A power transaction data processing method of an energy storage power station

Abstract

The application discloses a kind of energy storage power station electric power transaction data processing method, specifically relates to electric power transaction data processing technical field, the present application is extracted by multiple transaction data task identification, time boundary and upstream and downstream correlation attribute, summarizes transaction period, dispatching instruction change, time node division verification section such as metering settlement period, each link data is associated according to business processing order to form the transaction processing chain of section and constructs section verification object, realizes the time range uniformity of whole link transaction data, task attribution alignment and section accurate anchoring, effectively solve the problem that different link data is scattered and fragmented, deviation positioning is fuzzy, and abnormal section is difficult to accurately lock.

Description

Technical Field

[0001] This invention relates to the field of power transaction data processing technology, and more specifically, to a method for processing power transaction data of an energy storage power station. Background Technology

[0002] With the continuous improvement of my country's power market system, energy storage power stations, with their rapid response and bidirectional regulation capabilities, have been widely involved in various types of power market businesses, including power energy trading and ancillary service trading. The entire trading process generates data from multiple stages, such as declaration, clearing, dispatch, execution, metering, and settlement. The data sources are scattered, the time sequence is complex, and the business correlation is high. The consistency, accuracy, and traceability of power trading data are important foundations for ensuring fair transaction settlement, protecting the rights and interests of market participants, and improving the operational efficiency of energy storage power stations. However, existing methods for processing electricity trading data in energy storage power stations still have the following problems: First, existing data processing mainly focuses on independent verification of a single transaction link, without establishing a chain-like data association mechanism covering the entire transaction process, and without completing a unified segmentation based on time boundaries and business logic. The time caliber and task attribution of data in different links lack alignment standards, resulting in vague deviation positioning and making it impossible to accurately pinpoint the specific segment and related link where the anomaly occurred. Secondly, the transaction data anomaly tracing generally adopts a fixed-depth full-link backtracking mode, without dynamically matching the tracing level according to the significance of the transaction deviation. When facing minor deviations, full tracing can easily lead to redundant computing resources, while when facing hidden deviations, the root cause may be missed due to insufficient tracing depth. Processing efficiency and tracing accuracy cannot be balanced. Furthermore, the abnormal data correction and quality control system is rather crude. On the one hand, it cannot effectively distinguish between data entry errors and actual operational deviations, which poses a risk of blindly correcting the original data and concealing the true operational status. On the other hand, it lacks a multi-dimensional quantitative data quality evaluation and grading mechanism, making it difficult to implement differentiated follow-up actions based on the data quality level and failing to provide reliable support for data application throughout the entire transaction process. Therefore, a method for processing electricity trading data in energy storage power stations is proposed to solve the above problems. Summary of the Invention

[0003] To overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide a method for processing power trading data of an energy storage power station.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for processing power trading data in an energy storage power station includes the following steps: Acquire the declaration data, clearing data, dispatch data, execution data, metering data and settlement data generated during the power trading process of energy storage power stations, construct the transaction processing chain according to the transaction tasks and time relationships, and form the segment verification objects corresponding to each verification segment; Downstream reference data is generated based on the upstream data nodes and corresponding processing rules of adjacent transaction links in the segment verification object. The downstream reference data is compared with the downstream actual data to determine the segment deviation characteristic data. The segment deviation characteristic data is comprehensively analyzed to obtain the deviation significance value, and the reverse traceability level is matched according to the deviation significance value. Among them, the segment deviation characteristic data includes relative quantity deviation, misalignment duration percentage, abnormal link percentage, and deviation duration percentage; Reverse verification is performed along the transaction processing chain according to the reverse tracing level to determine candidate anomaly source nodes, and the anomaly source data is determined through forward recalculation verification. The abnormal source data and its associated downstream transaction data are corrected. A data quality score is obtained based on the transaction data quality information of the corrected transaction data. The transaction data quality level is matched, and the corresponding graded handling is performed.

[0005] Specifically, the process for forming the verification object for the section is as follows: Extract the transaction task identifier, data source identifier, data formation time, transaction period, transaction direction, transaction power, transaction volume, data change time, and upstream reference identifier from various types of electricity transaction data; Verification sections are divided based on the transaction start time, transaction end time, time of scheduling instruction change, time of execution status change, metering cycle boundary, and settlement cycle boundary. Data belonging to the same transaction task are linked in the order of declaration, clearing, scheduling, execution, measurement and settlement to form a transaction processing chain, and adjacent transaction links are set up according to the data node order to form segment verification objects.

