Blockchain-based new energy transaction security assessment method

Abstract

This invention discloses a blockchain-based security assessment method for new energy transactions, involving the interdisciplinary fields of new energy and blockchain. The method includes multi-source data fusion of the transaction data set to be assessed to generate a structured transaction data graph. After the graph is written into the blockchain network to form timestamped transaction data blocks, a smart contract is invoked to perform on-chain automated assessment, generating a preliminary security assessment result. Based on this result, a dynamic model is constructed to simulate and extrapolate risks, generating quantitative indicators of security risks and transmission paths. The preliminary result is then weighted and corrected to output the final security level. This method achieves cross-domain data association and integration of power flow, capital flow, and information flow in transactions, and improves the assessment dimensions and the ability to proactively warn of potential cascading risks by combining static rule verification with dynamic risk extrapolation.

Description

Technical Field

[0001] This invention belongs to the field of interdisciplinary technology of new energy and blockchain, specifically a blockchain-based method for assessing the security of new energy transactions. Background Technology

[0002] In the field of new energy power trading, transaction security assessment relies on multi-dimensional analysis of market operation data. Existing technologies typically collect and manage power grid physical operation data, financial market settlement data, and trading platform information data separately. Common blockchain applications focus on the notarization and traceability of transaction records in the flow of funds or contract information, forming multiple independent data verification chains. This separate processing model leads to the fragmentation of the real-time correlation between power delivery, fund clearing, and information exchange, making it impossible to effectively construct a unified data view reflecting the entire transaction. When anomalies occur, it is difficult to cross-compare and collaboratively analyze data between different systems, making it difficult to detect some hidden cross-domain risks.

[0003] Current security assessment methods primarily rely on static comparisons between pre-defined rules and historical models. While automated verification based on blockchain smart contracts can perform basic checks such as format compliance and signature validity, its judgment logic is rigid and only applicable to clearly defined, discrete violations. This approach lacks the ability to dynamically extrapolate the cascading effects and systemic risks arising from complex transactions. It cannot simulate how initially compliant transactions evolve and potentially trigger grid security or market stability issues when market conditions, network states, or the behavior of participating entities change; thus, the assessment conclusions lag behind the dynamic evolution of risks.

[0004] A technical solution is needed that can integrate multi-source heterogeneous transaction data to form a unified relational view, and on this basis, extend from static rule verification to dynamic risk prediction. This solution needs to address the problems of data dimensional fragmentation and static assessment, enabling real-time, forward-looking quantitative assessment of cross-domain relational security risks in new energy transactions. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art; Therefore, this invention proposes a blockchain-based security assessment method for new energy transactions, including: Obtain the set of transaction data to be evaluated in the new energy trading market; The set of transaction data to be evaluated is subjected to multi-source data fusion processing to generate a structured transaction data map, which includes power flow mapping relationship, capital flow mapping relationship and information flow mapping relationship; The structured transaction data graph is written into the distributed ledger of the blockchain network to generate transaction data blocks with timestamp sequences. The system invokes a security assessment rule set based on smart contracts to perform on-chain automated assessment of the transaction data block, generating a preliminary security assessment result. The preliminary security assessment result includes data consistency verification result, transaction compliance verification result, and physical information consistency verification result. Based on the preliminary security assessment results, a dynamic security assessment model is constructed to simulate and extrapolate potential risks to the set of transaction data to be assessed, and to generate quantitative indicators of security risks and risk transmission paths. By combining the aforementioned quantitative indicators of safety risks and risk transmission paths, the preliminary safety assessment results are weighted and corrected to generate the final safety level of new energy transactions.

[0006] Furthermore, the step of performing multi-source data fusion processing on the set of transaction data to be evaluated to generate a structured transaction data map includes: The dataset of transactions to be evaluated includes power generation output data, grid dispatch data, energy consumption load data, and market transaction contract data. Extract the real-time output curves and predicted output data of the new energy power generation equipment from the power generation side output data, and establish the power generation equipment output spectrum; Extract the real-time operating status of the power grid and the sequence of dispatch instructions from the power grid-side dispatch data to establish a power grid dispatch map; Adjustable load curves and energy consumption behavior characteristics are extracted from the energy consumption-side load data to establish an energy consumption load map. Extract transaction prices, transaction volumes, and transaction entity information from the market transaction contract data to establish a market transaction map; The power generation output map, power grid dispatch map, energy load map, and market transaction map are spatiotemporally aligned and correlated to generate a structured transaction data map containing multiple correlations of power, funds, and information.

[0007] Furthermore, the step of writing the structured transaction data graph into the distributed ledger of the blockchain network to generate transaction data blocks with timestamp sequences includes: The structured transaction data map is divided according to preset data partitioning rules to generate multiple data slices; Perform a hash operation on each data slice to generate the corresponding data slice hash value; The data slice hash values ​​are arranged in chronological order to construct a Merkle tree structure, generating the Merkle tree root hash. The Merkle root hash, timestamp information, and hash value of the previous block are packaged together to generate a new transaction data block; The new transaction data blocks are verified and stored on the blockchain through the consensus mechanism of the blockchain network, forming an immutable distributed ledger record.

