A method and system for intelligent governance of power equipment data based on data owner responsibility chain

Abstract

The application relates to the technical field of data governance, in particular to a power equipment data intelligent governance method and system based on a data owner responsibility chain, which comprises the following steps: recording data responsible person information of a data governance object, establishing a data owner responsibility chain, recording the responsibility, permission and activity record of each data owner, and generating a data owner profile index.In the application, the efficiency and accuracy of task execution are enhanced, and human errors and delays are reduced by allocating power equipment data checking and updating tasks; in the data checking link, deviation and missing data items are effectively marked out by comparing the data with an authoritative data source; the data items that need to be updated and corrected are manually audited and determined; the quality of the data and the timeliness of the update are ensured; abnormalities in the data flow are found in time; the monitoring and safety of the equipment are strengthened; the data governance efficiency of the power equipment is significantly improved; and the high quality and high efficiency of the data governance are ensured.

Description

Technical Field

[0001] This invention relates to the field of data governance technology, and in particular to an intelligent governance method and system for power equipment data based on the data owner responsibility chain. Background Technology

[0002] Data governance technology refers to a series of strategies and practices for the precise management and control of data throughout its entire lifecycle. This includes aspects such as data quality, data management, data policy development, compliance, and security, with the aim of ensuring data accuracy, accessibility, consistency, and protection. In modern enterprises, data governance is a core part of information technology management, involving how to reasonably collect, store, use, and delete data to support the enterprise's decision-making process and comply with relevant legal and regulatory requirements. Effective data governance can not only increase the value of data but also reduce risks in enterprise operations.

[0003] Among them, the intelligent governance method for power equipment data based on the data owner responsibility chain refers to managing and optimizing the data processing and application of power equipment by constructing a clear responsibility chain, thereby improving the operation and maintenance efficiency of power equipment and the effectiveness of data utilization. Applied to the power industry, this ensures that the integration, analysis, and utilization of equipment data are carried out within a strict management framework, achieving high-quality and efficient use of data. Through intelligent data governance, power companies can better monitor and maintain equipment, optimize resource allocation, improve service quality and energy efficiency, and also help cope with complex regulatory requirements and improve system transparency.

[0004] Existing data governance technologies lack personalized management strategies tailored to specific industries or equipment, resulting in inefficiencies and ineffectiveness that fail to fully meet specific needs. For example, in the power industry, the sheer volume and complexity of equipment data require more meticulous and professional processing methods, which traditional data governance approaches cannot effectively handle intricate data within specific scenarios. Current technologies rely on manual allocation of data verification and update tasks, increasing both workload and the risk of errors. Regarding data security, the lack of dynamic risk assessments and corresponding security adjustments exposes data to greater security threats. For instance, failure to promptly detect anomalies in data streams can lead to security incidents, impacting the stable operation of the entire power system. These shortcomings limit the effectiveness of data governance in practice, hindering the full realization of data's potential value and increasing operational risks. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent governance method and system for power equipment data based on a data owner responsibility chain.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent governance method for power equipment data based on a data owner responsibility chain, comprising the following steps:

[0007] S1: Record the data owner information of the data governance object, establish a data owner responsibility chain, record the responsibilities, permissions and activity records of each data owner, and generate a data owner profile index;

[0008] S2: Based on the data owner profile index, create a power equipment data governance task, automatically identify the data owner, assign data verification and update tasks, assign identifiers, and generate task assignment records;

[0009] S3: Using the task allocation record, analyze the power equipment information that failed the data verification, compare the data with authoritative data sources, mark the data items with deviations and missing data items, and generate a summary record of verification results;

[0010] S4: Based on the summarized verification results, verify the actual situation, determine the issues that need to be addressed, complete the data governance, and generate an audit and adjustment record;

[0011] S5: Based on the audit and adjustment records, monitor the data flow during the operation of the power equipment, calculate the information entropy, and if the information entropy is lower than the safety threshold, record the abnormal time, data flow source and security risk, and generate an entropy value assessment result;

[0012] S6: Based on the entropy value assessment results, analyze data security risks, adjust encryption measures according to the data sensitivity and risk level, and generate a data security solution by continuously tracking the encryption effect.

[0013] As a further aspect of the present invention, the data owner profile index includes an index of each data owner's responsibilities, permissions, activity records, and personal information; the task allocation record includes the identification information of the data manager, information on the assigned data verification and update tasks, and task identifiers; the verification result summary record includes deviations, missing data items, and comparison results with authoritative data sources during the data verification process of power equipment information; the audit and adjustment record includes a description of data governance issues, a record of the actual verification situation, the adjustment measures taken, the adjusted data status, and a timestamp; the entropy evaluation result includes the recorded abnormal time, data stream source, information entropy calculation value, and security risk level; and the data security scheme includes encryption measures adjusted according to the data's sensitivity and risk level, continuous monitoring of encryption effectiveness evaluation, and a security update plan.

