New energy power transaction system and method considering block chain

Abstract

The invention relates to the technical field of energy management, in particular to a new energy power transaction system and method considering a block chain, and the system comprises a node matching sequence adjustment module, an electric quantity transmission error judgment module, an on-chain record verification module, an abnormal transaction caching module, and a transaction full-link statistics module. According to the method, the actual activity degree of each node is dynamically identified, the matching sequence is adjusted according to the actual activity degree, balanced distribution of matching opportunities is achieved, the actual deviation condition of energy transmission is determined by combining a bidirectional comparison mechanism of electric quantity transmission data, and a traceable consistency verification process is formed by searching and checking multi-source records through unified identification codes. Abnormal data caching and tagging processing are performed on transactions with inconsistent records, so that cooperative improvement of matching fairness, electric quantity transmission accuracy, on-chain data consistency and exception recognition precision is realized in the whole transaction process, and resource allocation rationality and data processing transparency in new energy power transactions are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a system and method for new energy power trading considering blockchain. Background Art

[0002] The technical field of new energy power trading systems involves energy management throughout the entire process of acquiring, allocating, metering, dispatching, and trading renewable energy power resources. It uses information technology to achieve optimal allocation and orderly trading of power resources, systematically combining energy supply and demand forecasts with user-side response mechanisms and market-based pricing methods to promote the efficient utilization of a high proportion of new energy access to the power grid. Specifically, a new energy power trading system refers to a system that matches and settles power purchases and sales through a unified trading platform established by power grid companies based on a centralized database structure. It typically uses a manual review mechanism based on user electricity metering data to determine transaction volumes, and employs a static pricing strategy for matching. Transactions are completed through a central server to complete data storage and transaction confirmation operations, thereby achieving market circulation and resource matching for energy.

[0003] In the existing new energy power trading process, due to the over-reliance on the centralized matching model of the central platform, it is impossible to flexibly adjust the matching order according to the changes in the transaction frequency of the trading nodes in actual transactions. The electricity accounting based on one-way data may cause the omission of transmission deviations. Under the mechanism based on manual review, it is difficult to efficiently identify electricity deviations and on-chain record anomalies. When errors or inconsistencies occur in transaction data, there is a lack of effective identification and traceability methods. For example, in high-frequency trading scenarios, when the node activity varies greatly and the electricity fluctuations are frequent, the traditional system is prone to problems such as uneven matching opportunities, abnormal data backlogs and transaction failures, which affect the fairness of transactions and the stability of system operation. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and propose a system and method for new energy power trading considering blockchain.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions: A system for new energy power trading considering blockchain includes: The node matching order adjustment module obtains the node identities of all power generators and power consumers in the current matching pool, calculates the cumulative number of transactions for each node, swaps the order of nodes with cumulative transaction times higher than the transaction frequency benchmark value with nodes with cumulative transaction times lower than the transaction frequency benchmark value, and records the node identification sequence to generate a node matching priority list; The power transmission error determination module calculates the error rate based on the node matching priority list and the power output of the power generator and the power received by the power consumer for each transaction. If the error rate is greater than the power transmission error threshold, it is marked as exceeding the limit, and a power transmission error determination record is obtained; The on-chain record verification module reads each transaction identification code based on the power transmission error determination record, retrieves the corresponding matching, power generation and reception records in the blockchain ledger, determines whether the data is consistent within the transaction identification code range, marks inconsistent transactions as record anomalies, and generates an on-chain record consistency list; The abnormal transaction cache module filters transactions marked as record abnormalities based on the on-chain record consistency list, stores the corresponding transaction data into the cache area according to the transaction identification code, generates an abnormal reason tag, and generates abnormal transaction cache data.

[0006] As a further solution of the present invention, the node matching priority list includes a node identity sorting list, an exchange order record sequence, and a priority identification tag; the power transmission error judgment record includes an error value tag, an error limit exceedance tag, and a threshold comparison result; the on-chain record consistency list includes a transaction identification code mapping table, a data consistency tag, and a data verification status; the abnormal transaction cache data includes an abnormal identification code list, a cache data structure, and an abnormal cause tag.

[0007] As a further solution of the present invention, the node matching order adjustment module includes: The node identity recognition submodule extracts and records the node identifiers in each node identity information table based on all registered power generation nodes and power consumption nodes in the current matching pool. Combined with the historical transaction data recorded in the blockchain ledger, the node identity identifiers are matched with the buyer and seller information in the transaction records. Nodes with transaction records are selected as active nodes to generate an active node identifier sequence. The matching frequency calculation submodule reads the number of occurrences of the corresponding node in all transaction records in the blockchain ledger based on the unique identifier of each node in the active node identifier sequence, takes the cumulative number of transactions for each node as the transaction frequency value, and calculates the frequency deviation value of each node in combination with the transaction frequency benchmark value, and sorts and establishes a node frequency deviation list; The priority queue generation submodule compares the frequency deviation value corresponding to each node in the node frequency deviation list with the original queue position, exchanges the node order and updates the adjusted positions of all nodes, and records the adjusted node order in the blockchain account book to obtain the node matching priority list.

[0008] As a further solution of the present invention, the power transmission error determination module includes: The power record extraction submodule reads each completed matching transaction data in the blockchain ledger based on the node matching priority list, extracts the recorded generator node number and consumer node number, retrieves the power output record of the generator node at the transaction time and the power reception record of the consumer node at the same time in each transaction, and identifies each valid transaction with a number to generate a matching transaction power data set; The error difference calculation submodule calculates the relative error percentage of power transmission in each transaction based on the power output value of the power generator and the power received value of the power consumer recorded in the matched transaction power data set, and obtains a transaction power error rate list; The error result judgment submodule compares all transaction error rates with the power transmission error threshold based on the error rate of each transaction record in the transaction power error rate list, identifies the transaction numbers with error rates greater than the power transmission error threshold, marks the status as out of limit, and marks the rest as normal, and integrates them to obtain the power transmission error judgment record.

