A method and system for verifying power grid access data

Abstract

The present invention discloses a method and system for verifying power grid access data, which relates to the field of power grid technology, including obtaining power grid access raw data, parsing and converting it into a unified data format, and performing data preprocessing; constructing a data verification rule set, obtaining the preprocessed access data to load the data verification rule set, performing access data verification and marking abnormal verification results; reviewing the marked abnormal access data to confirm whether the marking is correct, confirming whether the abnormal data is repairable data, repairing the access data and calculating data quality indicators; importing the power grid access data that meets the data quality standard into the database of the power grid management system, completing the power grid access data verification, and updating the data verification rules. The present invention improves the security of data and the reliability of the power grid management system, provides accurate and timely information support for power grid management, and ensures that the power grid management system can operate stably and for a long time.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a method and system for verifying power grid access data. Background Art

[0002] In the power industry, especially with the rise of smart grids and power spot markets, data accuracy and reliability have become one of the key factors for the stable operation of the system. With the deep integration of modern information technology and power systems, the amount of data accessed by the power grid has grown exponentially, which has posed unprecedented challenges to the real-time processing capabilities and data quality of data. Traditional data verification methods, such as simple rule-based checks, can no longer meet the increasingly complex data structures and high-precision requirements. In addition, with the globalization and diversification of the power trading market, data sources are more extensive and data types are more diverse, which further increases the difficulty and complexity of data verification.

[0003] At present, the data verification methods adopted by most power systems mainly rely on preset fixed rule sets. Although these rules guarantee the basic quality of data to a certain extent, their flexibility and adaptability are insufficient when faced with highly dynamic changes and abnormal data. When processing large-scale real-time data streams, fixed rules may not be able to identify new data anomaly patterns in a timely manner, resulting in potential data quality issues not being effectively captured, thus affecting the normal operation of the power market and the accuracy of decision-making. In addition, fixed rule sets often have difficulty coping with the diversification of data types and the frequent changes in data formats, which limits the universality and scalability of data verification methods. Summary of the invention

[0004] In view of the problems existing in the existing power grid access data verification method, the present invention is proposed. Therefore, the problem to be solved by the present invention is how to provide a power grid access data verification method and system.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for verifying power grid access data, which includes obtaining raw power grid access data, parsing and converting it into a unified data format, and performing data preprocessing; constructing a data verification rule set, obtaining the preprocessed access data and loading the data verification rule set, performing access data verification and marking abnormal verification results; reviewing the marked abnormal access data to confirm whether the marking is correct, confirming whether the abnormal data is repairable data, repairing the access data and calculating data quality indicators; importing power grid access data that meets the data quality standards into the database of the power grid management system, completing power grid access data verification, and updating data verification rules.

[0007] As a preferred solution of the power grid access data verification method described in the present invention, the data preprocessing includes the following steps: collecting the communication protocol and API interface of the data source of the access data, parsing the received access data, and creating a data field mapping table; mapping the field names and formats from different sources to the corresponding fields under a unified format, encoding and converting the data, and converting the data in different formats into a unified processing format; transforming the data, integrating the access data from different sources into the same data set; storing the preprocessed data in a database, and delivering the processed data to the verification platform for data verification.

[0008] As a preferred solution of the power grid access data verification method described in the present invention, the integration of access data from different sources into the same data set includes the following steps: finding one or more fields as merge keys in the mapped access unified format data, and merging the data sets from different sources, specifically: if the record in the data set is a globally unique identifier, then directly use the identifier as the merge key; when there is no single globally unique identifier, use a combination of multiple fields to form a composite key for data merging; if the key values ​​of two data sets exist in both data sets at the same time, then retain the record for merging; if the record in data set D has no matching key value in data set F , then retain dataset D in the merged result set and fill all fields of dataset F with NULL; if the record in dataset F has no matching key value in dataset D, then retain dataset F in the merged result set and fill all fields of dataset D with NULL; if the record in dataset D or F has no matching key value in the other dataset, then retain the record in the merged result set and fill all fields of the other dataset with NULL; perform data conflict detection, check whether there is access data conflict after data merging, calculate access data priority index, select access data for integration according to priority value index, and verify the data in the integrated dataset.