[0006] Specifically, the process for determining the relative quantity deviation is as follows: For the j-th adjacent transaction segment within the i-th verification segment, compare the downstream reference power with the downstream actual power, and the downstream reference electricity with the downstream actual electricity, and determine the relative power deviation using the following formula. and relative power deviation : , ; in, , These are the downstream reference power and the downstream actual power, respectively. , These are the downstream reference power volume and the downstream actual power volume, respectively. , Determined according to the minimum effective power resolution and the minimum effective charge resolution respectively; The relative power deviation and relative energy deviation are weighted based on comparable data items, and the formula is used. Obtain the relative quantity deviation ; in, and Power and energy consumption can be compared and identified; and The deviation result is used when there is only one comparable item, corresponding to the weight.

[0007] Specifically, the process for identifying abnormal processes is as follows: The ratio of the non-overlapping duration of the downstream reference time coverage area to the total duration of the verification segment of the downstream actual time coverage area is determined as the misalignment duration percentage. The basic deviation value is obtained by weighting the relative quantity deviation and the proportion of misalignment time. When the basic deviation value is greater than or equal to the basic deviation threshold, the corresponding adjacent transaction link is identified as an abnormal link. When the actual downstream transaction direction is inconsistent with the downstream reference transaction direction, the corresponding adjacent transaction link will be identified as the direction abnormal link.

[0008] Specifically, the process of obtaining the significant value of the deviation is as follows: relative quantity deviation of the section and the proportion of segment misalignment time The maximum value of the corresponding parameter within the same verification segment is used to determine the percentage of abnormal steps. The percentage of duration of segment deviation is determined based on the number of abnormal steps and the total number of adjacent transaction steps pending verification. Determined based on the duration of the abnormal detection segment; The formulas are used to analyze the relative quantity deviation of sections, the proportion of section misalignment time, the proportion of abnormal links, and the proportion of section deviation duration. Weighting is performed to obtain the significance value of the deviation. ;in , , , These are the corresponding preset weighting factors.

[0009] Specifically, the process for determining the reverse traceability level is as follows: Three sets of significant deviation value intervals are pre-defined, and each set of significant deviation value intervals corresponds to a reverse traceability level. The significant deviation value is matched with the corresponding significant deviation value interval to obtain the reverse traceability level of the corresponding verification segment. The reverse traceability level includes the first level, the second level and the third level, and the higher the significant deviation value, the higher the matched reverse traceability level.

[0010] Specifically, corrections will be made to the abnormal source data and its associated downstream transaction data, including: The set of objects to be corrected is determined based on the transaction task identifier, the verification section identifier, and the upstream and downstream reference relationships. Based on the anomaly type, the correction object set is remapped for time boundaries, transaction volume is redistributed, upstream and downstream reference relationships are rebuilt, or backup metering data is replaced, and correction records are generated. When the actual execution result deviates from the scheduling target and the original execution data is true and valid, the original execution data is retained, and an execution deviation identifier and deviation description are added.

[0011] Specifically, the process of analyzing transaction data quality information is as follows: The number of missing nodes is counted, and the ratio is calculated to the total number of necessary data nodes to obtain the transaction chain missing quantity. ; When a verification item meets the corresponding processing rules, its consistency identifier is recorded as 1; otherwise, it is recorded as 0. If the corresponding transaction process does not generate this type of data or the verification item is not applicable, it is not included in the calculation. The consistency identifiers of all applicable verification items are weighted and averaged to obtain the link consistency value. ; Read the expected formation time, expected update time, and actual formation or update time of each existing data node. When the actual formation or update time is later than the corresponding processing time limit, record the data node as a timeout node. Count the number of timeout nodes and calculate the ratio with the number of existing data nodes to obtain the data timeout quantity. ; Each existing data node is checked to ensure it has a clear data source, creation time, transaction task identifier, and upstream / downstream connection basis. Each verifiable item is recorded as 1, and each unverifiable item as 0. All verification results are averaged to obtain the source verification value. ; After normalizing the transaction chain missing amount, chain consistency value, data timeout amount, and source verification value, the formula is used. A weighted calculation is performed to obtain the data quality score, where , , , These are pre-set weighting factors.

[0012] Specifically, the process for determining the quality level of transaction data is as follows: Three sets of data quality score intervals are pre-defined, and each set of data quality score intervals corresponds to a set of transaction data quality levels. The data quality scores are matched with the corresponding data quality score intervals to determine the corresponding transaction data quality level.