[0009] As a further solution of the present invention, the on-chain record verification module includes: The identification code parsing submodule extracts a unique transaction identification code based on each transaction data attached to the power transmission error determination record, decomposes the identification code, parses the timestamp field into a time format, and constructs a unique transaction request index. It then sequentially searches the matching records, power generation node data records, and power consumption node data records, and groups the associated fields by node number, time, power value, and transaction number to generate a transaction binding field set. The ledger record comparison submodule compares the three record fragments of each transaction in the transaction binding field set, checks the time fields of the records in turn to see if there is any mismatch, determines if there is any offset between the matching time and the node-side record time, and cross-identifies the transaction number field and the node number field one by one to confirm whether the three records belong to the same transaction, thereby obtaining field consistency offset information; The record consistency judgment submodule determines whether the on-chain record consistency matching criteria are met based on the field conflict type and time offset identified in the field consistency offset information. If the node number, power field and time field are all consistent, the transaction is marked as a normal record. If any field cannot be matched in the corresponding three records, the corresponding transaction is marked as a record abnormality, and the on-chain record consistency list is obtained.

[0010] As a further solution of the present invention, the abnormal transaction cache module includes: The abnormal record screening submodule screens transaction numbers marked as abnormal based on each record in the on-chain record consistency list, uses the power generation record, power consumption record, and matching record corresponding to each abnormal transaction number as the data source, constructs an abnormal transaction data unit, and obtains abnormal transaction screening data; The identification code cache writing submodule filters each transaction record in the abnormal transaction data, organizes the transaction field information bound in the same record according to the field classification, sets a unified field sequence structure, converts the format according to the node number, power value and timestamp sequence, and writes the data into the cache area according to the field classification method. At the same time, the difference relationship between the fields is content-marked to obtain the cache write field difference marking result; The abnormal label generation submodule converts various difference types into text labels according to the field difference record content in the cache write field difference tagging result. If there are multiple field differences in a record, multiple labels are spliced ​​and combined to form label content, and the transaction number is used as the index field, associated with each cache data record for storage, to generate abnormal transaction cache data.

[0011] As a further embodiment of the present invention, the system further comprises: The full-link transaction statistics module counts the number of matching order adjustments and the number of abnormal transaction caches that occur in all nodes during the transaction process based on the abnormal transaction cache data and the node matching priority list, forms full-link statistical information, and obtains a full-link operation statistics table for blockchain new energy power transactions; The blockchain new energy power transaction full-link operation statistics table includes the matching order adjustment frequency, the number of abnormal transaction caches, and node operation status indicators.

[0012] As a further solution of the present invention, the transaction full-link statistics module includes: The matching order adjustment statistics submodule extracts and compares the ranking position of each node in different rounds based on the matching transaction records in the node matching priority list, compares the priority positions of the same node in adjacent rounds by number difference, and determines whether the position in the time series has changed. If there is a difference in position, it is recorded as a ranking adjustment event, and the event is bound to the node identifier and classified by node number. The number of times the node order changes during the whole matching process is recorded one by one to obtain the number of matching order adjustments; The abnormal cache statistics submodule reads each transaction number recorded in the abnormal transaction cache data, extracts the node number of the power generator and the node number of the power consumer, counts the number of times each node is marked as an abnormal transaction in the cache data, and records the cumulative frequency of occurrence. The two-dimensional data are archived correspondingly at the node level, and the abnormal feature data items under the node dimension are established to obtain the node abnormal interference record; The link data summary submodule matches each node field recorded in the node abnormal interference record and the matching order adjustment number value one by one according to the node number, and uniformly classifies them into the statistical summary table. The node number is used as the main index field, and the corresponding matching order adjustment number, abnormal cache number and node abnormal interference index are respectively registered as data fields for structured registration, and the blockchain new energy power transaction full-link operation statistics table is summarized.

[0013] A method for new energy power trading considering blockchain, comprising the following steps: S1: Obtain the node identities of all power generators and power consumers in the current matching pool, calculate the cumulative number of transactions for each node, swap the order of nodes with cumulative transaction times higher than the benchmark value with nodes with cumulative transaction times lower than the benchmark value, record the node identification sequence, and generate a node matching priority list; S2: Based on the node matching priority list, the error rate is calculated based on the power output of the power generator and the power received by the power consumer for each transaction. If it is greater than the power transmission error threshold, it is marked as exceeding the limit, and a power transmission error determination record is obtained; S3: Based on the power transmission error determination record, read each transaction identification code, search the corresponding matching, power generation and reception records in the blockchain ledger, determine whether the data is consistent within the transaction identification code range, mark inconsistent transactions as record anomalies, and generate an on-chain record consistency list; S4: Based on the on-chain record consistency list, filter out transactions marked as record abnormalities, store the corresponding transaction data in a cache area according to the transaction identification code, generate an abnormality reason tag, and generate abnormal transaction cache data; S5: Based on the abnormal transaction cache data and the node matching priority list, the number of matching order adjustments and abnormal transaction cache times that occur in all nodes during the transaction process are counted to form full-link statistical information and obtain a blockchain new energy power transaction full-link operation statistics table.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the actual activity of each node is dynamically identified and the matching order is adjusted accordingly to achieve a balanced distribution of matching opportunities. The actual deviation of energy transmission is clarified by combining the two-way comparison mechanism of power transmission data. A traceable consistency verification process is formed by retrieving and checking multi-source records through a unified identification code. Abnormal data is cached and labeled for transactions with inconsistent records, thereby achieving a coordinated improvement in matching fairness, power transmission accuracy, on-chain data consistency and abnormal identification accuracy in the entire transaction process. A comprehensive, quantifiable, diagnosable and optimizable new energy power transaction status perception system is constructed to achieve refined monitoring and systematic operation evaluation during the transaction process, thereby enhancing the rationality of resource allocation and the transparency of data processing in new energy power transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the node matching order adjustment module of the present invention; Figure 3 This is a flow chart of the power transmission error determination module of the present invention; Figure 4 This is a flow chart of the on-chain record verification module of the present invention; Figure 5 This is a flow chart of the abnormal transaction cache module of the present invention; Figure 6 This is a flow chart of the full-link transaction statistics module of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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.