[0008] Furthermore, the invocation of a security assessment rule set based on smart contracts to perform on-chain automated assessment of the transaction data block generates preliminary security assessment results, including: Read the transaction data block and its contained transaction data graph from the distributed ledger of the blockchain network; The power flow mapping relationship in the transaction data map is input into the physical constraint verification smart contract to perform the matching verification of the power generation plan and the power flow constraint of the grid, and generate the physical consistency verification result. The fund flow mapping relationship in the transaction data graph is input into the market rule verification smart contract to perform the compliance verification of the transaction price and the market clearing rules, and generate the transaction compliance verification result; The information flow mapping relationship in the transaction data graph is input into the information integrity verification smart contract to perform the verification of the identity of the transaction subject and the integrity of the transaction data, and generate the information integrity verification result; The preliminary security assessment results are generated by summarizing the physical consistency verification results, transaction compliance verification results, and information integrity verification results.

[0009] Furthermore, the step of constructing a dynamic security assessment model based on the preliminary security assessment results, simulating and extrapolating potential risks to the set of transaction data to be assessed, and generating quantitative security risk indicators and risk transmission paths includes: Analyze the abnormal verification items in the preliminary security assessment results to identify potential risk trigger points; Using the potential risk trigger points as initial disturbances, a risk transmission network is constructed based on the mapping relationships of power flow, capital flow, and information flow in the transaction data graph; In the aforementioned risk transmission network, a disturbance scenario is simulated to deduce the diffusion process of risk along the paths of power flow, capital flow, and information flow; Quantify the scope, intensity, and duration of impact during the risk diffusion process to generate quantitative indicators of safety risks; Record the key transmission nodes and paths in the risk diffusion process, and generate the risk transmission path.

[0010] Furthermore, the step of constructing a risk transmission network based on the potential risk trigger point as the initial disturbance and according to the mapping relationship of power flow, capital flow, and information flow in the transaction data graph includes: Extract the connection relationships between power generation equipment, power grid nodes, energy users, and trading entities in the transaction data map; Transform the physical connections in the power flow mapping relationship into power risk edges in the risk transmission network; Transform the fund transfers in the fund flow mapping relationship into the fund risk edge in the risk transmission network; Transform the information interaction in the information flow mapping relationship into the information risk edge in the risk transmission network; Using the entities corresponding to the potential risk trigger points as starting nodes, and connecting them with power risk edges, capital risk edges, and information risk edges, a risk transmission network containing multiple types of risk transmission paths is constructed.

[0011] Furthermore, the preliminary security assessment results are weighted and corrected by combining the aforementioned security risk quantification indicators and risk transmission paths to generate the final new energy transaction security level, including: Based on the scope and intensity of influence in the aforementioned safety risk quantification indicators, calculate the risk weight for each risk transmission path; The risk weights are weighted and fused with the corresponding verification results in the preliminary security assessment results to generate a corrected verification score. Based on the topology of the risk transmission path, calculate the risk aggregation coefficient and propagation efficiency in the network; Based on the aggregation coefficient and propagation efficiency, the corrected verification score is adjusted a second time to generate a comprehensive security score. The comprehensive security score is mapped to a preset security level range to determine the final new energy transaction security level.

[0012] Further, the step of weighting and fusing the risk weights with the corresponding verification results in the preliminary security assessment to generate a corrected verification score includes: The physical consistency verification results, transaction compliance verification results, and information integrity verification results in the preliminary security assessment results are quantified into numerical scores. The scope of influence in the aforementioned safety risk quantification indicators is quantified as the breadth of influence coefficient, and the intensity of influence is quantified as the depth of influence coefficient. Based on the risk transmission path, determine the impact breadth coefficient and impact depth coefficient associated with each verification result; A weighting function is constructed using the influence breadth coefficient and influence depth coefficient to perform a weighted calculation on the numerical score, generating a weighted individual verification score. The weighted individual verification scores are summed and normalized to generate the corrected verification score.

[0013] Furthermore, the method also includes the step of continuously and dynamically updating the final new energy transaction security level: Periodically acquire new transaction data from the new energy trading market to form an incremental transaction data set; Perform the multi-source data fusion processing on the incremental transaction data set to generate an incremental transaction data map; The incremental transaction data map is written into the blockchain network to form a new transaction data block; The newly added transaction data block is automatically evaluated on-chain by invoking the security assessment rule set based on smart contracts, and an incremental preliminary security assessment result is generated. Based on the historical assessment results and the incremental preliminary security assessment results, the parameters of the dynamic security assessment model are updated, and potential risks are simulated and deduced for the newly added transaction data set, thereby updating the security risk quantification indicators and risk transmission paths. The final new energy transaction security level is adjusted in real time based on the updated security risk quantification indicators and risk transmission paths.

[0014] Furthermore, updating the parameters of the dynamic security assessment model by combining historical assessment results and the incremental preliminary security assessment results includes: Obtain historical transaction data blocks and their corresponding historical security assessment results from the distributed ledger of the blockchain network; The historical security assessment results are compared and analyzed with actual historical risk events to calculate the accuracy rate of the historical assessment. Based on the historical assessment accuracy, adjust the confidence parameters of risk transmission inference in the dynamic security assessment model; The incremental preliminary security assessment results are input into the dynamic security assessment model after adjusting the confidence parameters, and the edge weights and node influence factors of the risk transmission network are retrained. Based on the retrained dynamic security assessment model, updated model parameters are generated, thereby completing the update of the dynamic security assessment model.