[0014] As a further aspect of the present invention, the steps of recording the data manager information of the data governance object, establishing a data owner responsibility chain, recording the responsibilities, permissions, and activity records of each data owner, and generating a data owner profile index are as follows:

[0015] S101: Based on the data governance object information, enter the name, department and role of the data owner into the target dataset, assign a unique identifier to each record, record the creation and update timestamps, and generate a basic data owner profile;

[0016] S102: Using the aforementioned data owner basic profile, enter the responsibilities and permissions of each data owner into the dataset, synchronously record the timestamp and version number of the modification each time the data is updated, verify the integrity and consistency of the data, and generate a responsibility profile record;

[0017] S103: Through the aforementioned responsibility profile, record the activity logs of each data owner, including data access, modification, and security events, link the activities to the data owner's personal profile, establish a data owner responsibility chain, verify the traceability of activity records, and generate a data owner profile index.

[0018] As a further aspect of the present invention, the steps of creating power equipment data governance tasks based on the data owner profile index, automatically identifying data owners, assigning data verification and update tasks, assigning identifiers, and generating task assignment records are as follows:

[0019] S201: Using the data owner profile index, identify the person in charge of the power equipment data, and generate a power equipment data person in charge matching record by matching the identification result with the person in charge profile;

[0020] S202: Based on the matching record of the data responsible persons of the power equipment, automatically assign data verification and update tasks to the associated data responsible persons. Each task is accompanied by instructions and deadlines, and the content of the task is recorded to generate data management tasks.

[0021] S203: Through the data management task, create a unique identifier for each assigned task, record the task creation time and person in charge information, verify the tracking and management of each task, and generate task assignment records.

[0022] As a further aspect of the present invention, the steps of using the task allocation record to analyze the power equipment information that failed data verification, comparing the data with authoritative data sources, marking deviations and missing data items, and generating a summary record of verification results are as follows:

[0023] S301: Based on the task allocation record, filter out the power equipment information that failed the initialization data verification, record the person in charge and the problem points of the power equipment, and generate an initialization problem power equipment record;

[0024] S302: Using the aforementioned initial problem power equipment record, perform comparative analysis on each data item, compare it with authoritative data sources, mark the deviation and missing data, and generate a deviation comparison record;

[0025] S303: By summarizing the marked deviations and missing data items through the deviation comparison record, extracting key information and proposing improvement plans, verifying the integrity of power equipment data quality control, and generating a summary record of verification results.

[0026] As a further aspect of the present invention, the steps of summarizing the verification results, verifying the actual situation based on the verification results, determining the issues requiring remediation, completing data remediation, and generating audit and adjustment records are as follows:

[0027] S401: Based on the summary record of the verification results, identify multiple types of data anomalies and fluctuations, track and analyze the source of each type of data, extract key sources of deviation, and obtain a list of data problems;

[0028] S402: Based on the data problem list, set data governance measures, adjust the data for the identified problems, manually correct obvious errors, perform data processing and batch update operations, and obtain a summary of data governance results;

[0029] S403: Through the data governance result summary, review the adjusted dataset, verify the data consistency and integrity, check that the dataset update complies with business rules and requirements, and obtain the review and adjustment record.

[0030] As a further aspect of the present invention, based on the audit and adjustment records, the steps of monitoring the data flow during the operation of power equipment, calculating information entropy, and recording the abnormal time, data flow source, and security risk to generate an entropy value assessment result are as follows:

[0031] S501: Using the aforementioned audit and adjustment records, monitor the data flow of power equipment during operation, calculate the information entropy of the data flow in real time, evaluate the volatility of the data, and generate real-time entropy value monitoring records;

[0032] S502: Based on the real-time entropy monitoring record, the information entropy of each data stream is calculated using sliding window technology and compared with the set safety threshold. The abnormal time and source of the data stream below the threshold are identified and recorded, and a low entropy abnormal record is generated.

[0033] S503: By analyzing the low entropy value anomaly records, analyze the potential security risks of each abnormal data stream, summarize the risk factors and improvement measures, verify the security and integrity of power equipment data, and generate entropy value assessment results.

[0034] As a further aspect of the present invention, the formula for the sliding window technique is as follows:

[0035]

[0036] Where E(t) is the weighted average entropy value within a given time window W, t represents the number of time points, and H i This represents the information entropy at time point i. α represents the average information entropy of all data points within the sliding window, W represents the width of the sliding window, α represents the square root weighting coefficient, β represents the relative value weighting coefficient, and γ represents the adjustment coefficient.

[0037] As a further aspect of the present invention, the steps of analyzing data security risks based on the entropy value evaluation results, adjusting encryption measures according to the data sensitivity and risk level, and generating a data security scheme by continuously tracking the encryption effect are as follows:

[0038] S601: Using the entropy value evaluation result, prioritize the security risks in the data stream, adjust the encryption measures according to the sensitivity and risk level of the data, match the security requirements of different levels, and generate the adjusted encryption strategy;

[0039] S602: Based on the adjusted encryption strategy, implement new encryption measures, including adjusting the key strength and changing the encryption method, compare the changes in security performance before and after encryption, record the time and effect of each adjustment, and generate an encryption strategy implementation record;

[0040] S603: Implement the encryption strategy, continuously monitor and evaluate the effectiveness of new encryption measures, verify the integrity and confidentiality of data streams, record key adjustments and improvements, and generate a data security plan.

[0041] A power equipment data intelligent governance system based on data owner responsibility chain, wherein the power equipment data intelligent governance system based on data owner responsibility chain is used to execute the above-mentioned power equipment data intelligent governance method based on data owner responsibility chain, the system comprising:

[0042] The data registration module collects data on managers of power equipment, records the responsibilities, permissions, and activity logs of each data owner, integrates the information, and generates a data owner profile index.