[0017] 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, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0018] See also Figure 1 , a system for new energy power trading considering blockchain includes: The node matching order adjustment module obtains the node identities of all power generators and power consumers in the current matching pool, combines them with historical transaction records in the blockchain ledger, calculates the cumulative number of transactions for each node, and compares it with the transaction frequency benchmark value (the benchmark value defined by the distributed energy trading standard: ≥5 times / day for small and medium-sized power generation units). Nodes with a cumulative transaction frequency higher than the benchmark value are swapped with nodes in the queue with a transaction frequency lower than the benchmark value. The node identification sequence of this swap is recorded in the blockchain ledger to generate a node matching priority list. The power transmission error determination module, based on the node matching priority list, obtains the power output record of the power generator and the power reception record of the power consumer in each transaction, calculates the error rate, and compares it with the set power transmission error threshold (the measurement tolerance specified in the new energy grid communication standard is ±0.5% of rated capacity). If the error rate exceeds the threshold, it is marked as exceeding the limit and the power transmission error determination record is obtained; The on-chain record verification module determines the record based on the power transmission error, reads the unique transaction identification code bound to each transaction (using a 64-bit unique transaction identifier: the first 8 bits of the institution code + a 16-bit timestamp + a 40-bit hash), retrieves the corresponding matching, power generation and reception records in the blockchain ledger, and determines whether the data is consistent within the transaction identification code range. Inconsistent transactions are marked as record abnormalities, consistent transactions are marked as record normal, and an on-chain record consistency list is generated; The abnormal transaction cache module filters out transactions marked as abnormal based on the consistency list of on-chain records, stores the corresponding transaction data in the cache area according to the transaction identification code, and generates an abnormal reason label for each cache record to generate abnormal transaction cache data; The full-link transaction statistics module is based on the abnormal transaction cache data and the node matching priority list. It counts the number of matching order adjustments and abnormal transaction caches that occur in all nodes during the transaction process, forms full-link statistical information, and obtains the full-link operation statistics table of blockchain new energy power transactions.

[0019] The node matching priority list includes a node identity sorting list, an exchange order record sequence, and a priority identification label. The power transmission error judgment record includes an error value label, an error limit exceeding mark, and a threshold comparison result. The on-chain record consistency list includes a transaction identification code mapping table, a data consistency mark, and a data verification status. The abnormal transaction cache data includes an abnormal identification code list, a cache data structure, and an abnormal reason label. The blockchain new energy power transaction full-link operation statistics table includes the matching order adjustment frequency, the number of abnormal transaction caches, and the node operation status indicator.

[0020] See also Figure 2 , the node matching order adjustment module includes: The node identity recognition submodule extracts and records the node identifiers in each node identity information table based on all registered power generation nodes and power consumption nodes in the current matching pool. Combined with the historical transaction data recorded in the blockchain ledger, the node identity identifiers are matched with the buyer and seller information in the transaction records. Nodes with transaction records are selected as active nodes to generate an active node identifier sequence. Based on all the power generation nodes and power consumption nodes registered in the current matching pool, we first need to read the latest transaction snapshot data about the matching pool structure in the blockchain account book, and extract the registration information field in nodes. This field records the registration type and active status of each node, and uses the node's unique identification number as the subsequent tracking index. During the extraction process, the nodes are classified into two groups, "power generation party" and "power consumption party", according to the registration type field. For example, node numbers N001 and N003 are power generation parties, and N002, N004, and N005 are power consumption parties. To further determine whether the node is active in the current cycle, you can filter it through the node activity status field in the account book. A value of 1 indicates that the current cycle is active, and a value of 0 indicates an inactive state. In this example, N001, N002, and N005 are inactive. If the status field values ​​of N003 and N005 are both 1, they are listed as candidate nodes that can enter the matching system. Then, the historical transaction table in the ledger is accessed to read each node one by one to see if there has been a valid transaction in the past n days. The "transaction timestamp" and "buyer / seller node ID" are used as key fields for comparison and matching. For example, if node N001 appears three times in the transaction record, with the times being "2024-08-05", "2024-08-06", and "2024-08-07", it can be confirmed as an active transaction node. On the contrary, if the node has never appeared in the transaction table, it will not be considered. For example, if there is no transaction record for node N004, it will not be included in the active node list. Combining the above process, a list of active nodes in the current matching cycle can be gradually established, forming the following preliminary list of active nodes: Table 1 Active node screening results Node number Node Type Current Status Number of historical transaction records Is the node active? N001 Power generation active 3 yes N002 Electricity users active 2 yes N003 Power generation active 3 yes N004 Electricity users Inactive 0 no N005 Electricity users active 2 yes As shown in Table 1, the active nodes finally screened out by the system are N001, N002, N003, and N005, and the identification numbers of these four nodes are combined into a preliminary active node identification sequence in numerical order.

[0021] The matching frequency calculation submodule reads the number of occurrences of the corresponding node in all transaction records in the blockchain ledger based on the unique identifier of each node in the active node identifier sequence, and uses the cumulative number of transactions for each node as the transaction frequency value. Combined with the transaction frequency benchmark value, the formula is used: ; Obtain the frequency deviation value of each node by calculation, sort and establish a node frequency deviation list, where: Representation node The frequency deviation value (unit: times), Representation node In the The cumulative number of transactions per day (unit: times), Indicates the Average number of node transactions per day for the entire system (unit: times), Representation node The benchmark value of transaction frequency is set to 5 times (unit: times). Indicates the total number of days in the statistical period; Based on the above active node identification sequence, we enter the next stage of the matching frequency calculation process. We call the daily transaction record table in the blockchain ledger and extract and summarize the transaction frequency of each node in the past n consecutive days. Specifically, n is set to 3 days. The average number of matching transactions in the system is calculated for each day. The sample data is as follows: Table 2 Daily transaction times of nodes and average transaction frequency of the system Node number Number of transactions on the first day Number of transactions on the second day Number of transactions on the 3rd day System average on the first day System average on the second day System average on the 3rd day N001 6 7 8 5 5 5 N002 3 4 2 5 5 5 N003 5 6 5 5 5 5 N005 4 5 6 5 5 5 Refer to Table 2. Taking N001 as an example, the number of transactions is 6, 7, and 8, and the average value of the three days is 5. Substitute it into the formula and calculate as follows: ; ; Similarly, the number of transactions for N003 is 5, 6, and 5: ; ; After obtaining the frequency deviation values ​​of all active nodes, construct a frequency deviation value sequence: Table 3 Frequency deviation value sequence table Node number Frequency deviation value (times) N001 3.6 N002 4.47 N003 3.93 N005 3.8 Finally, based on the numerical sequence, the node frequency deviation list is sorted and generated.