[0013] The technical effects and advantages of this invention are as follows: This invention extracts task identifiers, time boundaries, and upstream and downstream association attributes from multiple types of transaction data, summarizes time nodes such as transaction cycle, scheduling instruction changes, and metering and settlement cycle to divide verification segments, associates data from each link according to the business processing order to form a segmented transaction processing chain, and constructs segment verification objects. This achieves unified time caliber, aligned task affiliation, and precise segmented anchoring of transaction data across the entire chain, effectively solving the problems of scattered and fragmented data in different links, ambiguous deviation positioning, and difficulty in accurately locking abnormal segments. This invention extracts segment deviation features from four dimensions: relative quantity deviation, time misalignment ratio, coverage of abnormal links, and duration of deviation. It then calculates the significant value of the deviation by weighting and matching it with a three-level reverse tracing depth. Combined with data lineage tracing and forward recalculation verification, it verifies layer by layer and locates the source of the anomaly. This achieves dynamic adaptation between tracing depth and deviation severity, which avoids the computational redundancy caused by full-link backtracking under slight deviations and ensures the accuracy and completeness of root cause location under significant deviations. This invention differentiates between data entry errors and actual operational deviations by employing differentiated correction rules, simultaneously retaining complete correction trajectories and supporting evidence, and quantifying data quality scores from four dimensions: transaction chain integrity, link consistency, data timeliness, and source verifiability. It then matches three quality levels for tiered handling, achieving precise correction of abnormal data and systematic control of transaction data quality. This avoids the risk of erroneous modification of actual operational deviations and provides clear and reliable data application basis for subsequent transaction execution, measurement, and settlement processes. Attached Figure Description