[0043] Based on the data owner profile index, the identifier allocation module identifies the data owner, automatically allocates data verification and update tasks, assigns an identifier to each task, and obtains task allocation records.

[0044] The data verification module uses the task allocation record to check the power equipment data items, compare them with authoritative data sources, mark the deviations and missing parts in the data, and generate a summary record of verification results.

[0045] The data analysis module uses the verification results to summarize the records, manually reviews the data items of the power equipment, determines the data content that needs to be corrected, identifies the problems that need to be addressed, and obtains the audit and adjustment records.

[0046] Based on the audit and adjustment records, the safety monitoring module monitors the operating data of power equipment, calculates the information entropy value, marks data anomalies below the safety threshold, records the anomaly time and data source, analyzes potential safety risks, and formulates data security solutions.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] This invention enhances the traceability and transparency of data governance by accurately recording the information of those responsible for power equipment data and establishing a clear chain of responsibility. It records the duties, permissions, and activities of each data owner, optimizing responsibility allocation and ensuring clear traceability of those responsible at each stage. When creating power equipment data governance tasks, it automatically identifies data owners and assigns data verification and update tasks, enhancing the efficiency and accuracy of task execution and reducing human error and delays. In the data verification stage, data is compared with authoritative data sources, effectively identifying deviations and missing data items, directly improving data accuracy and reliability. Manual review identifies data items requiring updates and corrections, ensuring data quality and timely updates. Monitoring the data flow during power equipment operation and calculating information entropy enables timely detection of anomalies in the data flow, strengthening equipment monitoring and security. By analyzing data security risks and adjusting encryption measures based on data sensitivity, it strengthens data protection, reduces security risks, significantly improves the efficiency of power equipment data governance, and ensures high-quality and efficient data governance applications. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flowchart of S1 of the present invention;

[0051] Figure 3 This is a detailed flowchart of the S2 process of the present invention;

[0052] Figure 4 This is a detailed flowchart of the S3 process of the present invention;

[0053] Figure 5 This is a detailed flowchart of the S4 process of the present invention;

[0054] Figure 6This is a detailed flowchart of S5 of the present invention;

[0055] Figure 7 This is a detailed flowchart of S6 of the present invention;

[0056] Figure 8 This is a system flowchart of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0058] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0059] Please see Figure 1 This invention provides a technical solution: an intelligent governance method for power equipment data based on a data owner responsibility chain, comprising the following steps:

[0060] S1: Record the data owner information of the data governance object, compile the information into a directory, establish a data owner responsibility chain, record the responsibilities, permissions and activity records of each data owner, and structure the information according to the responsibility hierarchy to generate a data owner profile index;

[0061] S2: Based on the data owner profile index, select power equipment as the governance object, create a data governance master task, automatically identify the data owner of the associated equipment according to the selection, assign data verification and update tasks, assign identifiers to tasks, and generate task assignment records;

[0062] S3: Using task allocation records, analyze the power equipment information that failed data verification, compare the data with authoritative data sources, mark the data items with deviations and missing data, and generate a summary record of verification results;

[0063] S4: By summarizing the verification results, select the power equipment data that failed, manually review the data, compare it with the real-time operation data, determine the data items that need to be updated and corrected, record the decision basis in the review process, and generate a review adjustment record;

[0064] S5: Based on the audit and adjustment records, monitor the data flow during the operation of power equipment, calculate the information entropy of data packets, and if the information entropy is lower than the safety threshold, record the abnormal time, data flow source and security risk, and generate entropy value assessment results;

[0065] S6: Based on the entropy value assessment results, analyze data security risks, adjust encryption measures according to the data's sensitivity and risk level, implement protection protocols, continuously track the encryption effect, verify the effectiveness of the adjustment measures, and generate a data security solution.

[0066] The data owner profile index includes an index of each data owner's responsibilities, permissions, activity records, and personal information. The task assignment record includes the identification information of the data manager, information on the assigned data verification and update tasks, and task identifiers. The verification result summary record includes deviations, missing data items, and comparison results with authoritative data sources during the data verification process of power equipment information. The audit and adjustment record includes a description of data governance issues, a record of the actual verification situation, the adjustment measures taken, the adjusted data status, and timestamps. The entropy assessment result includes the recorded abnormal time, data flow source, information entropy calculation value, and security risk level. The data security plan includes encryption measures adjusted according to the data's sensitivity and risk level, continuous monitoring of encryption effectiveness evaluation, and a security update plan.

[0067] Please see Figure 2 The specific steps for recording the data owner information of data governance objects, establishing a data owner responsibility chain, recording the responsibilities, permissions, and activity records of each data owner, and generating a data owner profile index are as follows:

[0068] S101: Based on the data governance object information, the execution flow of entering the data owner's name, department, and role into the target dataset, assigning a unique identifier to each record, and recording the creation and update timestamps to generate the basic data owner profile is as follows;

[0069] Based on the data governance object information, the system inputs the data owner's name, department, and role into the target dataset. This process involves multiple steps. The system needs to extract the data owner's personal information from the data governance object information, including name, department, and role. After extraction, the data is cleaned and standardized to meet the database's input requirements. The system generates a unique identifier for each record, involving an algorithm such as a UUID generation algorithm, to ensure the uniqueness of each data owner's record in the dataset. Simultaneously, the system records the creation and update timestamps of each data entry. The timestamp generation uses the server's system time to ensure the accuracy of the recorded time. The entire process is automated, ensuring efficient and accurate data entry and generating basic data owner profiles.