[0022] The frequency deviation value refers to the absolute degree of deviation of the average daily matching frequency of a specific node from the benchmark matching frequency set by the system within a specified statistical period. It is used to measure the degree of consistency between the matching activity of the node and the system standard. Specifically, this value is calculated by calculating the ratio of the daily matching frequency of the node to the average matching frequency of the entire system on that day, and then taking the difference between the average within the period and the set benchmark value, and finally expressed in the form of an absolute value. The smaller the frequency deviation value, the more stable the matching behavior of the node within the period and the closer it is to the target benchmark value; conversely, it means that the matching frequency of the node fluctuates greatly or deviates from the target standard for a long time. Therefore, this indicator can be used to optimize the subsequent matching order and reflect the active consistency and participation quality of the node in the trading system.

[0023] The core operation logic of the formula is to quantitatively measure the stability of the node's matching frequency over multiple days, using the "average ratio difference" method. Indicates the node In continuous Statistical aggregation of all transaction frequency performance within the day; fractional part In order to calculate the node The ratio of the matching frequency of a day to the average frequency of the system on that day is used to reflect the deviation of the activity of the node compared with the overall system. The average of such ratios of multiple days (i.e. multiplying by ) can smooth the impact of daily fluctuations, thereby establishing a stability index; subtracting It is the "benchmark value" set in advance by the system to evaluate the frequency deviation of the node as a whole; the outer absolute value symbol This is to eliminate directional influences and ensure that whether the node deviates from the target by a factor of two, whether it is too high or too low, it is regarded as a deviation and included in the result value. It overall reflects the absolute deviation between the node and the system benchmark frequency within the statistical period. The smaller the value, the more stable the matching frequency and the closer it is to the target standard.

[0024] The priority queue generation submodule compares the frequency deviation value of each node in the node frequency deviation list with the original queue position, exchanges the node order and updates the adjusted positions of all nodes, and records the adjusted node order in the blockchain ledger to obtain the node matching priority list; According to the node deviation value sorting results in the aforementioned frequency deviation list, position comparison and queue exchange operations are performed from the minimum deviation value to the maximum value. First, the original node sequence list is read. For example, it is {N001, N002, N003, N005}, and the corresponding frequency deviations are 3.6, 4.47, 3.93, and 3.8 times respectively. It is found that although N001 has a small deviation, it is at the head of the queue and does not need to be adjusted. However, N002 has the largest deviation value but is ranked second. There is an improper order. The judgment logic is executed. If the front If the node deviation value is significantly greater than that of the subsequent node (the difference threshold can be set to 0.5 times), the position swap logic is triggered. For example, if the difference between N002 and N005 is 0.67 times, which is greater than the set threshold, their positions in the queue are swapped and the queue is updated to {N001, N005, N003, N002}. After the swap is completed, the timestamp of the current adjustment action and the node ID exchange sequence are recorded and stored in the "matching sort adjustment record field" in the blockchain account book. The record format is: time 2024-08-07 14:00, adjustment action: N002 N005, records it in the log field for subsequent blockchain audit calls, and finally outputs the node matching priority list generated at this stage.

[0025] See also Figure 3 , the power transmission error judgment module includes: The electricity record extraction submodule reads the data of each completed matching transaction in the blockchain ledger based on the node matching priority list, extracts the recorded generator node number and consumer node number, retrieves the electricity output record of the generator node at the transaction time and the electricity reception record of the consumer node at the same time in each transaction, and identifies each valid transaction with a number to generate a matching transaction electricity data set; Based on the node matching priority list, obtain the transaction number and node pair information of each transaction marked as "completed". By traversing the blockchain ledger transaction record table, extract the two fields of "generator node number" and "user node number" one by one, and call the node power monitoring record table with the transaction timestamp as the index to extract the "output power value" recorded by the generator node at the matching completion time point in each transaction and the "received power value" recorded by the user node. For example, the generator node of transaction number TX001 outputs 1000 power at 08:00:15. kWh, the electricity consumption node receives 995kWh. If the system detects that a transaction has missing energy records, or the record status is "invalid" or "abnormal" within the specified time period, the transaction data will be excluded. For example, transaction number TX006 is excluded from subsequent analysis because the status field is "invalid". For all valid transactions that meet the conditions, they are numbered in ascending order by transaction number and three fields are extracted: "transaction number", "generator electricity consumption (kWh)" and "consumer electricity consumption (kWh)" to form a structured data set, as shown in the following table: Table 4 Matching transaction electricity records Transaction ID Power generation capacity (kWh) Electricity consumption (kWh) TX001 1000 995 TX002 950 940 TX003 1020 1015 TX004 980 950 TX005 970 965 As shown in Table 4, this data set serves as the original basis for subsequent error judgment calculations, and the matching transaction electricity data set is obtained.

[0026] The error difference calculation submodule uses the formula: ; The relative error percentage of power transmission in each transaction is calculated and a list of transaction power error rates is obtained, where: Indicates the The transaction power error rate (percentage), Indicates the The output power of the power generation node in the transaction (kWh), Indicates the amount of electricity received by the electricity user node (kWh); According to the power generation of each group in the matching transaction power data set and receiving power , use the error calculation formula to calculate each item: TX001: ; TX002: ; TX003: ; TX004: ; TX005: ; The calculation results are structured and recorded as follows: Table 5 Transaction power error rate calculation table Transaction ID Error rate (%) TX001 0.2506 TX002 0.5291 TX003 0.2457 TX004 1.5544 TX005 0.2584 As shown in Table 5, the percentage of power transmission error for each transaction is calculated to form a list of transaction power error rates.

[0027] The normalized electricity error rate refers to the absolute deviation between the output electricity of the power generation node and the received electricity of the power consumption node in an electricity matching transaction. It is expressed as a percentage of the total electricity scale and reflects the relative degree of electricity loss during the transmission process. This indicator not only measures the numerical difference between the electricity data on both sides, but also eliminates the influence of factors such as different node scales and load capacities by normalizing the deviation value with the total electricity of both parties, so that the error assessment has a unified reference standard between high-power and low-power transactions. The higher the normalized electricity error rate, the greater the electricity loss under the same supply and receiving capacity; this indicator is suitable for monitoring transmission deviations in multi-node and multi-cycle electricity transactions, and is an important quantitative basis for evaluating the credibility of transaction data and the stability of network transmission.