[0015] Compared with the prior art, the beneficial effects of the present invention are: The structured transaction data map generated by multi-source data fusion links and integrates power flow, capital flow, and information flow data that were originally scattered across different systems, constructing a unified and traceable data relationship network. This technical solution establishes a clear mapping relationship between each power delivery, each capital settlement, and each communication record. The evaluation process can be conducted on a complete and interconnected data foundation, rather than relying on isolated data fragments for biased judgment. This solution can directly reveal inconsistencies, asynchronies, or logical conflicts between cross-system data, thereby identifying systemic anomalies or hidden violations that are difficult to detect under traditional separate auditing models.

[0016] The combination of on-chain automated assessment and dynamic risk simulation extends security assessment from immediate judgment based on fixed rules to predictive analysis incorporating dynamic simulation. Rule set execution based on smart contracts completes standardized and automated basic verification of transaction data blocks. The dynamic security assessment model utilizes preliminary verification results and a complete correlation data graph to simulate the evolution of risks under various scenarios, such as market parameter fluctuations, abnormal node behavior, or changes in network state. This technical solution can calculate quantitative indicators representing the degree of risk and depict the possible transmission paths of risk between different transaction entities and system components. This ensures that the assessment output not only includes a judgment of the current state but also provides predictive information about risk development trends and the potential scope of impact, offering forward-looking decision-making references for risk management. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the steps of the blockchain-based new energy transaction security assessment method described in this invention. Figure 2 A flowchart for generating transaction data blocks; Figure 3 A bar chart comparing the three types of risk quantification indicators; Figure 4 A graph showing the relationship between the size of different blocks and verification time on the blockchain; Figure 5 The bar chart is dynamically updated to reflect the edge weights of the risk transmission network. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1The overall implementation scheme of the blockchain-based new energy transaction security assessment method is as follows: A set of transaction data to be assessed in the new energy trading market is obtained. Multi-source data fusion processing is performed on this set to form a structured transaction data graph. This graph integrates power flow mapping, capital flow mapping, and information flow mapping. This structured transaction data graph is then written into the distributed ledger of the blockchain network, generating transaction data blocks with timestamp sequences. A security assessment rule set based on smart contracts is invoked to automatically assess the transaction data blocks on the blockchain, generating a preliminary security assessment result that includes data consistency verification results, transaction compliance verification results, and physical information consistency verification results. Based on this preliminary security assessment result, a dynamic security assessment model is constructed to simulate and extrapolate potential risks in the transaction data set to be assessed, obtaining quantitative indicators of security risks and risk transmission paths. Finally, the preliminary security assessment result is weighted and corrected based on the quantitative indicators of security risks and risk transmission paths, thereby generating the final new energy transaction security level.

[0020] In one embodiment of the present invention, the data set to be evaluated includes power generation output data, grid dispatch data, energy consumption load data, and market transaction contract data. Real-time output curves and predicted output data of new energy power generation equipment are extracted from the power generation output data to establish a power generation equipment output map. This map records the actual and predicted power output sequences of each new energy power generation device at different time scales. In another embodiment, the real-time operating status of the power grid and the sequence of dispatch instructions are extracted from the grid dispatch data to establish a grid dispatch map. This map includes grid node voltages, real-time line power flow values, and day-ahead and intraday dispatch plan instructions from the dispatch center.

[0021] In practical implementation, adjustable load curves and energy consumption behavior characteristics are extracted from energy-side load data to establish an energy load map. This map aggregates the power curves and historical energy consumption patterns of base load, interruptible load, and transferable load from different energy-consuming entities. In further implementation, transaction prices, transaction volumes, and transaction entity information are extracted from market transaction contract data to establish a market transaction map. This map clearly identifies the buyers and sellers of each transaction contract, the agreed-upon transaction volume, the delivery time, and the corresponding settlement price. It can be understood that the power generation output map, grid dispatch map, energy load map, and market transaction map all carry a unified timestamp index and spatial location label during their creation.

[0022] The power generation output map, power grid dispatch map, energy load map, and market transaction map are spatiotemporally aligned and correlated to generate a structured transaction data map containing multiple correlations of power, capital, and information. Spatiotemporal alignment refers to matching and arranging data points in different maps in the time and spatial dimensions based on a unified time base and physical location identifier. In specific implementation, a spatiotemporal alignment mapping function is used to achieve the correlation of multi-map data. The expression of the function is as follows:

[0023] in: Representative power generation equipment output diagram Represents the power grid dispatch map. Representative energy load diagram, Represents market trading patterns. These represent unified time reference parameters and unified spatial location identifier parameters. Represents spatiotemporal alignment and association mapping operations. This represents the generated structured transaction data graph. In specific implementations, the association mapping operation establishes the power flow mapping relationship between power generation equipment and grid nodes, the fund flow mapping relationship between trading entities and fund accounts, and the information flow mapping relationship among all participants. It can be understood that the power flow mapping relationship describes the physical path and quantity relationship of electrical energy flowing from power generation equipment through grid nodes to energy users. In some embodiments, the fund flow mapping relationship describes the fund transfer path and amount relationship based on market transaction contracts, such as electricity bill payments and subsidy settlements.