[0070] S102: Using the basic data owner profile, the responsibilities and permissions of each data owner are entered into the dataset. The timestamp and version number of the modification are recorded synchronously each time the data is updated. The execution process of verifying the integrity and consistency of the data and generating the responsibility profile record is as follows;

[0071] Enter the responsibilities and permissions of each data owner into the dataset, according to formula V. new =V old +1, calculate the new version number. Where V old V represents the old version number. new This represents the updated version number. Detailed formula explanation and calculation derivation: Considering instances of data updates, the original version number of the data owner is set to 2. When responsibility information is updated, the version number should be increased according to the formula, resulting in a new version number of 3. The calculation process ensures that every data update is traceable, maintaining data integrity and consistency.

[0072] S103: The execution flow of recording each data owner's activity log, including data access, modification and security events, through the duty file record, links the activities with the data owner's personal file, establishes the data owner responsibility chain, verifies the traceability of activity records, and generates the data owner file index is as follows;

[0073] The system records the activity logs of each data owner through responsibility profiles. This process includes monitoring and recording the data owner's data access, modification, and security events. The recording of activity logs is automated. The system creates log entries in the database based on the data owner's actions. Each entry records the access time, operation type, and operator's identity information in detail. The system also links each log entry to the data owner's personal profile, establishing a clear chain of responsibility. The establishment of the chain of responsibility is crucial for tracking data activities, allowing organizations to quickly locate the responsible party and generate a data owner profile index in the event of data breaches or misoperations.

[0074] Please see Figure 3 Based on the data owner profile index, the specific steps for creating power equipment data governance tasks, automatically identifying data owners, assigning data verification and update tasks, assigning identifiers, and generating task assignment records are as follows:

[0075] S201: The execution flow of generating power equipment data owner matching records by using the data owner profile index to identify the person in charge of power equipment data and matching the identification results with the person in charge profile is as follows;

[0076] The system uses a data owner profile index to identify the person in charge of power equipment data. The process involves index retrieval and matching. The data owner profile index contains detailed information about each person in charge of power equipment data. The system searches for the person in charge of a specific data element through the index. The search algorithm filters the results based on parameters such as equipment type and equipment location. During the matching process with the person in charge profile, the system compares the person in charge's qualifications with the characteristics of the data to ensure the correctness of the match. After a successful match, the system records the person in charge's name, responsibilities, and the matched power equipment data, generating a power equipment data person in charge matching record.

[0077] S202: Based on the matching records of power equipment data responsible persons, automatically assign data verification and update tasks to the associated data responsible persons. Each task comes with instructions and deadlines, and the content of the task is recorded. The execution flow of the data management task is generated as follows;

[0078] Automatically assign data verification and update tasks to the associated data owners, according to the formula T = t start +d, calculates the task deadline. Where t... start d represents the start time of the task assignment, d represents the duration of the task in days, and T represents the deadline of the task. Detailed explanation and calculation derivation: Setting the task assignment start time to October 1st and the task duration to 30 days, the calculation shows the deadline is October 31st. This calculation ensures each task has a clear deadline, allowing those responsible to plan their work effectively and avoid exceeding the deadline.

[0079] S203: The execution flow of creating a unique identifier for each assigned task through data management tasks, recording the task creation time and responsible person information, verifying the tracking and management of each task, and generating task assignment records is as follows;

[0080] By managing data tasks, a unique identifier is created for each assigned task. Identifier generation is a crucial step, ensuring the uniqueness and traceability of task management. During the creation process, the system uses a combination of the current date and time and an encryption algorithm to generate a unique identifier. At the same time, the system records the task creation time and the person in charge information. The creation time is recorded using a timestamp to ensure the accuracy and immutability of the time. The accurate entry of the person in charge information is achieved by linking to the detailed profile of the person in charge in the database. The tracking and management of each task is verified through the system's log function, which monitors each stage of the task, records the changes in the status of each task, and generates task assignment records.

[0081] Please see Figure 4 The specific steps for analyzing power equipment information that failed data validation, comparing the data with authoritative data sources, marking discrepancies and missing data items, and generating a summary record of validation results are as follows:

[0082] S301: Based on the task allocation record, filter the power equipment information that failed the initialization data verification, record the person in charge and the problem points of the power equipment, and generate the initialization problem power equipment record. The execution flow is as follows:

[0083] Based on task allocation records, information on power equipment that failed initial data verification is filtered. The filtering process mainly relies on the automated detection function of the data verification system. The system checks the completeness and format correctness of each power equipment data. Data that fails the test will be marked and the problem points will be recorded, including missing data, errors, or inconsistencies. The system also records the person in charge information for each piece of equipment, including equipment ID, person in charge name, problem description, and relevant timestamps. This is the basis for subsequent verification and data quality improvement work. It is achieved by matching with the data owner profile index. Recording the person in charge and the problem points of power equipment provides a basis for subsequent problem solving and data quality improvement, and generates an initial problem power equipment record.