[0014] Figure 1 This is a flowchart of a power trading data processing method for an energy storage power station according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0016] like Figure 1 As shown, the steps of a power trading data processing method for an energy storage power station are as follows: Construct a transaction processing chain and form segment verification objects: Obtain the declaration data, clearing data, scheduling data, execution data, metering data and settlement data generated during the power transaction process of the energy storage power station, perform time alignment and transaction task association on data from different sources, construct a transaction processing chain, and form segment verification objects corresponding to each verification segment according to the data node order in the transaction processing chain; Specifically: J101: Extract the transaction task identifier, data source identifier, data formation time, transaction period, transaction direction, transaction power, transaction volume, data change time, and upstream reference identifier from various types of power transaction data, and group data belonging to the same transaction task into the same transaction data set; J102: Summarize the transaction start time, transaction end time, scheduling instruction change time, execution status change time, metering cycle boundary and settlement cycle boundary, arrange them in chronological order, and determine the time range between two adjacent time boundaries as the verification segment; The verification section refers to the time range within which the transaction task identifier, transaction direction, effective dispatch instruction, execution status, and metering and settlement boundaries remain unchanged; the actual power sampling value of the energy storage power station can change with the operating status within this verification section. J103: Allocate each transaction data to the corresponding verification segment according to its time coverage, and associate them according to the processing order of declaration, clearing, scheduling, execution, measurement and settlement to form a transaction processing chain corresponding to each verification segment; Among them, the transaction processing chain refers to the data association sequence formed by the same transaction task at different processing stages; J104: According to the data node order in the transaction processing chain, establish a set of adjacent transaction links corresponding to each verification segment, and record the upstream data nodes, downstream data nodes and their data associations corresponding to each adjacent transaction link to form segment verification objects; Among them, the objects of segment verification include adjacent transaction links corresponding to declaration and clearing, clearing and scheduling, scheduling and execution, execution and measurement, and measurement and settlement; Determine the significance of the deviation and match the reverse tracing level: compare adjacent transaction links in the transaction processing chain within each verification segment, and extract the segment deviation feature data of the corresponding verification segment; A comprehensive analysis of the deviation characteristic data of the sections is conducted to obtain the significant deviation value of the corresponding verification section, and the corresponding reverse traceability level is matched according to the significant deviation value. Among them, the segment deviation characteristic data includes relative quantity deviation, misalignment duration percentage, abnormal link percentage, and deviation duration percentage; Specifically: J201 determines the transaction volume values ​​of adjacent transaction stages and segments. The declaration, clearing, scheduling, execution, measurement and settlement are arranged in the order of transaction processing, and any two consecutive processing steps are defined as an adjacent transaction step; The adjacent transaction stages include declaration and clearing, clearing and scheduling, scheduling and execution, execution and measurement, and measurement and settlement. Record the verification section number as The adjacent transaction links within the verification section will be numbered as follows: In adjacent transaction stages, the data node formed earlier is recorded as the upstream data node, and the data node formed later is recorded as the downstream data node; Regarding the first Within the verification zone, the first For each adjacent transaction link, the segment power, segment electricity, effective time coverage and standardized transaction direction corresponding to the upstream data node and the downstream data node are determined respectively; Among them, the discharge direction, electricity sales direction and upward adjustment direction corresponding to the energy storage power station outputting electricity to the grid are taken as the first standard direction, and the charging direction, electricity purchase direction and downward adjustment direction corresponding to the energy storage power station absorbing electricity from the grid are taken as the second standard direction, and the transaction directions recorded in different transaction links are converted into the corresponding standard directions. Based on the effective processing rules of upstream data nodes and corresponding adjacent transaction links, a downstream reference transaction direction is generated; when the actual downstream transaction direction is inconsistent with the downstream reference transaction direction, the corresponding adjacent transaction link is marked as a direction abnormal link. When a data node contains multiple power sampling values ​​within a verification segment, the average power of the segment is obtained by weighting the values ​​according to the duration of each sampling value. The power consumption of a given segment is prioritized for reading, recording, and directly allocating to the verification segment by the data node. When the data node does not directly record the segment power, the segment power is determined based on the power data and its duration within the verification segment. Based on the effective processing rules of upstream data nodes and corresponding adjacent transaction links, downstream reference power, downstream reference electricity, and downstream reference time coverage are generated; the actual downstream data is compared with the downstream reference data to determine the relative quantity deviation and misalignment duration ratio. Among them, declaration and clearing: generating clearing reference data based on declaration data and clearing allocation records; Clearing and Scheduling: Generate scheduling reference data based on the clearing results and effective scheduling adjustment records; Scheduling and Execution: Generate execution reference data based on the effective scheduling instructions; Execution and Measurement: Generate measurement reference data based on actual execution power and duration; Measurement and Settlement: Generate settlement reference data based on valid measurement data and settlement rules; In addition, the power, energy, and time coverage range generated based on the upstream data nodes and corresponding processing rules will be determined as the downstream reference power, downstream reference energy, and downstream reference time coverage range, respectively. The actual power, electricity, and time coverage range recorded or collected by the downstream data nodes are respectively defined as the downstream actual power, downstream actual electricity, and downstream actual time coverage range. Subsequent relative quantity deviations and misalignment duration percentages are determined by comparing downstream reference data with downstream actual data, without directly comparing the original values ​​of upstream data nodes with downstream data nodes; J202 determines the relative quantity deviation For the j-th adjacent transaction segment within the i-th verification zone, compare the downstream reference power with the downstream actual power, and the downstream reference electricity with the downstream actual electricity, and determine the relative power deviation using the following formula. and relative power deviation : , ;in, , These are the downstream reference power and the downstream actual power, respectively. , These are the downstream reference power volume and the downstream actual power volume, respectively. , Determined according to the minimum effective power resolution and the minimum effective charge resolution respectively; The relative power deviation and relative energy deviation are weighted based on comparable data items, and the formula is used. Obtain the relative quantity deviation ; in, and This is a comparative indicator for power and energy; it is set to 1 if comparable, otherwise to 0. and To correspond to the weight, the deviation result is used when only one item is comparable; when