[0024] In some embodiments, the information flow mapping relationship describes the sending, receiving, and verification relationships of information such as power generation forecasts, load forecasts, transaction applications, and dispatch instructions. Optionally, after completing spatiotemporal alignment, each entity node in the structured transaction data graph is associated with its role and connection status in the power flow, capital flow, and information flow networks. Optionally, the data structure of the structured transaction data graph is represented using an attribute graph model, where nodes represent entities such as power generation equipment, grid nodes, energy users, and transaction entities, edges represent power, capital, or information interaction relationships between entities, and both edges and nodes contain fields describing their attributes.

[0025] In one embodiment of the present invention, see [reference] Figure 2In implementation, the structured transaction data graph is segmented according to preset data partitioning rules, generating multiple data slices. These partitioning rules can be based on transaction time windows, geographical regions, or transaction entity types. Each data slice contains a subset of entity nodes from the structured transaction data graph, along with their associated edges and attribute data. A hash operation is performed on each data slice to generate a corresponding hash value. The hash operation uses the SHA-256 algorithm to convert the binary sequence of the data slice into a fixed-length hash value string. These data slice hash values ​​are then used to construct a Merkle tree structure in chronological order, generating a Merkle tree root hash. In practice, the construction process involves treating all data slice hash values ​​as leaf nodes, pairing them together to calculate the hash value of the parent node, and recursively calculating layer by layer until a unique root node hash is generated. It can be understood that the Merkle tree root hash uniquely represents the complete data state of the structured transaction data graph at a specific time sequence. The Merkel root hash, timestamp information, and hash value of the previous block are packaged together to generate a new transaction data block. The timestamp information records the precise moment of data packaging, and the hash value of the previous block establishes a chain-like connection on the blockchain.

[0026] The consensus mechanism of the blockchain network verifies and stores new transaction data blocks on the chain, forming an immutable distributed ledger record. In some embodiments, the consensus mechanism adopts a practical Byzantine fault-tolerant algorithm, in which preset consensus nodes vote to verify the validity of the data in the new block.

[0027] The process involves reading transaction data blocks and their contained transaction data graphs from the distributed ledger of the blockchain network. This reading operation is completed through the node interface of the blockchain network, ensuring the integrity and consistency of the obtained data. The power flow mapping relationships in the transaction data graph are input into a physical constraint verification smart contract to perform a matching verification between the power generation plan and the grid power flow constraints, generating a physical consistency verification result. The physical constraint verification smart contract encodes physical rules such as the power flow equations of the grid, line capacity limits, and the upper and lower limits of generator output. In specific implementation, the matching verification is performed through a verification function. To achieve this, where: This represents the actual and planned power flow distribution derived from the power flow mapping relationship extracted from the transaction data map.

[0028] The set of physical constraints representing the power grid, functions The output is the physical consistency verification result, which indicates whether the power flow exceeds limits or violates physical laws. The fund flow mapping relationship in the transaction data graph is input into the market rule verification smart contract to perform compliance verification of the transaction price and market clearing rules, generating a transaction compliance verification result. The market rule verification smart contract encodes the calculation method of the market clearing price, price upper and lower limits, and settlement rules. The information flow mapping relationship in the transaction data graph is input into the information integrity verification smart contract to perform verification of the identity of the transaction entity and the integrity of the transaction data, generating an information integrity verification result. The information integrity verification smart contract verifies whether the digital signature of the transaction entity and the hash value of the transaction data are consistent with the on-chain record. Optionally, information integrity verification also includes checking whether the transaction data has been tampered with during transmission and storage.

[0029] The results of physical consistency verification, transaction compliance verification, and information integrity verification are aggregated to generate a preliminary security assessment result. In some embodiments, the aggregation operation combines the three verification results into a structured assessment report, which includes the verification status (pass, warning, failure) and a detailed description for each item. It can be understood that the preliminary security assessment result serves as the input basis for subsequent risk simulation and deduction. Optionally, the preliminary security assessment result itself is also written as a record to the blockchain network for auditing and traceability.

[0030] In one embodiment of the present invention, in a specific implementation, abnormal verification items in the preliminary security assessment results are analyzed to identify potential risk trigger points. Abnormal verification items in the preliminary security assessment results refer to entries marked as "warning" or "failure" in the physical consistency verification results, transaction compliance verification results, and information integrity verification results. Each abnormal verification item is associated with one or a group of specific entities and relationships in the structured transaction data graph. Potential risk trigger points are specific events or entity states that may trigger subsequent chain risks, parsed from these abnormal entries. Using potential risk trigger points as initial disturbances, a risk transmission network is constructed based on the power flow mapping relationship, capital flow mapping relationship, and information flow mapping relationship in the transaction data graph. In a specific implementation, all connection relationships between power generation equipment, grid nodes, energy users, and transaction entities in the transaction data graph are extracted. These connection relationships have been clearly defined in the structured transaction data graph.