[0028] The calculation logic of the formula is constructed based on the relatively symmetrical ratio expression of power deviation. The goal is to quantify the deviation between the power output of the power generator and the power received by the power consumer, and eliminate the absolute error misleading caused by the power level. In the formula, Indicates the absolute loss of electricity during transmission. The absolute value calculation is used to eliminate the positive and negative directionality when the generated power is higher or lower than the received power, and uniformly reflect the "degree of deviation"; the denominator It is the sum of the bilateral electrical quantities and is used as a normalized benchmark. It can achieve comparative comparability of errors at different energy levels and avoid the error amplification or compression effects caused by different system scales. It is finally expressed as a standardized error rate through percentage conversion × 100%, which is convenient for comparison and judgment with the set threshold (such as 0.5%).

[0029] The error result determination submodule compares the error rates of all transaction records in the transaction power error rate list with the power transmission error threshold of ±0.5%, identifies the transaction numbers with error rates greater than the power transmission error threshold of 0.5%, marks the status as out of limit, and marks the rest as normal, and integrates them to obtain the power transmission error determination record; According to the error rate of each transaction recorded in the transaction power error rate list , set the upper limit of the power deviation threshold specified in the new energy grid connection standard to 0.5%, and judge the error rate value of each transaction item by item. If the error rate is greater than 0.5%, the judgment result is "out of limit", otherwise it is "normal". For example, the error of TX002 is 0.5291%, and TX004 is 1.5544%. Both exceed the specified error standard and are marked as "out of limit". TX001, TX003 and TX005 are all below the threshold and are marked as "normal". The records are integrated according to the transaction number, error value, and error judgment result. The results are as follows: Table 6 Electricity Error Determination Results Transaction ID Error rate (%) Judgment results TX001 0.2506 normal TX002 0.5291 Overrun TX003 0.2457 normal TX004 1.5544 Overrun TX005 0.2584 normal As shown in Table 6, the error determination results clearly indicate which transactions have exceeded the error tolerance range, and the power transmission error determination records are obtained.

[0030] See also Figure 4 , the on-chain record verification module includes: The identification code parsing submodule extracts a unique transaction identification code from each transaction data item in the power transmission error determination record, deconstructs the identification code, parses the timestamp field into a time format, and constructs a unique transaction request index. It then sequentially searches the matching records, power generation node data records, and power consumption node data records, and groups the associated fields by node number, time, power value, and transaction number to generate a transaction binding field set. Based on each transaction data attached to the power transmission error determination record, the 64-bit unique identification code bound to the transaction field is extracted, and the identification code is divided into the first 8-bit organization identification segment, the middle 16-bit timestamp segment and the last 40-bit hash check segment. The character segmentation function is called to obtain the content of each part in turn, and the organization code "B202" is mapped to the sub-node number registered in the business management table. The timestamp "20240807102000" is converted to the system unified format "2024-08-07 10:20:00", and standardized into a timestamp index "1020" through a time format parsing function. The unique transaction request key "B202-1020" is generated by combining the organization code and the standard timestamp. This key is used as the search path in the blockchain ledger structure to locate the corresponding three types of data tables - the matching data table, the power generation node data table, and the power consumption node data table. The contents of the fields named "TxID", "GenNode", "LoadNode", "TimeStamp", and "PowerVolume" are extracted according to the field index. Through the field mapping operation, they are uniformly named "transaction number", "generator number", "power consumption number", "transaction time", and "power value". The three types of records are correspondingly collected using the transaction number as the unique identifier, organized into a structured set in a tabular manner, and stored in the intermediate cache queue, as shown in Table 7. Finally, the transaction binding field set is obtained.

[0031] Table 7 Blockchain transaction record field comparison table Transaction ID Institution Code Timestamp Matching time Power generation recording time Electricity consumption recording time Node numbers are consistent The power field is consistent TX1001 A101 20240807101530 1015 1015 1016 True True TX1002 B202 20240807102000 1020 1021 1020 True False TX1003 A101 20240807102545 1026 1026 1025 False False TX1004 C303 20240807103030 1030 1033 1030 True True TX1005 B202 20240807103515 1035 1035 1034 True True As shown in Table 7, the transaction number field is the basic index field, and the remaining fields are compared and associated based on this field to ensure that the field mapping relationships in different data tables are consistent.

[0032] The ledger record comparison submodule compares the three record fragments of each transaction in the transaction binding field set, checks the time fields of the records to see if there are any mismatches, determines if there is any offset between the matching time and the node-side record time, and cross-identifies the transaction number field and the node number field one by one to confirm whether the three records belong to the same transaction, thereby obtaining field consistency offset information; According to each transaction structure in the transaction binding field set, the time fields in the matching record, power generation record and power consumption record are compared one by one. By calculating the difference between the "power generation record time" and the "matching time", as well as the difference between the "power consumption record time" and the "matching time", it is judged whether the three are within the allowable time offset range. The offset tolerance is set to ±2 minutes, that is, if the difference in the time field exceeds 2 minutes, it is marked as time inconsistency. For example, the matching time of record TX1004 is 1030, and the power generation record time is 1033. The difference between the two is 3 minutes, which exceeds the set interval and is identified as time field offset. At the same time, the node number fields in the three records are cross-judged. If the generator number is consistent with the source node in the matching record, If there is a discrepancy between the number, the user number, and the target node number in the matching record, the field is marked as "node number inconsistency". Taking TX1003 as an example, the source node in the matching record is A1G9, and in the power generation record it is A1H1. This record field is marked as inconsistent. Then, it is determined whether there is an offset in the electricity field. If the electricity value in the power generation record is 1000kWh, and the electricity value in the electricity consumption record is 997kWh, it is recorded as inconsistent in the electricity field. The three fields are compared independently, and the results are recorded as "time field offset", "node field mismatch", and "electricity field inconsistency", forming the offset structure of the transaction at the field level, and the final output is field consistency offset information.

[0033] The record consistency judgment submodule determines whether the on-chain record consistency matching criteria are met based on the field conflict type and time offset identified in the field consistency offset information. If the node number, power field, and time field are all consistent, the transaction is marked as a normal record. If any field cannot be matched in the corresponding three records, the corresponding transaction is marked as a record abnormality, and the on-chain record consistency list is obtained; According to the field comparison recorded in the field consistency offset information, the comparison items of each transaction are judged. If any of the three items "time field offset", "node field mismatch" or "electricity field inconsistency" is marked as False, the status of the transaction is marked as "record abnormality", otherwise it is marked as "record normal". The marking basis is saved in the data record structure, with "transaction number" as the unique index field and "offset field identifier" as the data append item. A three-field data list of "transaction number-consistency status-offset type" is structured and generated. For example, transaction TX1003 is judged to be a record abnormality because both the node field and the electricity field are False. In transaction TX1005, all field comparisons are True, and it is judged to be a normal record. Finally, all transaction judgment results are summarized and output as an on-chain record consistency list.