neither item is comparable and quantitative data should be generated according to the transaction rules, Qᵢⱼ is recorded as 1. Additional explanation: The power comparability indicator is used to indicate whether the current adjacent transaction links have downstream reference power and downstream actual power that are consistent and comparable to each other; If both power data exist and the corresponding transaction task, verification segment, unit of measurement and data type are consistent, the value is 1; otherwise, the value is 0. The electricity comparability indicator is used to indicate whether there are downstream reference electricity and downstream actual electricity that are consistent in scope and can be compared with each other in the current adjacent transaction links; If both electricity data exist and the corresponding transaction task, verification section, metering unit and data type are consistent, the value is 1; otherwise, the value is 0. J203 determines the percentage of misalignment time. Compare the downstream reference time coverage with the downstream actual time coverage. Identify the non-overlapping portions within the current verification segment as time misalignment portions, and accumulate their durations to obtain the misalignment duration. Then, combine the misalignment duration with the total duration of the i-th verification segment using the formula... Obtain the percentage of misalignment time ; J204 determines the basic deviation value and abnormal status of adjacent transaction links. The basic deviation value of each adjacent transaction stage is obtained by multiplying the relative quantity deviation and the proportion of misalignment duration by a pre-set weighting factor and summing the results. The basic deviation value is then compared with a pre-set basic deviation threshold. If the basic deviation value is greater than or equal to the threshold, it indicates that the corresponding adjacent transaction stage has reached an abnormal condition; otherwise, it has not reached an abnormal condition. Additional notes: The basic deviation threshold is determined based on historical transaction data that has been manually confirmed to be free of anomalies, specifically taking the upper limit of the historical basic deviation value of the corresponding adjacent transaction stage; When historical data is insufficient, preset the allowable deviation, measurement resolution, and time recording accuracy according to the trading platform; when the data conversion relationship is different in different adjacent trading stages, set the corresponding basic deviation threshold respectively. When there is a directional anomaly in a corresponding adjacent transaction segment, the adjacent transaction segment is identified as an abnormal segment regardless of whether the basic deviation value reaches the basic deviation threshold; when there is no directional anomaly, the abnormal status is determined based on the comparison result between the basic deviation value and the basic deviation threshold. J205 Formation Section Relative Quantity Deviation, Section Misalignment Duration Percentage, and Abnormal Link Percentage The maximum relative quantity deviation among adjacent transaction stages within the same verification segment is selected as the segment's relative quantity deviation. ; The maximum percentage of misalignment time among adjacent transaction stages within the same verification segment is selected as the segment misalignment time percentage. ; The number of abnormal steps within the same verification segment is counted, and the percentage of abnormal steps is calculated by comparing the number of abnormal steps with the total number of adjacent transaction steps to be verified. ; Additional explanation: The maximum value is used to form the relative quantity deviation and the proportion of segment misalignment time in the current verification segment. This is used to retain the most obvious quantity deviation and time misalignment in the current verification segment and to avoid obvious anomalies being diluted by the average results of other normal steps. The proportion of abnormal steps is used to reflect the scope of the deviation's impact on the transaction processing chain. J206 determines the percentage of duration of deviation. The current verification section is divided into multiple detection segments according to the power sampling time, the data status change time, or the preset detection period; the basic deviation value of adjacent transaction links in each detection segment is calculated, and the detection segment with the basic deviation value greater than or equal to the basic deviation threshold or with directional abnormality is identified as an abnormal detection segment; For each adjacent transaction segment, the duration of its anomaly detection segment is accumulated and compared with the total duration of the current verification segment to obtain the corresponding deviation duration percentage. The maximum value among the deviation duration percentages corresponding to each adjacent transaction segment is selected as the segment deviation duration percentage of the current verification segment. ; J207 Determines the Significance Value of the Deviation The formulas are used to analyze the relative quantity deviation of sections, the proportion of section misalignment time, the proportion of abnormal links, and the proportion of section deviation duration. Weighting is performed to obtain the significance value of the deviation. ;in , , , These are the corresponding preset weighting factors; J208 Matching Reverse Traceability Level Three sets of significant deviation value intervals are pre-defined, and each set of significant deviation value intervals corresponds to a reverse traceability level. The significant deviation value is matched with the corresponding significant deviation value interval to obtain the reverse traceability level of the corresponding verification segment. The reverse traceability level includes the first level, the second level and the third level, and the higher the significant deviation value, the higher the matched reverse traceability level. Additional explanation: Level 1 is used for reverse verification of the settlement, measurement, and execution stages at the end of the transaction processing chain; Level 2 is used to trace back to the scheduling and clearing stages based on Level 1; Level 3 is used to trace back to the reporting stage based on Level 2, and to conduct a complete verification of the transaction processing chain. The abnormal source data is located according to the reverse traceability level: a reverse traceability instruction is generated according to the reverse traceability level corresponding to the verification section. Through data lineage tracing technology, starting from the settlement data or the currently available end transaction data, the generation relationship, time correspondence, transaction direction and upstream and downstream reference relationship of each data node are verified in the reverse direction of the transaction processing chain. The first level verifies the supporting relationships between settlement and measurement, and between measurement and execution; the second level, based on this, further verifies the supporting relationships between execution and scheduling, and between scheduling and clearing; the third level further verifies the supporting relationships between clearing and declaration. Each level can locate the source of abnormal data within its corresponding traceability scope. When the deviation between the downstream reference data and the downstream actual data does not reach the abnormal conditions, and the transaction task, time range and transaction direction are all matched, it is determined that the downstream data node has effective upstream support. In other cases, the node is determined to lack effective upstream support, and the corresponding data node is listed as a candidate source node for anomalies. Additional explanation: For candidate anomaly source nodes, a forward recalculation verification is performed: Reference data for the node is regenerated using valid upstream data and corresponding processing rules, and subsequent reference results are regenerated forward along the transaction processing chain. If the basic deviation values ​​of all adjacent downstream transaction stages after recalculation are below the corresponding threshold and there is no directional anomaly, the candidate node is identified as the source of the anomaly; otherwise, verification continues upstream. If the source of the anomaly is not determined at Level 1, the traceability level will be upgraded to Level 2; if the source of the anomaly is not determined at Level 2, the level will be upgraded to Level 3; if the source of the anomaly is still not determined at Level 3, the transaction data, traceability path, candidate nodes and recalculation results will be sent to the management personnel terminal for manual verification. Correcting