency of the strategy, and generate energy usage results.

10. The energy transaction optimization management method based on privacy protection blockchain according to claim 9 is characterized in that: The formula of the cluster analysis algorithm is as follows: Among them, S i is the sum of variance within the cluster, x is a data point, μ i is cluster C i The mean value of the data points, w i is the weight of the data point, λ is the regularization coefficient, ∥C i ∥ is the number of clusters.

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