[0034] See also Figure 5 ,The abnormal transaction cache module includes: The abnormal record screening submodule filters transaction numbers marked as abnormal based on each record in the on-chain record consistency list. It uses the power generation record, power consumption record, and matching record corresponding to each abnormal transaction number as the data source, constructs an abnormal transaction data unit, and obtains abnormal transaction screening data. Based on the record set in the on-chain record consistency list, the "record status" field is read sequentially. During the traversal process, it is determined whether it is "record abnormal". If it is abnormal, the corresponding transaction number field "TXID" is immediately extracted and recorded. At the same time, the node information, power data, and time information corresponding to the number are extracted from the transaction mapping table. The "generation node number", "power consumption node number", "generated power", "power consumption", "matching time", "generation time", and "power consumption time" fields are written into the buffer record set. Then, each successfully extracted abnormal record is subjected to field deduplication, format correction, and time standardization operations. For example, the status of transaction number TX1002 is abnormal. After extraction, it is found that the generated power is 1000kWh and the consumed power is 960kWh, with an energy difference of more than 40kWh. In addition, the matching time differs from the generation record time by 1 minute. The time field is marked as offset. The node numbers are all legal structures. The transaction mark is written into the abnormal buffer structure set, and the record number and original identification code are synchronized to construct the standard transaction cache entry parameters, as shown in Table 8, and finally the abnormal transaction screening data is obtained.

[0035] Table 8 Abnormal transaction cache data table Transaction ID Power generation node number Power node number Matching time Power generation time Electricity usage time Power generation (kWh) Electricity consumption (kWh) Identification code Abnormal Label TX1002 G-A102 L-B202 1020 1021 1020 1000 960 B20220250807102000… Power field offset + time field offset TX1003 G-A101 L-C202 1026 1026 1025 990 970 A10120250807102545… Power field offset + node field offset As shown in Table 8, all transactions marked as abnormal are uniformly classified into the cache preparation set according to the transaction number and corresponding fields, and the field placement and identification code parsing processing are completed.

[0036] The identification code cache writing submodule filters each transaction record in the data based on abnormal transactions, organizes the transaction field information bound in the same record according to field classification, sets a unified field sequence structure, converts the format according to the node number, power value and timestamp sequence, and writes it to the cache area according to the field classification method. At the same time, it marks the content of the difference relationship between each field and obtains the cache write field difference marking results; Call each transaction number in the abnormal transaction screening data and locate the corresponding 64-bit identification code field. Perform structural analysis on the identification code and decompose it into three components: the organization code field, the timestamp field, and the hash field. Set the organization code as the cache namespace prefix, and the timestamp field is used to generate the cache key suffix. Then rearrange the electricity field, node field, and time field according to the field standard template, map the power generation node and electricity consumption node numbers to standardized short code formats such as "GA01" and "LB02", retain two decimal places in the electricity field, and use a five-digit format in the 24-hour system. Then construct a field item value pair data structure. Before writing the data into the cache area, compare the original record and the matching record in the node field, time field, and electricity field one by one to see if there are any character inconsistencies or numerical differences. If a difference is identified, set it as the identification field, and write all identification results into the tag field of the cache data, bind it with the unique transaction identification code, and finally obtain the cache write field difference tag.

[0037] The abnormal label generation submodule converts various difference types into text labels based on the field difference record content in the cached field difference tagging results. If there are multiple field differences in a record, multiple labels are spliced ​​together to form the label content. The transaction number is used as the index field and associated with each cached data record to generate abnormal transaction cache data. Based on the identifier content recorded in the cache write field difference tag, the difference items of each record are read in sequence to determine whether the node numbers are inconsistent. If the character encoding between the power generation node number and the matching record source node number is different, it is marked as a "node field offset". The power field is judged. If the difference between the generated power and the consumed power exceeds 30kWh, it is classified as an "power field offset". The time field is judged. If the difference between the matching time and the power generation time or the consumed power time is greater than 1 minute, it is set as a "time field offset". Each type of offset label is generated independently. When multiple offsets exist, multiple labels are spliced ​​in the order of "power + time + node" to form a label combination. For example, in transaction TX1002, the power difference is 40kWh and the time difference is 1 minute. The spliced ​​label is "power field offset + time field offset". The generated label content is written together with the transaction number into the label record table to establish abnormal transaction cache data.

[0038] See also Figure 6 The full-link transaction statistics module includes: The matching order adjustment statistics submodule extracts and compares the ranking position of each node in different rounds based on the matching transaction records in the node matching priority list. It compares the priority rankings of the same node in adjacent rounds and determines whether there is any change in the order of the nodes in the time series. If there is a difference in the order, it is recorded as a sequence adjustment event. The event is bound to the node identifier and classified by node number. The number of times the node order changes during the entire matching process is recorded one by one to obtain the number of matching order adjustments. Based on each matching transaction record in the node matching priority list, the matching round number and participating node number fields indicated in the record are extracted one by one, and all transaction rounds are summarized by node number to form the matching ranking position sequence of each node in different time periods. Then, the ranking positions of two adjacent rounds in the ranking sequence of each node are compared, that is, the ranking position of the first round is taken. The round number minus the If the difference between the round ranking numbers is not zero, it means that the node's priority has moved forward or backward in the two rounds of matching, which is determined to be a sequence adjustment event. Then, the node number is used as the index to count the number of sequence adjustment events that have occurred, forming a record of the number of matching sequence adjustments for each node. For example, node N001 has two forward shifts and one backward shift in three consecutive matching rounds, with a total of three adjustment events recorded. The ranking of node N002 has not changed in each round, and the number of adjustment events is counted as 0. The numbers of all nodes and their corresponding sequence adjustment times are formed into a structured table to obtain the value of the matching sequence adjustment times.