[0031] The physical connections in the power flow mapping relationship are transformed into power risk edges in the risk transmission network. A power risk edge represents the physical impact of a power anomaly at one node on adjacent nodes along the power transmission path. Similarly, the fund transfers in the capital flow mapping relationship are transformed into capital risk edges in the risk transmission network. Capital risk edges represent the transmission of financial risk between trading entities due to one party's payment default or settlement failure. Finally, the information interaction in the information flow mapping relationship is transformed into information risk edges in the risk transmission network. Information risk edges represent the transmission of decision-making risks caused by erroneous or delayed information during information transmission between entities. Using the entity corresponding to a potential risk trigger point as the starting node and the power risk edge, capital risk edge, and information risk edge as connections, a risk transmission network containing multiple types of risk transmission paths is constructed.

[0032] In a risk transmission network, a disturbance scenario is simulated to deduce the spread of risk along power flow, capital flow, and information flow paths. The simulation takes identified potential risk trigger points as input, sets their initial impact strength and range, and iteratively calculates the propagation of risk impact based on the connectivity and transmission attributes of each edge in the risk transmission network. In specific implementation, the model for calculating the transmission strength of risk along the edges is as follows:

[0033] in: Representative node exist The intensity of the risk impact at any given time Represents all nodes The set of neighboring nodes connected by risk edges Represents the node To the node The weight coefficient of the risk edge, Representing neighbor nodes exist The intensity of the risk impact at any given moment This represents the inherent conduction attenuation coefficient of the risk edge. This section describes aggregation and attenuation functions representing the intensity of risk impact. It quantifies the scope, intensity, and duration of impact during risk diffusion, generating a quantitative indicator of safety risk. The scope of impact refers to the total number or percentage of nodes affected in the final state of the risk transmission network. In some embodiments, the intensity of impact is a statistical measure of the final risk impact intensity of all affected nodes. The duration of impact refers to the time elapsed from the initial disturbance to the point where the risk impact intensity of all nodes in the network reaches zero or falls below a threshold. The section records key transmission nodes and paths during risk diffusion, generating risk transmission paths. Key transmission nodes are those whose state changes significantly amplify or inhibit the scope and speed of risk diffusion during risk simulation. A risk transmission path is a sequence of risk edges connecting the starting node to any affected node. Optionally, risk transmission paths are categorized and labeled according to the type of risk they transmit. Optionally, key transmission nodes are identified based on their centrality measure within the entire risk transmission network.

[0034] See Figure 3 This is a bar chart comparing three types of risk quantification indicators. The wide-ranging transmission of information risk corresponds to the characteristic that erroneous or delayed information can trigger multi-node decision-making risks when information is transmitted between entities such as power generation, power grid, energy consumption, and trading. The high intensity and long duration of power risk reflect the severe and lasting impact of power anomalies in the physical power grid on the operation of the power system. This chart visually reveals the characteristics of power risk (highest impact intensity) and information risk (widest impact range), helping the team quickly identify core control targets and prioritize resources for real-time monitoring of power risk and blocking the spread of information risk. The quantitative comparison of the three types of risks verifies the rationality of the risk transmission network. For example, the wide-ranging transmission of information risk confirms the high connectivity of information flow among multiple entities, providing data support for optimizing the risk transmission network model.

[0035] In one embodiment of the present invention, in a specific implementation, the risk weight of each risk transmission path is calculated based on the scope and intensity of influence in the security risk quantification indicators. The scope of influence is quantified by the proportion of affected nodes or entities in the risk transmission network, and the intensity of influence is quantified by the average or maximum value of the risk influence intensity values ​​reached by the affected nodes at the end of the risk simulation. In some embodiments, the risk weight of a specific risk transmission path is calculated by performing a weighted product operation on the quantified value of the scope of influence and the quantified value of the intensity of influence. The risk weight is then weighted and fused with the corresponding verification results in the preliminary security assessment results to generate a corrected verification score. In a specific implementation, the physical consistency verification results, transaction compliance verification results, and information integrity verification results in the preliminary security assessment results are quantified into numerical scores, for example, the "pass" status is mapped to a full score, the "warning" status to a medium score, and the "failure" status to a low score or zero score.

[0036] The impact scope of the security risk quantification index is quantified as an impact breadth coefficient, a value between 0 and 1, representing the extent to which the risk may potentially spread. The impact intensity of the security risk quantification index is quantified as an impact depth coefficient, a value greater than 0, representing the severity of the risk's impact on a single entity. The impact breadth coefficient and impact depth coefficient associated with each verification result are determined based on the risk transmission path. This means that if an abnormal verification item is identified as a potential risk trigger point and a risk transmission path is generated, then the numerical score corresponding to that abnormal verification item will be associated with the impact breadth coefficient and impact depth coefficient calculated from these paths.

[0037] A weighting function is constructed using the breadth and depth of influence coefficients to perform a weighted calculation on the numerical scores, generating a weighted individual verification score. In practice, a specific form of the weighting function is as follows:

[0038] in: Representing the The weighted score of each verification item. Representing the The raw numerical score of each verification item. Representative and the The influence breadth coefficient associated with each verification item Representative and the The influence depth coefficient associated with each verification item The adjustment parameter representing overall risk sensitivity, The basic threshold parameter represents the impact of risk. All weighted individual verification scores are summed and normalized to generate a corrected verification score. The summation is to add up the weighted scores of the three dimensions of physical consistency, transaction compliance, and information integrity. The normalization maps the summed scores to a uniform score range with an upper limit.