[0009] As a preferred solution of the power grid access data verification method of the present invention, the calculation of the access data priority index includes the following steps: collecting characteristic data of the access data, and performing quantitative index calculation, and the calculation formula is:

[0010] ,

[0011] ,

[0012] ,

[0013] In the formula, represents the historical accuracy score of data source i, is the data value of data source i at the jth record, is the actual value of the corresponding data source i at the jth time, and n is the number of records; represents the integrity score of data source i, is the number of records with values ​​in data source i, is the total number of records; represents the business value score of data source i, is the weight of the k-th type of business, is the impact factor of data source i on the kth type of business, and m is the total number of business types; the priority score is calculated based on the quantitative indicators of each data, and the calculation formula is:

[0014] ,

[0015] ,

[0016] In the formula, represents the overall priority score of the i-th data source, , , are the weight coefficients of historical accuracy, data completeness and business value, and are the minimum and maximum values ​​of indicator x in all data sources, respectively. Represents the quantitative index of each data. Represents indicators from all data sources;

[0017] As a preferred solution of the power grid access data verification method described in the present invention, the construction of the data verification rule set includes the following steps: initializing the rule base, defining the initial rule base of basic verification rules, specifying the type, effective time, application area, parameter value, priority and alarm level for each rule and adding a descriptive label; when data is accessed, using the rules in the initial rule base to verify the data, recording the verification results, and extracting failure key features from the data that failed the verification; converting the extracted failure key features into structured feature vectors, using the feature vectors, searching for failure key features in the initial rule base, evaluating the validity of the rules of the failure key features, and checking whether they can accurately capture the abnormal patterns of the failure key features; searching for feature rules corresponding to the current data in the initial rule base, if the corresponding feature rules of the current data fail to capture the abnormal patterns of the current data, modifying or creating new rules according to the newly extracted features, adding the optimized rules or the newly created rules to the initial rule base, and removing the old rules that are no longer applicable, and regularly reviewing the initial rule base; the regular review of the initial rule base includes eliminating outdated or invalid rules to maintain the timeliness and validity of the initial rule base.

[0018] As a preferred solution of the power grid access data verification method described in the present invention, the access data verification includes the following steps: the access data verification includes the following steps: after completing the initial inspection of the access data, submitting the access data to the data verification module, and temporarily storing the access data in the data queue to be verified according to the data integration result of the access data; reading the data verification rules from the database, executing the data verification rules in sequence according to the priority order of the access data, and starting to verify one by one; based on the data verification rules read from the database, if the verification rules fail and the alarm level is an error, then the verification of the verification rules with lower priority is performed after the current data is implemented, and the verification process of the next data in the data queue to be verified is entered, and it is marked as an abnormal verification result; based on the data queue to be verified, if there is an alarm, the alarm information is recorded, and the next rule is executed until the verification rules have been executed; when all verification rules are executed, the marked abnormal access data is reviewed and confirmed, and the passed abnormal access data is transmitted to the database.

[0019] As a preferred solution of the power grid access data verification method of the present invention, the calculation of the data quality index includes the following steps: confirming whether the data field of the access data after data repair conforms to the expected format, checking whether the numerical field is within a reasonable range and whether all fields have values; calculating the data quality index to compare the real value with the recorded value, and calculating the correctness ratio of the data point, and the calculation formula is:

[0020] ,

[0021] In the formula, is the access data accuracy index after repair, N represents the total number of access data recorded in the dataset, Represents the value of the i-th record in the data set, Indicates the verified correct value of the i-th record; select the data quality threshold A1 according to business requirements. If the access data accuracy index after repair is ≥A1, the data repair result is accepted and the repaired data is saved in the database; otherwise, the data repair result is rejected and the verification result of the accessed data fails.

[0022] In a second aspect, the present invention provides a power grid access data verification system, which includes: an acquisition module, used to acquire power grid access raw data, parse and convert it into a unified data format, and perform data preprocessing; a verification module, used to construct a data verification rule set, acquire the preprocessed access data to load the data verification rule set, perform access data verification and mark abnormal verification results; a repair module, used to review the marked abnormal access data to confirm whether the mark is correct, confirm whether the abnormal data is repairable data, repair the access data and calculate data quality indicators; an update module, used to import power grid access data that meets the data quality standards into the database of the power grid management system, complete the power grid access data verification, and update the data verification rules.

[0023] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the grid access data verification method are implemented.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program, when executed by a processor, implements the steps of a method for verifying grid access data.