[0084] S302: The execution flow of initializing the problem power equipment record, performing comparative analysis on each data item, comparing it with authoritative data sources, marking data deviations and missing data, and generating deviation comparison records is as follows;

[0085] The data was compared with authoritative data sources using the formula D=∑|d i -s i |, calculate the total data deviation. Where, d i Data values ​​representing electrical equipment, s i The corresponding data value represents the authoritative data source, and D represents the total deviation. Detailed formula explanation and calculation derivation: Assume the voltage record of the power equipment is 220V, and the standard voltage value of the authoritative data source is 230V. According to the formula, the deviation of the data point is 10V. If there are a total of 5 similar data point deviations, the cumulative total deviation is 50V. The calculation process helps identify and quantify data deviation, providing an accurate measurement benchmark for data verification and correction.

[0086] S303: The execution flow for verifying the integrity of power equipment data quality control and generating a summary record of verification results by summarizing the marked deviations and missing data items through deviation comparison records, extracting key information and proposing improvement plans, and generating a summary record of verification results is as follows;

[0087] By comparing deviation records, the system summarizes and marks deviations and missing data items. The process involves batch data processing and analysis. The system automatically summarizes the deviations of all records, including numerical deviations and missing data. Key information is extracted for important data points that affect equipment performance and safety, such as key parameters like voltage and current. Improvement proposals are based on deviation data and the actual needs of equipment operation. Proposals include optimization of the data acquisition process and updates to calibration methods. The system verifies the integrity of power equipment data quality control by comparing and analyzing data records to ensure that each data item meets the established quality standards and generates a summary record of verification results.

[0088] Please see Figure 5 The specific steps for data governance and generating audit and adjustment records are as follows: Based on the summarized verification results, the actual situation is verified, issues requiring remediation are identified, data governance is completed, and audit and adjustment records are generated.

[0089] S401: Based on the summary record of verification results, identify multiple types of data anomalies and fluctuations, track and analyze the source of each type of data, extract key sources of deviation, and obtain a list of data problems. The execution process is as follows:

[0090] Extracting key sources of deviation from the aggregated verification results records involves data sources including system logs, user input records, and external data interfaces. This is crucial for identifying anomalies. For each type of data, volatility indicators and anomaly rates are calculated. For example, for user input records, the standard deviation of the daily average input can be calculated to identify abnormal fluctuations. For system logs, the daily variation of the error rate can be analyzed to detect potential problems. The analysis results will be used to trace the root cause of problematic data. During the identification process, the processing and analysis of each type of data must ensure the accuracy of the algorithm and the integrity of the data in order to accurately track the specific problems of each type of data and form a list of data problems.

[0091] S402: Based on the data problem list, set data governance measures, adjust the data for the identified problems, manually correct obvious errors, perform data processing and batch update operations, and obtain the data governance result summary. The execution flow is as follows:

[0092] Data governance is carried out based on the data problem list, according to formula P. adj =P orig +ΔP, calculate the adjusted data P adj In the formula, P orig ΔP represents the original data, and ΔP represents the adjustment amount made according to the problem list. Detailed formula explanation and calculation derivation: To achieve data accuracy and consistency, P... origDerived from the original data values ​​stored in the database, ΔP is determined by the error type identified in the data problem list and the specific correction required. If a data item is incorrectly increased by 10%, ΔP will be set to -10% of the original data to correct this error. (Example P) orig =100, if the error increases by 10%, then ΔP = -10. Substituting this into the formula, we get P. adj =100-10=90, indicating that the data adjustment process is clear and ensures data consistency.

[0093] S403: The execution process for reviewing and adjusting the dataset by summarizing the data governance results, verifying data consistency and integrity, checking that the dataset updates comply with business rules and requirements, and obtaining the review and adjustment records is as follows;

[0094] Through the data governance results summary, the adjusted dataset is reviewed. The process focuses on verifying data consistency and integrity, especially ensuring that the updated dataset meets business rules and requirements. The dataset review includes data integrity checks, consistency verification, and business rule compliance assessment. Data integrity checks ensure that all necessary fields are filled and no data items are missing. Consistency verification confirms that the data has not been illegally tampered with by comparing records before and after the data update. The business rule compliance assessment ensures that the data update conforms to business logic and operational requirements, ensuring the quality of the dataset and meeting business needs. In this way, the dataset update can reflect the effectiveness of data governance activities and provide effective support for further business decisions, resulting in a review and adjustment record.

[0095] Please see Figure 6 Based on the audit and adjustment records, the data flow during the operation of power equipment is monitored, and information entropy is calculated. If the information entropy is lower than the safety threshold, the abnormal time, data flow source, and security risk are recorded. The specific steps for generating the entropy value assessment result are as follows:

[0096] S501: The execution process of monitoring data flow during the operation of power equipment by auditing and adjusting records, calculating the information entropy of the data flow in real time, assessing the volatility of the data, and generating real-time entropy value monitoring records is as follows;

[0097] Monitor the data flow of power equipment during operation, calculate the information entropy of the data flow in real time, and apply the formula. Calculate the information entropy. Where p i H represents the probability of event i. i This represents information entropy. Detailed formula explanation and calculation derivation: Four data stream events are set up, each with an occurrence probability of 0.25. According to the formula, the information entropy contribution of each event is -0.25log20.25 = 0.5. The total information entropy H... iThe result is 4 × 0.5 = 2. The calculation process helps assess the volatility of the data stream. The level of information entropy can reveal the randomness and predictability of the data. Low information entropy indicates high predictability, while high information entropy indicates high randomness. The generated real-time entropy monitoring records will be used for further data security analysis.