[0039] The abnormal cache statistics submodule reads each transaction number recorded in the abnormal transaction cache data, extracts the node number of the power generator and the node number of the power consumer, counts the number of times each node is marked as an abnormal transaction in the cache data, and records the cumulative frequency of occurrence. The two-dimensional data are archived correspondingly at the node level, and the abnormal feature data items under the node dimension are established to obtain the node abnormal interference record; The complete transaction number set in the cache that has been recorded as abnormal transactions is called, and the power generation party node number and power consumption party node number fields registered in each transaction are extracted in turn. The occurrence frequency of the two fields is independently counted, and the abnormal transaction triggering frequency of all nodes in the entire abnormal cache is recorded as an event that the node is associated with an abnormal transaction. For example, in transaction EX002, the power generation party node is N003 and the power consumption party node is N004. In the record, N003 has triggered anomalies 3 times in total, and N004 has triggered anomalies 2 times in total. Then, the node sequence adjustment number record generated in the previous paragraph is called, and the node number is used as the key value for one-to-one matching to form a field pair structure at the node level. The "sequence adjustment number" and "abnormal transaction number" corresponding to each node are formed into a binary field combination, and the abnormal behavior feature matrix is ​​uniformly constructed to obtain the node abnormal interference record.

[0040] The link data summary submodule matches each node field recorded in the node abnormal interference record with the matching order adjustment times according to the node number, and uniformly classifies them into the statistical summary table. The node number is used as the main index field, and the corresponding matching order adjustment times, abnormal cache times, and node abnormal interference index are respectively used as data fields for structured registration. The summary obtains the blockchain new energy power transaction full link operation statistics table; According to the node abnormal interference records and the matching sequence adjustment times, the node number field is further extracted as the main index field, and the "sequence adjustment times" and "abnormal transaction times" fields are directly spliced ​​to form a total table structure. Nodes with missing data are uniformly padded with zeros and included in the full statistical list. On this basis, the node numbers are arranged in ascending order, and the numbers, sequence adjustment times, and abnormal transaction times of each node are uniformly integrated and recorded to form a structured data matrix with node unique index fields and statistical attribute fields. This is further used as the basic item for summarizing the monitoring results of the entire power trading process. An example is as follows: Table 9 Example table of node order adjustment and abnormal statistics Node number Number of sequence adjustments Abnormal transaction times N001 3 1 N002 0 0 N003 2 3 N004 1 2 N005 4 1 As shown in Table 9, the data matrix can directly reflect the total number of times each node participates in sorting adjustments and abnormal transactions during multiple rounds of matching transactions, and obtain the full-link operation statistics of blockchain new energy power transactions.

[0041] A method for new energy power trading considering blockchain, comprising the following steps: S1: Obtain the node identities of all power generators and power consumers in the current matching pool, calculate the cumulative number of transactions for each node, swap the order of nodes with cumulative transaction times higher than the benchmark value with nodes with cumulative transaction times lower than the benchmark value, record the node identification sequence, and generate a node matching priority list; S2: Based on the node matching priority list, calculate the absolute difference between the power output of the power generator and the power received by the power consumer for each transaction. If it is greater than the power transmission error threshold, mark it as exceeding the limit and obtain the power transmission error judgment record; S3: Based on the power transmission error determination record, read each transaction identification code, search the corresponding matching, power generation and reception records in the blockchain ledger, determine whether the data is consistent within the transaction identification code range, mark inconsistent transactions as record anomalies, and generate an on-chain record consistency list; S4: Based on the on-chain record consistency list, filter out transactions marked as record abnormalities, store the corresponding transaction data in the cache according to the transaction identification code, generate an abnormality reason tag, and generate abnormal transaction cache data; S5: Based on the abnormal transaction cache data and the node matching priority list, the number of matching order adjustments and abnormal transaction caches that occurred in all nodes during the transaction process are counted to form full-link statistical information and obtain the full-link operation statistics table of blockchain new energy power transactions.

[0042] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A system for new energy power trading based on blockchain, characterized in that: The system comprises: The node matching order adjustment module obtains the node identities of all power generators and power consumers in the current matching pool, calculates the cumulative number of transactions for each node, swaps the order of nodes with cumulative transaction times higher than the transaction frequency benchmark value with nodes with cumulative transaction times lower than the transaction frequency benchmark value, and records the node identification sequence to generate a node matching priority list; The power transmission error determination module calculates the error rate based on the node matching priority list and the power output of the power generator and the power received by the power consumer for each transaction. If the error rate is greater than the power transmission error threshold, it is marked as exceeding the limit, and a power transmission error determination record is obtained; The on-chain record verification module reads each transaction identification code based on the power transmission error determination record, retrieves the corresponding matching, power generation and reception records in the blockchain ledger, determines whether the data is consistent within the transaction identification code range, marks inconsistent transactions as record anomalies, and generates an on-chain record consistency list; The abnormal transaction cache module filters transactions marked as record abnormalities based on the on-chain record consistency list, stores the corresponding transaction data into the cache area according to the transaction identification code, generates an abnormal reason tag, and generates abnormal transaction cache data.

2. The system for new energy power trading based on blockchain according to claim 1, characterized in that: The node matching priority list includes a node identity sorting list, an exchange order record sequence, and a priority identification tag; the power transmission error judgment record includes an error value tag, an error exceeding limit tag, and a threshold comparison result; the on-chain record consistency list includes a transaction identification code mapping table, a data consistency tag, and a data verification status; the abnormal transaction cache data includes an abnormal identification code list, a cache data structure, and an abnormal cause tag.

3. The system for new energy power trading based on blockchain according to claim 1, characterized in that: The node matching order adjustment module includes: The node identity recognition submodule extracts and records the node identifiers in each node identity information table based on all registered power generation nodes and power consumption nodes in the current matching pool. Combined with the historical transaction data recorded in the blockchain ledger, the node identity identifiers are matched with the buyer and seller information in the transaction records. Nodes with transaction records are selected as active nodes to generate an active node identifier sequence. The matching frequency calculation submodule reads the number of occurrences of the corresponding node in all transaction records in the blockchain ledger based on the unique identifier of each node in the active node identifier sequence, takes the cumulative number of transactions for each node as the transaction frequency value, and calculates the frequency deviation value of each node in combination with the transaction frequency benchmark value, and sorts and establishes a node frequency deviation list; The priority queue generation submodule compares the frequency deviation value corresponding to each node in the node frequency deviation list with the original queue position, exchanges the node order and updates the adjusted positions of all nodes, and records the adjusted node order in the blockchain account book to obtain the node matching priority list.