[0025] The beneficial effects of the present invention are that the grid access data from different sources can be seamlessly connected, and the compatibility and scalability of the system can be enhanced. The security of data and the reliability of the grid management system can be improved. The burden of manual review can be reduced, and the efficiency and intelligence level of data processing can be improved. The effective use of data can be ensured, and accurate and timely information support can be provided for grid management, so as to ensure that the grid management system can operate stably in the long term and cope with the ever-changing data environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 The figure is a flow chart of a method for verifying grid access data. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0030] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0031] Example 1

[0032] Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for verifying power grid access data, comprising:

[0033] S1: Obtain the original data of grid access, parse and convert it into a unified data format, and perform data preprocessing;

[0034] Specifically, collect the communication protocols and API interfaces of the data sources that access the data; parse the received access data and create a data field mapping table; receive raw data from different sources, which may include sensor data, market transaction data, weather forecast data, etc. Data is usually transmitted in different formats, such as CSV, JSON, or XML, so it is necessary to perform appropriate parsing to convert it into a unified format for further processing. Preliminary screening and cleaning: Perform preliminary screening of the data to remove obvious errors or incomplete information, for example, check whether the field is empty, whether the data type meets the expectations, and whether there are obvious syntax errors or format inconsistencies.

[0035] Map the field names and formats from different sources to the corresponding fields in a unified format, encode and convert the data, and convert data in different formats into a unified processable format;

[0036] Transform the data and integrate the access data from different sources into the same data set;

[0037] The preprocessed data is stored in the database and the processed data is delivered to the verification platform for data verification.

[0038] Find one or more fields in the mapped unified format data as merge keys to merge data sets from different sources. Specifically:

[0039] If the record in the data set has a globally unique identifier, the identifier is directly used as the merge key. When there is no single globally unique identifier, a combination of multiple fields is used to form a composite key for data merging.

[0040] If the key value of two data sets exists in both data sets, the record is retained for merging;

[0041] If the record in dataset D has no matching key value in dataset F, then dataset D is retained in the merged result set and all fields of dataset F are filled with NULL;

[0042] If the record in dataset F has no matching key value in dataset D, then dataset F is retained in the merged result set and all fields of dataset D are filled with NULL;

[0043] If a record in dataset D or F does not have a matching key value in the other dataset, the record is retained in the merged result set and all fields of the other dataset are filled with NULL;

[0044] Perform data conflict detection, check whether there is access data conflict after data merging, calculate access data priority index, select access data for integration based on priority value index, and verify the data in the integrated data set.

[0045] Collect the characteristic data of access data and calculate the quantitative indicators. The calculation formula is:

[0046] ,

[0047] ,

[0048] ,

[0049] In the formula, represents the historical accuracy score of data source i, is the data value of data source i at the jth record, is the actual value of the corresponding data source i at the jth time, and n is the number of records; represents the integrity score of data source i, is the number of records with values ​​in data source i, is the total number of records; represents the business value score of data source i, is the weight of the k-th type of business, is the impact factor of data source i on the kth type of service, and m is the total number of service types;

[0050] The priority score is calculated based on the quantitative indicators of each piece of data. The calculation formula is:

[0051] ,

[0052] ,

[0053] In the formula, represents the overall priority score of the i-th data source, , , are the weight coefficients of historical accuracy, data completeness and business value, and are the minimum and maximum values ​​of indicator x in all data sources, respectively. Represents the quantitative index of each data. Represents indicators from all data sources;

[0054] All access data are sorted according to the calculated priority scores, the access data are integrated according to the sorting results, and the data are verified in sequence.

[0055] S2: Build a data verification rule set, obtain the pre-processed access data, load the data verification rule set, perform access data verification, and mark abnormal verification results;

[0056] Specifically, initialize the rule base, define the initial rule base of basic verification rules, and create a rule base covering data format, date and time, value range, logical consistency, etc. Specify the type (such as general, single item, comprehensive), effective time, application area, parameter value, priority and alarm level for each rule, and add descriptive tags.

[0057] When data is accessed, the rules in the rule base are used to verify the data and the verification results are recorded. The key features that caused the failure, such as abnormal values, data format errors, logical contradictions, etc., are extracted from the data that failed the verification.