[0098] S502: Based on real-time entropy monitoring records, using sliding window technology, calculate the information entropy of each data stream and compare it with the set safety threshold. Identify and record the abnormal time and source of data streams below the threshold, and generate low entropy abnormal records. The execution process is as follows:

[0099] The formula for the sliding window technique is as follows:

[0100]

[0101] Where E(t) is the weighted average entropy value within a given time window W, t represents the number of time points, and H i This represents the information entropy at time point i. α represents the average information entropy of all data points within the sliding window, W represents the width of the sliding window, α represents the square root weighting coefficient, β represents the relative value weighting coefficient, and γ represents the adjustment coefficient.

[0102] formula:

[0103]

[0104] The formula is used to calculate the adjusted information entropy E(t) within a time window t. The sliding window W is set to 5, indicating that the calculation includes data from the current time point and the previous four time points. Real-time information entropy H i The information entropy was calculated by extracting data streams from the system log and using the Shannon entropy formula. The information entropy values ​​from t-4 to t were set to 1.2, 1.5, 1.4, 1.6, and 1.3, respectively. The average information entropy of all data points within the sliding window was calculated. Right now

[0105] The weighting parameters α, β, and adjustment coefficient γ are set to 0.7, 0.3, and 0.1, respectively. The setting of α is based on the need to enhance the influence of the square root weighting of entropy deviation, suitable for data flow environments more sensitive to abrupt changes. β is used to strengthen the relative change of information entropy, allowing the calculation results to better reflect the volatility of entropy. γ adjusts the baseline to adapt to different data flow characteristics.

[0106] The calculation process is as follows:

[0107] 1. For each i from t-W+1 to t, calculate The values ​​are 0.2, 0.1, 0, 0.2, and 0.1 respectively.

[0108] 2. Apply weights and adjustment coefficients:

[0109] for

[0110] for

[0111] 3. Calculate the adjusted entropy value at each time point:

[0112]

[0113] The results show that, within a given time window t, the weighted average adjustment value of the information entropy is 0.742, which is lower than... It shows small changes, but by adjusting the weights and adjustment coefficients, the entropy fluctuations can be observed in more detail, which can be further used to detect potential anomalies.

[0114] S503: The execution flow for analyzing the potential security risks of each abnormal data stream through low entropy value anomaly records, summarizing risk factors and improvement measures, verifying the security and integrity of power equipment data, and generating entropy value assessment results is as follows;

[0115] By analyzing low-entropy anomaly records, the potential security risks of each anomalous data stream are identified. This involves in-depth analysis of the anomalous low-entropy data streams to identify potential data consistency and security issues. Low entropy values ​​indicate a large amount of repetitive or predictable data in the stream, caused by system errors, data tampering, or external interference. The analysis process includes identifying specific patterns in the anomalous data streams, comparing them with normal operating data, and comparing them with security event records. The steps for summarizing risk factors and improvement measures include determining the strengthening of data protection measures, optimization of data processing workflows, and the establishment of early warning systems. Verification of the security and integrity of power equipment data is achieved by implementing improvement measures and monitoring their effectiveness, ultimately generating an entropy value assessment result.

[0116] Please see Figure 7 Based on the entropy value assessment results, the data security risks are analyzed, encryption measures are adjusted according to the data's sensitivity and risk level, and the data security solution is generated through continuous tracking of encryption effectiveness. The specific steps are as follows:

[0117] S601: Using the entropy value assessment results, the security risks in the data stream are prioritized, and the encryption measures are adjusted according to the sensitivity and risk level of the data to match the security requirements of different levels. The execution flow of the adjusted encryption strategy is as follows.

[0118] Using entropy assessment results, security risks in the data stream are prioritized. This process involves classifying each security risk in the entropy assessment results based on the degree of damage it causes and the probability of its occurrence. Prioritization considers the sensitivity and risk level of the data; highly sensitive data or high-risk issues are addressed first. The prioritized results are then used to adjust encryption measures to match differentiated security requirements. For example, for extremely sensitive data, stronger encryption technologies such as AES-256 are used, while for less sensitive data, lighter encryption measures such as AES-128 are used. This differentiated security strategy ensures the rational allocation of resources while guaranteeing data security, generating an adjusted encryption strategy.

[0119] S602: Based on the adjusted encryption strategy, implement new encryption measures, including adjusting the key strength and changing the encryption method, compare the changes in security performance before and after encryption, and record the time and effect of each adjustment. The execution flow for generating the encryption strategy implementation record is as follows;

[0120] Implement new encryption measures and calculate the encryption strength according to the formula E = k × S. In this formula, k represents the key length, S represents the security level coefficient, and E represents the encryption strength. Detailed explanation and derivation of the formula: Set the selected key length to 256 bits and the security level coefficient to 1.5 (corresponding to highly sensitive data). According to the formula, the encryption strength E is 384. This calculation helps determine the security performance of the encryption measures, ensuring the confidentiality and security of the data. The generated encryption strategy implementation record details the time of each adjustment, the encryption strength before and after the adjustment, and the effect, providing data support for verifying the encryption effect and further strategy adjustments.