[0039] Based on the topology of the risk propagation path, the clustering coefficient and propagation efficiency of the risk in the network are calculated. The clustering coefficient measures the tightness of the connections between nodes in the risk propagation network, while the propagation efficiency measures the average speed or ease with which the risk spreads along the network path. Based on the clustering coefficient and propagation efficiency, the corrected verification score is adjusted a second time to generate a comprehensive security score. In practice, this second adjustment is achieved through an adjustment factor. To achieve, It is a function of the clustering coefficient and propagation efficiency; the overall security score is the modified verification score plus an adjustment factor. The product of the comprehensive security score and the predetermined security level is used to map the comprehensive security score to a predefined security level range, thereby determining the final security level of the new energy transaction. The predefined security level range divides the comprehensive security score range into multiple consecutive sub-ranges, each sub-range corresponding to a discrete security level, such as "high," "medium," "low," or a more granular classification. In some embodiments, the security level range is defined by a mapping table, see Table 1.

[0040] Table 1: Mapping Table of Security Level Classification Intervals

[0041] Optionally, the revised verification score generation process will generate an intermediate score for each verification dimension. It can be understood that the final new energy transaction security level is an assessment conclusion that integrates static rule verification and dynamic risk simulation results.

[0042] See Figure 4 This is a graph illustrating the relationship between block size and verification time on a blockchain. The block size and verification time show a significant positive correlation. Larger blocks have longer verification times, while smaller blocks have shorter times. Even as the data size increases from 15MB to 25MB, the verification time only increases from 2 seconds to 3.5 seconds, indicating that the blockchain's verification mechanism is highly efficient, with the increase in time cost far less than the increase in data size. The changes in data size and verification time for blocks 2, 3, and 5 are completely synchronized, confirming that in this scenario, data size is the most significant factor determining verification time. The gradual increase in verification time demonstrates that the current blockchain network's performance can support the high-frequency data upload requirements of new energy transactions, providing technical assurance for the real-time nature of dynamic security assessments.

[0043] In one embodiment of the present invention, in specific implementation, newly added transaction data in the new energy trading market is periodically acquired to form an incremental transaction data set. Periodic acquisition means that all transaction-related data newly generated in the previous cycle are automatically collected from the market data interface, the power grid dispatch system, and the energy consumption-side metering system at preset time intervals, such as every 15 minutes or every hour. The structure of the incremental transaction data set is consistent with the initial transaction data set to be evaluated. Multi-source data fusion processing is performed on the incremental transaction data set to generate an incremental transaction data map. The process of multi-source data fusion processing is consistent with the process of generating the initial structured transaction data map. The incremental transaction data map also includes power flow mapping relationships, capital flow mapping relationships, and information flow mapping relationships, and has its own independent spatiotemporal stamp.

[0044] Incremental transaction data graphs are written into the blockchain network to form new transaction data blocks. The writing process follows the same procedure as the initial data, including data slicing, hash calculation, Merkle tree construction, packaging to generate new blocks, and uploading to the chain via the consensus mechanism. The new transaction data blocks are linked to the previous transaction data blockchain through hash pointers. An on-chain automated evaluation of the new transaction data blocks is performed using a security assessment rule set based on smart contracts, generating preliminary incremental security assessment results. The on-chain automated evaluation process is consistent with the evaluation process for the initial transaction data blocks. The preliminary incremental security assessment results also include physical consistency verification results, transaction compliance verification results, and information integrity verification results for the data within this incremental period.

[0045] Combining historical assessment results and incremental preliminary security assessment results, the parameters of the dynamic security assessment model are updated, and potential risks are simulated and extrapolated for the newly added transaction data set. This updates the quantitative indicators of security risks and the risk transmission paths. In specific implementation, historical transaction data blocks and their corresponding historical security assessment results are obtained from the distributed ledger of the blockchain network. Historical data is extracted by traversing all relevant blocks within a specific time window on the blockchain. The historical security assessment results are compared and analyzed with actual historical risk events to calculate the historical assessment accuracy. Actual historical risk events are recorded in an independent risk event database, which records each actual physical failure, transaction default, or information security incident and its scope and intensity of impact. Based on the historical assessment accuracy, the confidence parameter of the risk transmission extrapolation in the dynamic security assessment model is adjusted. The confidence parameter reflects the model's reliability estimate of its own extrapolation results; when the historical assessment accuracy is high, the confidence parameter is increased; when the historical assessment accuracy is low, the confidence parameter is decreased. In specific implementation, the confidence parameter... The update formula is

[0046] in: This represents the updated confidence level parameter. This represents the confidence level parameter before the update. Represents the learning rate coefficient. This represents the calculated historical assessment accuracy. This represents the preset accuracy target value. The incremental preliminary security assessment results are input into the dynamic security assessment model after adjusting the confidence parameters. The edge weights and node influence factors of the risk transmission network are retrained. Retraining is based on incremental data and confidence parameters, adjusting the transmission weights of each risk edge (power risk edge, funding risk edge, information risk edge) in the risk transmission network, as well as the risk sensitivity or resistance factor of each entity node, through an optimization algorithm. Based on the retrained dynamic security assessment model, updated model parameters are generated, thus completing the update of the dynamic security assessment model. According to the updated security risk quantification indicators and risk transmission paths, the final new energy transaction security level is adjusted in real time.