abnormal source data and determining transaction data quality level: Based on the abnormal source data and its impact range, data cleaning and rule correction techniques are used to correct the abnormal source data and its associated downstream transaction data, and the data before correction, the basis for correction, the data after correction, and the affected verification sections are recorded; transaction data quality information corresponding to the corrected transaction data is extracted, and a data quality score is obtained after comprehensive analysis of the transaction data quality information; the corresponding transaction data quality level is matched according to the data quality score, and corresponding actions are taken based on different transaction data quality levels; Among them, transaction data quality information includes the amount of missing transaction chains, chain consistency value, data timeout amount and source verification value, and transaction data quality level includes normal use level, restricted use level and low quality level; Specifically: J401 Determines the range of anomaly correction. Read the abnormal source data, and based on the transaction task identifier, verification section identifier, and upstream and downstream reference relationships, determine the downstream data nodes that are directly referenced or calculated by it, and form a set of correction objects; In addition, for cases where the execution result deviates from the transaction instruction but the original record is true and valid, the original transaction value will not be modified. Instead, the corresponding data node will be marked as the execution deviation node to avoid misjudging the actual execution deviation as a data error. J402 Corrected Transaction Data Based on the anomaly type, the corresponding correction rule is invoked to correct the anomaly source data and its directly affected downstream data, and a correction record is generated. Additional notes: The correction method should include at least the following: When the time boundary corresponding to the transaction data is incorrect, the transaction data is remapped to the correct verification segment based on the transaction task identifier, the effective time, and the time coverage of adjacent data nodes. When the aggregated transaction volume is incorrectly allocated to different verification segments, the transaction volume is redistributed based on valid scheduling data, execution data, or measurement data, while maintaining the total transaction volume before and after the correction. When the upstream and downstream data reference relationships are incorrect, the data reference relationships are reconstructed based on the transaction task identifier, data node type, and time overlap. When measurement data is missing or distorted and there is verified backup measurement data, the valid backup measurement data shall be used to replace it, while the original measurement data shall be retained. When the deviation is caused by the actual execution result failing to meet the scheduling target, the execution data is not replaced, but an execution deviation identifier and deviation description are added to the corresponding data node; The correction record should include at least the data before correction, the anomaly type, the basis for correction, the correction rules, the data after correction, the correction time, the affected data nodes, and the affected verification sections. J403 constructs a revised transaction data verification object. Based on the corresponding trading instruments and trading tasks, the pre-configured list of necessary data nodes, the list of adjacent trading links, the data processing time limit, and the data source verification items are read, and the corrected declaration, clearing, scheduling, execution, measurement, and settlement data are mapped to the corresponding lists to form quality verification objects; Additional explanations: The list of necessary data nodes is used to determine the data nodes that the current transaction task should have; the list of adjacent transaction links is used to determine the combination of data nodes that need to be verified for consistency; the data processing time limit is used to determine whether the data has been generated or updated beyond the time limit; the data source verification items include the data source, generation time, transaction task identifier, and upstream and downstream correlation basis. J404 determines the missing amount in the transaction chain. Match the list of necessary data nodes with the corrected transaction data nodes one by one. If a necessary data node does not exist, the corresponding record is empty, or the corresponding record cannot be used for subsequent processing due to format errors or verification failures, the data node is recorded as a missing node. The number of missing nodes is counted, and the ratio is calculated to the total number of necessary data nodes to obtain the transaction chain missing quantity. ; J405 determines link consistency value Verification is performed according to the transaction processing chain sequence. For each group of adjacent transaction links, the transaction direction, transaction time period, power, electricity and transaction status are verified to ensure that they comply with the corresponding transaction processing rules. When a verification item meets the corresponding processing rules, its consistency identifier is recorded as 1; otherwise, it is recorded as 0. If the corresponding transaction process does not generate this type of data or the verification item is not applicable, it is not included in the calculation. The consistency identifiers of all applicable verification items are weighted and averaged to obtain the link consistency value. ; Additional notes: When there are no applicable verification items, the corresponding adjacent transaction links will be identified as unverifiable links, and their consistency identifier will be recorded as 0. J406 determines data timeout amount and source verification value. Read the expected formation time, expected update time, and actual formation or update time of each existing data node. When the actual formation or update time is later than the corresponding processing time limit, record the data node as a timeout node. Count the number of timeout nodes and calculate the ratio with the number of existing data nodes to obtain the data timeout quantity. ; Each existing data node is checked to ensure it has a clear data source, creation time, transaction task identifier, and upstream / downstream connection basis. Each verifiable item is recorded as 1, and each unverifiable item as 0. All verification results are averaged to obtain the source verification value. ; Additional notes: When the number of existing data nodes is 0, no ratio calculation is performed, the data timeout is recorded as 1, and the source verification value is recorded as 0. J407 determines data quality score After normalizing the transaction chain missing amount, chain consistency value, data timeout amount, and source verification value, the formula is used. A weighted calculation is performed to obtain the data quality score, where , , , These are pre-defined weighting factors; Additional notes: A higher data quality score indicates that the corrected transaction data is more suitable for subsequent transaction processing. The weights of each analysis can be preset based on the results of using historically confirmed transaction data. When historical samples are insufficient, the data can be configured according to the business requirements of the transaction type regarding completeness, consistency, timeliness, and verifiability. J408 matches transaction data quality levels and performs tiered processing. Three sets of data quality score intervals are pre-defined, and each set of data quality score intervals corresponds to a set of transaction data quality levels. The data quality scores are matched with the corresponding data quality score intervals to determine the corresponding transaction data quality level. Among them, the higher the data quality score, the higher the quality of the transaction data. The transaction data quality levels include normal use level, restricted use level, and low quality level. Normal usage level: The transaction data meets the conditions for subsequent processing, and the corresponding transaction data will be sent to the subsequent execution, measurement or settlement stages; Restricted Usage Level: Transaction data can be used in limited scenarios. A quality label is attached to the corresponding transaction data, the scope of use is restricted, and a review entry is retained. Low quality level: The transaction data does not meet the conditions for automatic use. The transaction data, source of the anomaly, correction record and quality verification result are sent to the management terminal, and the management personnel shall perform data supplementation or manual verification.