4. The system for new energy power trading based on blockchain according to claim 1, characterized in that: The power transmission error determination module includes: The power record extraction submodule reads each completed matching transaction data in the blockchain ledger based on the node matching priority list, extracts the recorded generator node number and consumer node number, retrieves the power output record of the generator node at the transaction time and the power reception record of the consumer node at the same time in each transaction, and identifies each valid transaction with a number to generate a matching transaction power data set; The error difference calculation submodule calculates the relative error percentage of power transmission in each transaction based on the power output value of the power generator and the power received value of the power consumer recorded in the matched transaction power data set, and obtains a transaction power error rate list; The error result judgment submodule compares all transaction error rates with the power transmission error threshold based on the error rate of each transaction record in the transaction power error rate list, identifies the transaction numbers with error rates greater than the power transmission error threshold, marks the status as out of limit, and marks the rest as normal, and integrates them to obtain the power transmission error judgment record.

5. The system for new energy power trading based on blockchain according to claim 1, characterized in that: The on-chain record verification module includes: The identification code parsing submodule extracts a unique transaction identification code based on each transaction data attached to the power transmission error determination record, decomposes the identification code, parses the timestamp field into a time format, and constructs a unique transaction request index. It then sequentially searches the matching records, power generation node data records, and power consumption node data records, and groups the associated fields by node number, time, power value, and transaction number to generate a transaction binding field set. The ledger record comparison submodule compares the three record fragments of each transaction in the transaction binding field set, checks the time fields of the records in turn to see if there is any mismatch, determines if there is any offset between the matching time and the node-side record time, and cross-identifies the transaction number field and the node number field one by one to confirm whether the three records belong to the same transaction, thereby obtaining field consistency offset information; The record consistency judgment submodule determines whether the on-chain record consistency matching criteria are met based on the field conflict type and time offset identified in the field consistency offset information. If the node number, power field and time field are all consistent, the transaction is marked as a normal record. If any field cannot be matched in the corresponding three records, the corresponding transaction is marked as a record abnormality, and the on-chain record consistency list is obtained.

6. The system for new energy power trading based on blockchain according to claim 1, characterized in that: The abnormal transaction cache module includes: The abnormal record screening submodule screens transaction numbers marked as abnormal based on each record in the on-chain record consistency list, uses the power generation record, power consumption record, and matching record corresponding to each abnormal transaction number as the data source, constructs an abnormal transaction data unit, and obtains abnormal transaction screening data; The identification code cache writing submodule filters each transaction record in the abnormal transaction data, organizes the transaction field information bound in the same record according to the field classification, sets a unified field sequence structure, converts the format according to the node number, power value and timestamp sequence, and writes the data into the cache area according to the field classification method. At the same time, the difference relationship between the fields is content-marked to obtain the cache write field difference marking result; The abnormal label generation submodule converts various difference types into text labels according to the field difference record content in the cache write field difference tagging result. If there are multiple field differences in a record, multiple labels are spliced ​​and combined to form label content, and the transaction number is used as the index field, associated with each cache data record for storage, to generate abnormal transaction cache data.

7. The system for new energy power trading based on blockchain according to claim 1, characterized in that: The system further comprises: The full-link transaction statistics module counts the number of matching order adjustments and the number of abnormal transaction caches that occur in all nodes during the transaction process based on the abnormal transaction cache data and the node matching priority list, forms full-link statistical information, and obtains a full-link operation statistics table for blockchain new energy power transactions; The blockchain new energy power transaction full-link operation statistics table includes the matching order adjustment frequency, the number of abnormal transaction caches, and node operation status indicators.

8. The system for new energy power trading based on blockchain according to claim 7, characterized in that: The transaction full-link statistics module includes: The matching order adjustment statistics submodule extracts and compares the ranking position of each node in different rounds based on the matching transaction records in the node matching priority list, compares the priority positions of the same node in adjacent rounds by number difference, and determines whether the position in the time series has changed. If there is a difference in position, it is recorded as a ranking adjustment event, and the event is bound to the node identifier and classified by node number. The number of times the node order changes during the whole matching process is recorded one by one to obtain the number of matching order adjustments; The abnormal cache statistics submodule reads each transaction number recorded in the abnormal transaction cache data, extracts the node number of the power generator and the node number of the power consumer, counts the number of times each node is marked as an abnormal transaction in the cache data, and records the cumulative frequency of occurrence. The two-dimensional data are archived correspondingly at the node level, and the abnormal feature data items under the node dimension are established to obtain the node abnormal interference record; The link data summary submodule matches each node field recorded in the node abnormal interference record and the matching order adjustment number value one by one according to the node number, and uniformly classifies them into the statistical summary table. The node number is used as the main index field, and the corresponding matching order adjustment number, abnormal cache number and node abnormal interference index are respectively registered as data fields for structured registration, and the blockchain new energy power transaction full-link operation statistics table is summarized.

9. A method for new energy power trading considering blockchain, characterized in that The method is used to implement the system for new energy power trading considering blockchain as described in any one of claims 1 to 8, comprising the following steps: S1: Obtain the node identities of all power generators and power consumers in the current matching pool, calculate the cumulative number of transactions for each node, swap the order of nodes with cumulative transaction times higher than the benchmark value with nodes with cumulative transaction times lower than the benchmark value, record the node identification sequence, and generate a node matching priority list; S2: Based on the node matching priority list, the error rate is calculated based on the power output of the power generator and the power received by the power consumer for each transaction. If it is greater than the power transmission error threshold, it is marked as exceeding the limit, and a power transmission error determination record is obtained; S3: Based on the power transmission error determination record, read each transaction identification code, search the corresponding matching, power generation and reception records in the blockchain ledger, determine whether the data is consistent within the transaction identification code range, mark inconsistent transactions as record anomalies, and generate an on-chain record consistency list; S4: Based on the on-chain record consistency list, filter out transactions marked as record abnormalities, store the corresponding transaction data in a cache area according to the transaction identification code, generate an abnormality reason tag, and generate abnormal transaction cache data; S5: Based on the abnormal transaction cache data and the node matching priority list, the number of matching order adjustments and abnormal transaction cache times that occur in all nodes during the transaction process are counted to form full-link statistical information and obtain a blockchain new energy power transaction full-link operation statistics table.

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