[0121] S603: The execution process for generating a data security solution is as follows: through the implementation of encryption strategies, the effectiveness of new encryption measures is continuously monitored and evaluated, the integrity and confidentiality of data streams are verified, key adjustments and improvements are recorded, and the following data security solution is generated.

[0122] By documenting the implementation of encryption strategies, the effectiveness of new encryption measures is continuously monitored and evaluated. This involves analyzing data from the implementation records to determine the effectiveness and feasibility of the encryption measures. The monitoring process includes checking the integrity and confidentiality of data flows to ensure no unauthorized access or data breaches occur. Key adjustments and improvements are documented to continuously enhance data security. The effectiveness of new encryption measures is evaluated by comparing changes in security performance before and after encryption, such as reducing data breach rates or increasing the difficulty of authorizing data access. The implementation details, effectiveness assessments, and future security plans of the encryption strategies are detailed to guide enterprise data security management and strategy optimization, ultimately generating a data security solution.

[0123] Please see Figure 8A power equipment data intelligent governance system based on data owner responsibility chain, the power equipment data intelligent governance system based on data owner responsibility chain is used to execute the above-mentioned power equipment data intelligent governance method based on data owner responsibility chain, the system includes:

[0124] The data registration module collects data on managers of power equipment, records the responsibilities, permissions, and activity logs of each data owner, integrates the information, and generates a data owner profile index.

[0125] The identifier allocation module identifies the data owner based on the data owner profile index, automatically allocates data verification and update tasks, assigns an identifier to each task, and obtains task allocation records.

[0126] The data verification module uses task allocation records to check power equipment data items, compares them with authoritative data sources, marks deviations and missing parts in the data, and generates a summary record of verification results.

[0127] The data analysis module summarizes and records the verification results, manually reviews the data items of the power equipment, determines the data content that needs to be corrected, identifies the problems that need to be addressed, and obtains the audit and adjustment records.

[0128] The safety monitoring module monitors the operating data of power equipment based on the audit and adjustment records, calculates the information entropy value, marks data anomalies below the safety threshold, records the time of the anomalies and the source of the data, analyzes potential safety risks, and formulates data security solutions.

[0129] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A power equipment data intelligent governance method based on a data owner responsibility chain, characterized in that, The method comprises the following steps: record the data responsible person information of the data governance object, establish the data owner responsibility chain, record the responsibilities, permissions and activity records of each data owner, and generate the data owner profile index; based on the data owner profile index, create the power equipment data governance task, automatically identify the data responsible person, assign the data verification and update task, and assign the identifier, and generate the task assignment record; using the task assignment record, analyze the power equipment information that does not pass the data verification, compare the data with the authoritative data source, mark the deviation and missing data items, and generate the verification result summary record; according to the verification result summary record, verify the actual situation according to the verification result, determine the problems that need to be governed, complete the data governance, and generate the audit adjustment record; according to the audit adjustment record, monitor the data flow in the operation of the power equipment, calculate the information entropy, and if the information entropy is lower than the safety threshold, record the abnormal time, data flow source and safety risk, and generate the entropy value evaluation result; based on the entropy value evaluation result, analyze the data security risk, adjust the encryption measures according to the sensitivity and risk level of the data, and generate the data security scheme by continuously tracking the encryption effect; the step of monitoring the data flow in the operation of the power equipment according to the audit adjustment record, calculating the information entropy, and if the information entropy is lower than the safety threshold, recording the abnormal time, data flow source and safety risk, and generating the entropy value evaluation result is specifically: using the audit adjustment record, monitoring the data flow of the power equipment in the running process, calculating the information entropy of the data flow in real time, evaluating the volatility of the data, and generating the real-time entropy value monitoring record; based on the real-time entropy value monitoring record, using the sliding window technology, calculating the information entropy of each data flow, and comparing it with the set safety threshold, identifying and recording the abnormal time and source of the data flow below the threshold, and generating the low-entropy abnormal record; through the low-entropy abnormal record, analyzing the potential safety risk of each abnormal data flow, summarizing the risk factors and improvement measures, verifying the safety and integrity of the power equipment data, and generating the entropy value evaluation result; the formula of the sliding window technology is as follows: ； wherein, is a weighted average entropy value within a given time window , is the number of time points, is the information entropy at time point , is the average information entropy of all data points within the sliding window, is the width of the sliding window, is the root weighting coefficient, is the relative value weighting coefficient, is the adjustment coefficient.

2. The power equipment data intelligent governance method based on the data owner responsibility chain according to claim 1, characterized in that, the data owner profile index includes the responsibilities, permissions, activity records and personal information index of each data owner, the task assignment record includes the identification information of the data responsible person, the information of the assigned data verification and update task, and the identifier of the task, the verification result summary record includes the deviation, missing data items in the data verification process of the power equipment information and the comparison result with the authoritative data source, the audit adjustment record includes the description of the data governance problem, the record of the actual verification situation, the adjustment measures taken, the data state after adjustment and the time stamp, the entropy value evaluation result includes the recorded abnormal time, data flow source, information entropy calculation value and safety risk level, and the data security scheme includes the encryption measures adjusted according to the sensitivity and risk level of the data, the continuous tracking of the encryption effect evaluation and the safety update plan.