[0058] The extracted key failure features are converted into structured feature vectors. The feature vectors are used to search for the key failure features in the initial rule base, and the effectiveness of the rules of the key failure features is evaluated to check whether they can accurately capture the abnormal patterns of the key failure features.

[0059] Search the initial rule base for feature rules corresponding to the current data. If the feature rules corresponding to the current data fail to capture the abnormal patterns of the current data, modify or create new rules based on the newly extracted features, add the optimized rules or newly created rules to the initial rule base, remove old rules that are no longer applicable, and review the initial rule base regularly.

[0060] Regularly review the initial rule base, remove outdated or invalid rules, and maintain the timeliness and effectiveness of the rule base. Monitor the performance of the rule base, including the hit rate, false positive rate, false negative rate, and verification speed of the rules.

[0061] Collect user feedback, understand the performance of the rule base in actual applications, and perform targeted optimization based on the feedback.

[0062] Use machine learning algorithms to automatically identify abnormal patterns in data and assist in the generation and optimization of rules. During the data verification process, actively learn new abnormal patterns and continuously enrich and improve the rule base.

[0063] After completing the initial check of the access data, the access data is submitted to the data verification module. The verification module analyzes the basic information of the data, such as type, source, date and size, and temporarily stores the data content in the data queue to be verified.

[0064] After completing the initial check of the access data, the access data is submitted to the data verification module, and the access data is temporarily stored in the data queue to be verified according to the data integration result of the access data; the data verification rules are read from the database, and the data verification rules are executed in sequence according to the priority order of the access data, and verification begins one by one; based on the data verification rules read from the database, if the verification rule fails and the alarm level is an error, the verification of the verification rules with lower priority is performed after the current data is implemented, and the verification process of the next data in the data queue to be verified is entered, and it is marked as an abnormal verification result; based on the data queue to be verified, if there is an alarm, the alarm information is recorded and the next rule is executed until all the verification rules have been executed; when all verification rules are executed, the marked abnormal access data is reviewed and confirmed, and the passed abnormal access data is transmitted to the database.

[0065] S3: Review the marked abnormal access data to confirm whether the mark is correct, confirm whether the abnormal data is repairable data, repair the access data and calculate the data quality index;

[0066] Specifically, after the data is repaired, check whether the data fields of the accessed data conform to the expected format, check whether the numeric fields are within a reasonable range, and check whether all fields have values ​​without null values;

[0067] Use statistical methods (such as standard deviation, IQR) or cluster analysis to find outliers. Decide to keep, correct, or delete outliers. Perform one-hot encoding or label encoding on categorical variables and scale numerical variables to make them suitable for subsequent calculations or modeling.

[0068] Calculate data quality indicators, compare true values ​​with recorded values, refer to external reliable data sources, and calculate the correctness ratio of data points.

[0069] ,

[0070] In the formula, is the access data accuracy index after repair, N represents the total number of access data recorded in the dataset, Represents the value of the i-th record in the data set, Indicates the verified correct value of the i-th record; select the data quality threshold A1 according to business requirements. If the access data accuracy index after repair is ≥A1, the data repair result is accepted and the repaired data is saved in the database; otherwise, the data repair result is rejected and the verification result of the accessed data fails.

[0071] Table 1 Quality threshold test results

[0072] ,

[0073] As shown in Table 1, at an accuracy threshold of 80%, the data acceptance ratio increased from 95% to 93%, and the system operating efficiency decreased slightly from 280ms to 270ms, indicating that even at a lower threshold, the system performance fluctuated slightly, but the overall impact was not significant. At an accuracy threshold of 85%, the data acceptance ratio increased from 90% to 88%, and the system operating efficiency decreased from 260ms to 250ms, which showed that with the increase of the threshold, the system operating efficiency improved significantly, but the data acceptance ratio decreased slightly. At an accuracy threshold of 90%, the data acceptance ratio increased from 80% to 78%, and the system operating efficiency decreased from 240ms to 230ms, further confirming the positive impact of increasing the threshold on system efficiency. At an accuracy threshold of 95%, the data acceptance ratio increased from 65% to 63%, and the system operating efficiency (average delay) decreased from 200ms to 190ms, indicating that under high accuracy requirements, although the data acceptance ratio decreased significantly, the system operating efficiency was significantly improved. On the one hand, the high threshold improves data quality and system efficiency, but on the other hand, it also leads to a significant decrease in the data acceptance ratio. Therefore, 95% is selected as the accuracy threshold in this method to ensure high data quality while also improving the efficiency of system operation without excessively sacrificing the acceptance ratio of data.