[0047] In some embodiments, real-time adjustments involve periodically repeating the entire assessment process. Each periodic assessment uses the latest model parameters and incremental data to output a final new energy trading security level that reflects the current security status. In some embodiments, the update process can also be triggered by specific events, such as when a significant anomaly check item is detected. Optionally, the complete process of each dynamic update, including the acquired incremental data, the generated incremental assessment results, and the updated model parameter version, is logged on the blockchain. It is understood that this continuous dynamic update mechanism enables the security assessment to adapt to changes in market conditions, grid operating status, and risk patterns.

[0048] See Figure 5This is a bar chart showing the dynamic updates of edge weights in the risk transmission network. The increase in the information risk weight suggests the need to add cross-node information consistency verification rules to smart contracts to address the expanding scope of information risk transmission. The decrease in the power risk weight indicates an overestimation of physical risk in the early stages; this can be used to optimize the risk warning threshold, reduce unnecessary emergency responses, and improve system efficiency. The stability of the capital risk weight indicates that the current capital transaction compliance verification rules are largely adapted to business needs, requiring only minor iterations. The continuous adjustment of the edge weights for the three types of risks directly demonstrates that the dynamic security assessment model can continuously correct its understanding of risk transmission through incremental data learning, validating the design logic of "continuous updating and self-optimization." The fluctuation range of the weights reflects the model's stability. For example, a small fluctuation in the capital risk weight indicates stable transmission characteristics, reducing the computational resource investment in the corresponding module; while an increase in the information risk weight requires reserving more computing power for the information risk transmission deduction module.

[0049] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A blockchain-based security assessment method for new energy transactions, characterized in that, include: Obtain the set of transaction data to be evaluated in the new energy trading market; The set of transaction data to be evaluated is subjected to multi-source data fusion processing to generate a structured transaction data map, which includes power flow mapping relationship, capital flow mapping relationship and information flow mapping relationship; The structured transaction data graph is written into the distributed ledger of the blockchain network to generate transaction data blocks with timestamp sequences. The system invokes a security assessment rule set based on smart contracts to perform on-chain automated assessment of the transaction data block, generating a preliminary security assessment result. The preliminary security assessment result includes data consistency verification result, transaction compliance verification result, and physical information consistency verification result. Based on the preliminary security assessment results, a dynamic security assessment model is constructed to simulate and extrapolate potential risks to the set of transaction data to be assessed, and to generate quantitative indicators of security risks and risk transmission paths. By combining the aforementioned quantitative indicators of safety risks and risk transmission paths, the preliminary safety assessment results are weighted and corrected to generate the final safety level of new energy transactions.

2. The blockchain-based new energy transaction security assessment method according to claim 1, characterized in that, The step of performing multi-source data fusion processing on the dataset of transactions to be evaluated to generate a structured transaction data map includes: The dataset of transactions to be evaluated includes power generation output data, grid dispatch data, energy consumption load data, and market transaction contract data. Extract the real-time output curves and predicted output data of the new energy power generation equipment from the power generation side output data, and establish the power generation equipment output spectrum; Extract the real-time operating status of the power grid and the sequence of dispatch instructions from the power grid-side dispatch data to establish a power grid dispatch map; Adjustable load curves and energy consumption behavior characteristics are extracted from the energy consumption-side load data to establish an energy consumption load map. Extract transaction prices, transaction volumes, and transaction entity information from the market transaction contract data to establish a market transaction map; The power generation output map, power grid dispatch map, energy load map, and market transaction map are spatiotemporally aligned and correlated to generate a structured transaction data map containing multiple correlations of power, funds, and information.

3. The blockchain-based new energy transaction security assessment method according to claim 2, characterized in that, The step of writing the structured transaction data graph into the distributed ledger of the blockchain network to generate transaction data blocks with timestamp sequences includes: The structured transaction data map is divided according to preset data partitioning rules to generate multiple data slices; Perform a hash operation on each data slice to generate the corresponding data slice hash value; The data slice hash values ​​are arranged in chronological order to construct a Merkle tree structure, generating the Merkle tree root hash. The Merkle root hash, timestamp information, and hash value of the previous block are packaged together to generate a new transaction data block; The new transaction data blocks are verified and stored on the blockchain through the consensus mechanism of the blockchain network, forming an immutable distributed ledger record.

4. The blockchain-based new energy transaction security assessment method according to claim 1, characterized in that, The invocation of a security assessment rule set based on smart contracts performs on-chain automated assessment of the transaction data block, generating preliminary security assessment results, including: Read the transaction data block and its contained transaction data graph from the distributed ledger of the blockchain network; The power flow mapping relationship in the transaction data map is input into the physical constraint verification smart contract to perform the matching verification of the power generation plan and the power flow constraint of the grid, and generate the physical consistency verification result. The fund flow mapping relationship in the transaction data graph is input into the market rule verification smart contract to perform the compliance verification of the transaction price and the market clearing rules, and generate the transaction compliance verification result; The information flow mapping relationship in the transaction data graph is input into the information integrity verification smart contract to perform the verification of the identity of the transaction subject and the integrity of the transaction data, and generate the information integrity verification result; The preliminary security assessment results are generated by summarizing the physical consistency verification results, transaction compliance verification results, and information integrity verification results.