[0017] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0018] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0019] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0020] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0021] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0022] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0023] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0024] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for processing power trading data in an energy storage power station, characterized in that, Includes the following steps: Acquire the declaration data, clearing data, dispatch data, execution data, metering data and settlement data generated during the power trading process of energy storage power stations, construct the transaction processing chain according to the transaction tasks and time relationships, and form the segment verification objects corresponding to each verification segment; Downstream reference data is generated based on the upstream data nodes and corresponding processing rules of adjacent transaction links in the segment verification object. The downstream reference data is compared with the downstream actual data to determine the segment deviation characteristic data. The segment deviation characteristic data is comprehensively analyzed to obtain the deviation significance value, and the reverse traceability level is matched according to the deviation significance value. Among them, the segment deviation characteristic data includes relative quantity deviation, misalignment duration percentage, abnormal link percentage, and deviation duration percentage; Reverse verification is performed along the transaction processing chain according to the reverse tracing level to determine candidate anomaly source nodes, and the anomaly source data is determined through forward recalculation verification. The abnormal source data and its associated downstream transaction data are corrected. A data quality score is obtained based on the transaction data quality information of the corrected transaction data. The transaction data quality level is matched, and the corresponding graded handling is performed.

2. The method for processing power trading data of an energy storage power station according to claim 1, characterized in that, The specific process for forming the section verification object is as follows: Extract the transaction task identifier, data source identifier, data formation time, transaction period, transaction direction, transaction power, transaction volume, data change time, and upstream reference identifier from various types of electricity transaction data; Verification sections are divided based on the transaction start time, transaction end time, time of scheduling instruction change, time of execution status change, metering cycle boundary, and settlement cycle boundary. Data belonging to the same transaction task are linked in the order of declaration, clearing, scheduling, execution, measurement and settlement to form a transaction processing chain, and adjacent transaction links are set up according to the data node order to form segment verification objects.