[0074] Create data quality reports to summarize the problems found and the measures taken, and continuously monitor: Implement data quality monitoring mechanisms, regularly check data, and prevent similar problems from occurring in the future. Improve data collection, storage, and processing processes based on data quality analysis results

[0075] S4: Import the grid access data that meets the data quality standards into the database of the grid management system, complete the grid access data verification, and update the data verification rules.

[0076] Furthermore, the present embodiment also provides a power grid access data verification system, including: an acquisition module, used to acquire the original power grid access data, parse and convert it into a unified data format, and perform data preprocessing; a verification module, used to construct a data verification rule set, acquire the preprocessed access data, load the data verification rule set, perform access data verification and mark abnormal verification results; a repair module, used to review the marked abnormal access data to confirm whether the mark is correct, confirm whether the abnormal data is repairable data, repair the access data and calculate data quality indicators; an update module, used to import power grid access data that meets the data quality standards into the database of the power grid management system, complete the power grid access data verification, and update the data verification rules.

[0077] This embodiment also provides a computer device, which is applicable to the case of a power grid access data verification method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement all or part of the steps of the method described in the embodiment of the present invention as proposed in the above embodiment.

[0078] This embodiment also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in any optional implementation of the above embodiment is executed. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.

[0079] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0080] Example 2

[0081] Referring to Table 2, which is a second embodiment of the present invention, this embodiment provides a method for verifying grid access data. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0082] Table 2 Technical characteristics comparison table

[0083] ,

[0084] As can be seen from Table 2, this method enables grid access data from different sources to be seamlessly connected, enhancing the compatibility and scalability of the system. It can accurately identify abnormal data that does not meet quality standards, and prevent low-quality data from interfering with and misleading the grid management system. It ensures that only high-quality data that has passed verification can enter the system, improving data security and the reliability of the grid management system. It realizes the automation of data quality control, reduces the burden of manual review, and improves the efficiency and intelligence level of data processing. It avoids misjudgment and improves the level of refinement of data management. Repairing repairable abnormal data improves data utilization, provides quantitative data quality assessment, facilitates monitoring of data quality changes, and adjusts data processing strategies in a timely manner. Ensure the effective use of data and provide accurate and timely information support for grid management. Ensure that the grid management system can operate stably and long-term to cope with the ever-changing data environment.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for verifying power grid access data, characterized in that: include: Obtain the original data of grid access, parse and convert it into a unified data format, and perform data preprocessing; Build a data verification rule set, obtain the pre-processed access data, load the data verification rule set, perform access data verification, and mark abnormal verification results; Review the marked abnormal access data to confirm whether the mark is correct, confirm whether the abnormal data is repairable data, repair the access data and calculate the data quality index; Import the grid access data that meets the data quality standards into the database of the grid management system, complete the grid access data verification, and update the data verification rules; The data preprocessing comprises the following steps: Collect the communication protocol and API interface of the data source of the access data, parse the received access data, and create a data field mapping table; Map the field names and formats from different sources to the corresponding fields in a unified format, encode and convert the data, and convert data in different formats into a unified processing format; Transform the data and integrate the access data from different sources into the same data set; The preprocessed data is stored in the database and the processed data is delivered to the verification platform for data verification; The step of integrating access data from different sources into the same data set includes the following steps: Find one or more fields in the mapped unified format data as merge keys to merge data sets from different sources. Specifically: If the record in the data set has a globally unique identifier, the identifier is directly used as the merge key. When there is no single globally unique identifier, a combination of multiple fields is used to form a composite key for data merging. If the key value of two data sets exists in both data sets, the record is retained for merging; If the record in dataset D has no matching key value in dataset F, then dataset D is retained in the merged result set and all fields of dataset F are filled with NULL; If the record in dataset F has no matching key value in dataset D, then dataset F is retained in the merged result set and all fields of dataset D are filled with NULL; If a record in dataset D or F does not have a matching key value in the other dataset, the record is retained in the merged result set and all fields of the other dataset are filled with NULL; Perform data conflict detection, check whether there is any access data conflict after data merging, calculate the access data priority index, select access data for integration based on the priority value index, and verify the data in the integrated data set; The calculation of the access data priority index comprises the following steps: Collect the characteristic data of access data and calculate the quantitative indicators. The calculation formula is: In the formula, represents the historical accuracy score of data source i, is the data value of data source i at the jth record, is the actual value of the corresponding data source i at the jth time, and n is the number of records; represents the integrity score of data source i, is the number of records with values ​​in data source i, is the total number of records; represents the business value score of data source i, is the weight of the k-th type of business, is the impact factor of data source i on the kth type of service, and m is the total number of service types; The priority score is calculated based on the quantitative indicators of each piece of data. The calculation formula is: In the formula, represents the overall priority score of the i-th data source, , , are the weight coefficients of historical accuracy, data completeness and business value, and are the minimum and maximum values ​​of indicator x in all data sources, respectively. Represents the quantitative index of each data. Represents indicators from all data sources; All access data are sorted according to the calculated priority scores, the access data are integrated according to the sorting results, and the data are verified in sequence.