5. The blockchain-based new energy transaction security assessment method according to claim 4, characterized in that, The process of constructing a dynamic security assessment model based on the preliminary security assessment results, simulating and extrapolating potential risks in the dataset of transactions to be assessed, and generating quantitative security risk indicators and risk transmission paths includes: Analyze the abnormal verification items in the preliminary security assessment results to identify potential risk trigger points; Using the potential risk trigger points as initial disturbances, a risk transmission network is constructed based on the mapping relationships of power flow, capital flow, and information flow in the transaction data graph; In the aforementioned risk transmission network, a disturbance scenario is simulated to deduce the diffusion process of risk along the paths of power flow, capital flow, and information flow; Quantify the scope, intensity, and duration of impact during the risk diffusion process to generate quantitative indicators of safety risks; Record the key transmission nodes and paths in the risk diffusion process, and generate the risk transmission path.

6. The blockchain-based new energy transaction security assessment method according to claim 5, characterized in that, The process of constructing a risk transmission network based on the potential risk trigger point as the initial disturbance and according to the mapping relationship of power flow, capital flow, and information flow in the transaction data graph includes: Extract the connection relationships between power generation equipment, power grid nodes, energy users, and trading entities in the transaction data map; Transform the physical connections in the power flow mapping relationship into power risk edges in the risk transmission network; Transform the fund transfers in the fund flow mapping relationship into the fund risk edge in the risk transmission network; Transform the information interaction in the information flow mapping relationship into the information risk edge in the risk transmission network; Using the entities corresponding to the potential risk trigger points as starting nodes, and connecting them with power risk edges, capital risk edges, and information risk edges, a risk transmission network containing multiple types of risk transmission paths is constructed.

7. The blockchain-based new energy transaction security assessment method according to claim 1, characterized in that, The preliminary safety assessment results are weighted and corrected by combining the aforementioned safety risk quantification indicators and risk transmission paths to generate the final new energy transaction safety level, including: Based on the scope and intensity of influence in the aforementioned safety risk quantification indicators, calculate the risk weight for each risk transmission path; The risk weights are weighted and fused with the corresponding verification results in the preliminary security assessment results to generate a corrected verification score. Based on the topology of the risk transmission path, calculate the risk aggregation coefficient and propagation efficiency in the network; Based on the aggregation coefficient and propagation efficiency, the corrected verification score is adjusted a second time to generate a comprehensive security score. The comprehensive security score is mapped to a preset security level range to determine the final new energy transaction security level.

8. The blockchain-based new energy transaction security assessment method according to claim 7, characterized in that, The step of weighting and fusing the risk weights with the corresponding verification results in the preliminary security assessment to generate a corrected verification score includes: The physical consistency verification results, transaction compliance verification results, and information integrity verification results in the preliminary security assessment results are quantified into numerical scores. The scope of influence in the aforementioned safety risk quantification indicators is quantified as the breadth of influence coefficient, and the intensity of influence is quantified as the depth of influence coefficient. Based on the risk transmission path, determine the impact breadth coefficient and impact depth coefficient associated with each verification result; A weighting function is constructed using the influence breadth coefficient and influence depth coefficient to perform a weighted calculation on the numerical score, generating a weighted individual verification score. The weighted individual verification scores are summed and normalized to generate the corrected verification score.

9. The blockchain-based new energy transaction security assessment method according to claim 1, characterized in that, The method also includes the step of continuously and dynamically updating the final new energy transaction security level: Periodically acquire new transaction data from the new energy trading market to form an incremental transaction data set; Perform the multi-source data fusion processing on the incremental transaction data set to generate an incremental transaction data map; The incremental transaction data map is written into the blockchain network to form a new transaction data block; The newly added transaction data block is automatically evaluated on-chain by invoking the security assessment rule set based on smart contracts, and an incremental preliminary security assessment result is generated. Based on the historical assessment results and the incremental preliminary security assessment results, the parameters of the dynamic security assessment model are updated, and potential risks are simulated and deduced for the newly added transaction data set, thereby updating the security risk quantification indicators and risk transmission paths. The final new energy transaction security level is adjusted in real time based on the updated security risk quantification indicators and risk transmission paths.

10. The blockchain-based new energy transaction security assessment method according to claim 9, characterized in that, The step of updating the parameters of the dynamic security assessment model by combining historical assessment results and the incremental preliminary security assessment results includes: Obtain historical transaction data blocks and their corresponding historical security assessment results from the distributed ledger of the blockchain network; The historical security assessment results are compared and analyzed with actual historical risk events to calculate the accuracy rate of the historical assessment. Based on the historical assessment accuracy, adjust the confidence parameters of risk transmission inference in the dynamic security assessment model; The incremental preliminary security assessment results are input into the dynamic security assessment model after adjusting the confidence parameters, and the edge weights and node influence factors of the risk transmission network are retrained. Based on the retrained dynamic security assessment model, updated model parameters are generated, thereby completing the update of the dynamic security assessment model.

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