3. The method for processing power trading data of an energy storage power station according to claim 2, characterized in that, The specific process for determining the relative quantity deviation is as follows: For the j-th adjacent transaction segment within the i-th verification segment, compare the downstream reference power with the downstream actual power, and the downstream reference electricity with the downstream actual electricity, and determine the relative power deviation using the following formula. and relative power deviation : , ; in, , These are the downstream reference power and the downstream actual power, respectively. , These are the downstream reference power volume and the downstream actual power volume, respectively. , Determined according to the minimum effective power resolution and the minimum effective charge resolution, respectively; The relative power deviation and relative energy deviation are weighted based on comparable data items, and the formula is used. Obtain the relative quantity deviation ; in, The sum can be used to compare and indicate power and energy consumption; and The deviation result is used when there is only one comparable item, corresponding to the weight.

4. The method for processing power trading data of an energy storage power station according to claim 3, characterized in that, The specific process for identifying abnormal links is as follows: The ratio of the non-overlapping duration of the downstream reference time coverage area to the total duration of the verification segment of the downstream actual time coverage area is determined as the misalignment duration percentage. The basic deviation value is obtained by weighting the relative quantity deviation and the proportion of misalignment time. When the basic deviation value is greater than or equal to the basic deviation threshold, the corresponding adjacent transaction link is identified as an abnormal link. When the actual downstream transaction direction is inconsistent with the downstream reference transaction direction, the corresponding adjacent transaction link will be identified as the direction abnormal link.

5. The method for processing power trading data of an energy storage power station according to claim 1, characterized in that, The specific process for obtaining the significant value of the deviation is as follows: relative quantity deviation of the section and the proportion of segment misalignment time The maximum value of the corresponding parameter within the same verification segment is used to determine the percentage of abnormal steps. The duration of segment deviation is determined based on the number of abnormal segments and the total number of adjacent transaction segments awaiting verification. Determined based on the duration of the abnormal detection segment; The formulas are used to analyze the relative quantity deviation of sections, the proportion of section misalignment time, the proportion of abnormal links, and the proportion of section deviation duration. Weighting is performed to obtain the significance value of the deviation. ;in , , , These are the corresponding preset weighting factors.

6. The method for processing power trading data of an energy storage power station according to claim 1, characterized in that, The specific process for determining the reverse traceability level is as follows: Three sets of significant deviation value intervals are pre-defined, and each set of significant deviation value intervals corresponds to a reverse traceability level. The significant deviation value is matched with the corresponding significant deviation value interval to obtain the reverse traceability level of the corresponding verification segment. The reverse traceability level includes the first level, the second level and the third level, and the higher the significant deviation value, the higher the matched reverse traceability level.

7. The method for processing power trading data of an energy storage power station according to claim 1, characterized in that, Correcting the data from the abnormal source and its associated downstream transaction data, including: The set of objects to be corrected is determined based on the transaction task identifier, the verification section identifier, and the upstream and downstream reference relationships. Based on the anomaly type, the correction object set is remapped for time boundaries, transaction volume is reallocated, upstream and downstream reference relationships are rebuilt, or backup metering data is replaced, and correction records are generated. When the actual execution result deviates from the scheduling target and the original execution data is true and valid, the original execution data is retained, and an execution deviation identifier and deviation description are added.

8. The method for processing power trading data of an energy storage power station according to claim 1, characterized in that, The specific process for analyzing transaction data quality information is as follows: The number of missing nodes is counted, and the ratio is calculated to the total number of necessary data nodes to obtain the transaction chain missing quantity. ; When a verification item meets the corresponding processing rules, its consistency identifier is recorded as 1; otherwise, it is recorded as 0. If the corresponding transaction process does not generate this type of data or the verification item is not applicable, it is not included in the calculation. The consistency identifiers of all applicable verification items are weighted and averaged to obtain the link consistency value. ; Read the expected formation time, expected update time, and actual formation or update time of each existing data node. When the actual formation or update time is later than the corresponding processing time limit, record the data node as a timeout node. The number of timeout nodes is counted, and the ratio of this count to the number of existing data nodes is calculated to obtain the data timeout rate. ; Each existing data node is checked to ensure it has a clear data source, creation time, transaction task identifier, and upstream / downstream connection basis. Each verifiable item is recorded as 1, and each unverifiable item as 0. All verification results are averaged to obtain the source verification value. ; After normalizing the transaction chain missing amount, chain consistency value, data timeout amount, and source verification value, the formula is used. A weighted calculation is performed to obtain the data quality score, where , , , These are pre-set weighting factors.

9. The method for processing power trading data of an energy storage power station according to claim 1, characterized in that, The specific process for determining the quality level of transaction data is as follows: Three sets of data quality score intervals are pre-defined, and each set of data quality score intervals corresponds to a set of transaction data quality levels. The data quality scores are matched with the corresponding data quality score intervals to determine the corresponding transaction data quality level.

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