2. The method for verifying grid access data according to claim 1, characterized in that: The construction of the data verification rule set includes the following steps: Initialize the rule base, define the initial rule base of basic verification rules, specify the type, effective time, application area, parameter value, priority and alarm level for each rule, and add descriptive labels; When data is accessed, the rules in the initial rule base are used to verify the data, the verification results are recorded, and the key failure features are extracted from the data that failed the verification; The extracted key failure features are converted into structured feature vectors. The feature vectors are used to search for the key failure features in the initial rule base, and the validity of the rules of the key failure features is evaluated to check whether they can accurately capture the abnormal patterns of the key failure features. Search the initial rule base for feature rules corresponding to the current data. If the feature rules corresponding to the current data fail to capture the abnormal patterns of the current data, modify or create new rules based on the newly extracted features, add the optimized rules or newly created rules to the initial rule base, remove old rules that are no longer applicable, and review the initial rule base regularly. The regular review of the initial rule base includes eliminating outdated or invalid rules to maintain the timeliness and effectiveness of the initial rule base.

3. The method for verifying grid access data according to claim 2, characterized in that: The access data verification comprises the following steps: After completing the initial check of the access data, the access data is submitted to the data verification module, and the access data is temporarily stored in the queue of data to be verified according to the data integration result of the access data; Read the data verification rules from the database, execute them in order according to the priority of the accessed data, and start to verify them one by one; Based on the data verification rules read from the database, if the verification rules fail and the alarm level is error, the verification rules with lower priority will be verified after the current data is implemented, and the verification process of the next data in the queue of data to be verified will be entered, and it will be marked as an abnormal verification result; Based on the queue of data to be verified, if there is an alarm, the alarm information is recorded and the next rule is executed until all the verification rules have been executed; After all verification rules are executed, the marked abnormal access data is reviewed and confirmed, and the passed abnormal access data is transmitted to the database.

4. The method for verifying grid access data according to claim 3, characterized in that: The calculation of the data quality indicator comprises the following steps: After data repair, confirm whether the data fields of the accessed data conform to the expected format, check whether the numerical fields are within a reasonable range and whether all fields have values; Calculate the data quality index to compare the true value with the recorded value and calculate the correctness ratio of the data point. The calculation formula is: In the formula, is the access data accuracy index after repair, N represents the total number of access data recorded in the dataset, Represents the value of the i-th record in the data set, Indicates the verified correct value of the i-th record; Select data quality threshold A1 according to business needs. If the access data accuracy index after repair is ≥A1, the data repair result is accepted and the repaired data is saved in the database; otherwise, the data repair result is rejected and the verification result of the accessed data fails.

5. A power grid access data verification system, based on the power grid access data verification method according to any one of claims 1 to 4, characterized in that: include: An acquisition module is used to acquire the original data of power grid access, parse and convert it into a unified data format, and perform data preprocessing; The verification module is used to build a data verification rule set, obtain the pre-processed access data, load the data verification rule set, perform access data verification and mark abnormal verification results; The repair module is used to review the marked abnormal access data to confirm whether the mark is correct, confirm whether the abnormal data is repairable data, repair the access data and calculate the data quality index; The update module is used to import the grid access data that meets the data quality standards into the database of the grid management system, complete the grid access data verification, and update the data verification rules.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the grid access data verification method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the grid access data verification method according to any one of claims 1 to 4 are implemented.

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