3. The data master responsibility chain-based power equipment data intelligent management method of claim 1, wherein, The step of recording the data responsible person information of the data governance object, establishing a data owner responsibility chain, recording the responsibility, permission and activity record of each data owner, and generating a data owner profile index is specifically as follows: Based on the data governance object information, the name, department and role of the data owner are entered into the target data set, a unique identifier is assigned to each record, and the creation and update timestamps are recorded, and a data owner basic profile is generated; Using the data owner basic profile, the responsibilities and permissions of each data owner are entered into the data set, and the modified timestamp and version number are recorded synchronously at each data update, the data integrity and consistency are checked, and a responsibility profile record is generated; Through the responsibility profile record, the activity log of each data owner is recorded, including data access, modification and security events, the activity is linked to the personal profile of the data owner, a data owner responsibility chain is established, the tracking of the activity record is verified, and a data owner profile index is generated.

4. The data master responsibility chain-based power equipment data intelligent management method of claim 1, wherein, Based on the data owner profile index, a power equipment data governance task is created, the data responsible person is automatically identified, the data verification and update task is assigned, and an identifier is assigned, and the step of generating a task assignment record is specifically as follows: Using the data owner profile index, the responsible person of the power equipment data is identified, and a power equipment data responsible person matching record is generated by matching the identification result with the responsible person profile; Based on the power equipment data responsible person matching record, the data verification and update task is automatically assigned to the associated data responsible person, each task is accompanied by instructions and a deadline, and the content of the task is recorded, and a data management task is generated; Through the data management task, a unique identifier is created for each assigned task, the creation time and responsible person information of the task are recorded, the tracking management of each task is verified, and a task assignment record is generated.

5. The data master responsibility chain-based power equipment data intelligent management method of claim 1, wherein, Using the task assignment record, the power equipment information that does not pass the data verification is analyzed, the data is compared with the authoritative data source, the deviated and missing data items are marked, and the step of generating a verification result summary record is specifically as follows: Based on the task assignment record, the power equipment information that does not pass the initial data verification is filtered, the responsible person and problem point of the power equipment are recorded, and an initial problem power equipment record is generated; Using the initial problem power equipment record, each item of data is analyzed by comparison, and compared with the authoritative data source, the deviation and missing of the data are marked, and a deviation comparison record is generated; Through the deviation comparison record, the marked deviation and missing data items are summarized, the key information is extracted and the improvement scheme is proposed, the integrity of the power equipment data quality control is verified, and a verification result summary record is generated.

6. The data master responsibility chain-based power equipment data intelligent management method of claim 1, wherein, Through the verification result summary record, according to the verification result, the actual situation is verified, the problems that need to be governed are determined, the data governance is completed, and the step of generating an audit adjustment record is specifically as follows: Based on the verification result summary record, multiple types of data anomalies and volatility are identified, the source of each type of data is tracked and analyzed, the key deviation source is extracted, and a data problem list is obtained; According to the data problem list, set data governance measures, adjust data for identified problems, manually correct obvious errors, perform data processing and batch update operations, and obtain a data governance result summary; Through the data governance result summary, review the adjusted data set, verify data consistency and integrity, check the update of the data set in accordance with business rules and requirements, and obtain an audit adjustment record.

7. The data master responsibility chain-based power equipment data intelligent management method of claim 1, wherein, Based on the entropy evaluation result, analyze data security risks, adjust encryption measures according to the sensitivity and risk level of the data, and generate a data security scheme by continuously tracking the encryption effect, the steps being: Using the entropy evaluation result, prioritize the security risks in the data stream, adjust the encryption measures according to the sensitivity and risk level of the data, match the differentiated level of security requirements, and generate an adjusted encryption strategy; Based on the adjusted encryption strategy, implement new encryption measures, including adjusting key strength and changing encryption methods, compare the security performance changes before and after encryption, and record the time and effect of each adjustment, and generate an encryption strategy implementation record; Through the encryption strategy implementation record, continuously monitor and evaluate the effect of the new encryption measures, check the integrity and confidentiality of the data stream, record key adjustments and improvement measures, and generate a data security scheme.

8. A power equipment data intelligent governance system based on a data owner responsibility chain, characterized in that, The data owner responsibility chain-based intelligent management method for power equipment data according to any one of claims 1-7, the system comprising: The data registration module collects the data of the managers of the power equipment, records the responsibilities, permissions and activity logs of each data owner, integrates the information, and generates a data owner profile index; The identifier allocation module identifies the data responsible person based on the data owner profile index, automatically allocates data verification and update tasks, and allocates an identifier for each task to obtain a task allocation record; The data verification module checks the power equipment data items using the task allocation record, compares with the authoritative data source, marks the deviations and missing parts in the data, and generates a verification result summary record; The data analysis module manually reviews the data items of the power equipment through the verification result summary record, determines the data content that needs to be corrected, determines the problems that need to be governed, and obtains an audit adjustment record; The security monitoring module monitors the operation data of the power equipment based on the audit adjustment record, calculates the information entropy value, marks the data anomaly events below the security threshold, records the abnormal time and data source, analyzes the potential security risks, and formulates a data security scheme.

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