Intelligent management method and system for power equipment data based on chain of responsibility of data owner

Abstract

The invention relates to the technical field of data management, in particular to an intelligent power equipment data management method and system based on a data owner responsibility chain, and the method comprises the following steps: recording the information of a data principal of a data management object, building the data owner responsibility chain, recording the responsibility, authority and activity record of each data owner; and generating a data owner file index. According to the method, the efficiency and accuracy of task execution are enhanced by distributing the data verification and updating tasks of the power equipment, human errors and delay are reduced, in the data verification link, data are compared with an authoritative data source, deviation and missing data items are effectively marked, the data items needing to be updated and corrected are determined through manual auditing, and the data verification efficiency is improved. The data quality and the updating timeliness are ensured, the abnormity in the data flow is found in time, the monitoring and the safety of the equipment are enhanced, the data management efficiency of the power equipment is remarkably improved, and the high quality and the efficient purpose of data management are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a method and system for intelligent management of power equipment data based on a data owner responsibility chain. Background Art

[0002] The field of data governance technology refers to a series of strategies and practices for the precise management and control of data throughout its life cycle, including data quality, data management, data policy formulation, compliance and security, etc., with the aim of ensuring the accuracy, accessibility, consistency and protection of data. 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 laws and regulations. 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 of power equipment data with the data owner responsibility chain refers to managing and optimizing the data processing and application of power equipment by building a clear responsibility chain, improving the operation and maintenance efficiency of power equipment and the effect of data use. It is used in the power industry to ensure that the integration, analysis and utilization of equipment data can be carried out under a strict management framework to achieve 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 to cope with complex regulatory requirements and improve system transparency.

[0004] Existing data governance technologies lack personalized management strategies for specific industries or specific equipment, resulting in the efficiency and effectiveness of data governance not being able to fully meet specific needs. For example, in the power industry, the huge amount and complexity of equipment data require more detailed and professional processing methods, and traditional data governance cannot effectively handle complex data in specific scenarios. Existing technologies rely on manual operations in the allocation of data verification and update tasks, which not only increases the workload but also increases the risk of errors. In terms of data security, the lack of dynamic risk assessment and corresponding security adjustment measures makes data face greater security threats. For example, failure to promptly detect anomalies in data flows leads to safety accidents, affecting the stable operation of the entire power system. The shortcomings lead to limited effectiveness of data governance in actual operations, and the inability to fully tap the potential value of data, while increasing operational risks. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a method and system for intelligent management of power equipment data based on the data owner responsibility chain.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution, a method for intelligent management of power equipment data based on data owner responsibility chain, comprising the following steps:

[0007] S1: Record the data owner 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 file 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, analyzing the power equipment information that has not passed the data verification, comparing the data with the authoritative data source, marking the deviations and missing data items, and generating a summary record of the verification results;

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

[0011] S5: 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 security risk, and generate an entropy value assessment result;

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

[0013] As a further solution 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 assignment record includes the identification information of the data owner, the information of the assigned data verification and update tasks, and the task identifier, the verification result summary record includes the deviations, missing data items and comparison results with the authoritative data source in the data verification process of the power equipment information, the audit adjustment record includes a description of the data governance issue, a record of the actual verification situation, the adjustment measures taken, the adjusted data status and timestamp, the entropy value assessment result includes the recorded abnormal time, the source of the data flow, the information entropy calculation value and the security risk level, and the data security plan includes encryption measures adjusted according to the sensitivity and risk level of the data, continuously tracked encryption effect evaluation and security update plan.

[0014] As a further solution of the present invention, the steps of recording the data owner 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 data set, assign a unique identifier to each record, and record the creation and update timestamps to generate a basic file of the data owner;

[0016] S102: Using the data owner basic file, enter the responsibilities and permissions of each data owner into the data set, synchronously record the modified timestamp and version number each time the data is updated, check the data integrity and consistency, and generate a responsibility file record;

[0017] S103: Record the activity log of each data owner through the responsibility profile record, including data access, modification and security events, link the activities with the personal profile of the data owner, establish the data owner responsibility chain, verify the traceability of the activity record, and generate the data owner profile index.

[0018] As a further solution of the present invention, based on the data owner profile index, a power equipment data governance task is created, the data owner is automatically identified, data verification and update tasks are assigned, and identifiers are assigned. The steps of generating task assignment records are specifically as follows:

[0019] S201: using the data owner file index to identify the person in charge of the power equipment data, and generating a matching record of the person in charge of the power equipment data by matching the identification result with the person in charge file;

[0020] S202: Based on the matching record of the power equipment data person in charge, automatically assign data verification and update tasks to the associated data person in charge, with each task accompanied by instructions and deadlines, and recording the content of the task, to generate a data management task;

[0021] S203: Through the data management task, a unique identifier is created for each assigned task, the creation time and the person in charge information of the task are recorded, the tracking management of each task is verified, and a task assignment record is generated.

[0022] As a further solution of the present invention, the steps of using the task allocation record, analyzing the power equipment information that has not passed the data verification, comparing the data with the authoritative data source, marking the deviations and missing data items, and generating the verification result summary record are specifically as follows:

[0023] S301: Based on the task allocation record, filter the information of the power equipment that has not passed the initialization data verification, record the person in charge and the problem point of the power equipment, and generate an initialization problem power equipment record;

[0024] S302: using the initialized problematic power equipment record, performing comparative analysis on each data item, comparing it with the authoritative data source, marking the deviation and missing of the data, and generating a deviation comparison record;

[0025] S303: Through the deviation comparison record, the marked deviations and missing data items are summarized, key information is extracted and improvement plans are proposed, the integrity of the power equipment data quality control is verified, and a verification result summary record is generated.

[0026] As a further solution of the present invention, the steps of summarizing the verification results and recording them, verifying the actual situation based on the verification results, determining the problems that need to be managed, completing data management, and generating the audit adjustment records are specifically as follows:

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

[0028] S402: According to the data problem list, set data governance measures, make data adjustments 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: Review the adjusted data set through the data governance result summary, verify data consistency and completeness, check whether the update of the data set complies with business rules and requirements, and obtain the review adjustment record.

[0030] As a further solution of the present invention, according to the audit adjustment record, the data flow in the operation of the power equipment is monitored, and the information entropy is calculated. If the information entropy is lower than the safety threshold, the abnormal time, the source of the data flow and the safety risk are recorded. The steps of generating the entropy value evaluation result are specifically as follows:

[0031] S501: using the audit adjustment record to monitor the data flow of the power equipment during operation, calculating the information entropy of the data flow in real time, evaluating the volatility of the data, and generating a real-time entropy value monitoring record;

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

[0033] S503: Analyze the potential security risks of each abnormal data flow through the low entropy value abnormal records, summarize the risk factors and improvement measures, verify the security and integrity of the power equipment data, and generate an entropy value evaluation result.

[0034] As a further solution of the present invention, the formula of the sliding window technology 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 represents the information entropy at time point i, represents the average information entropy of all data points in 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 solution of the present invention, based on the entropy value evaluation result, the data security risk is analyzed, the encryption measures are adjusted according to the sensitivity and risk level of the data, and the steps of generating a data security solution by continuously tracking the encryption effect are specifically as follows:

[0038] S601: using the entropy value evaluation result, prioritizing the security risks in the data stream, adjusting the encryption measures with reference to the sensitivity and risk level of the data, matching the security requirements of the differentiated levels, and generating an adjusted encryption policy;

[0039] S602: Based on the adjusted encryption strategy, implement new encryption measures, including adjusting key strength and changing encryption mode, comparing security performance changes before and after encryption, and recording the time and effect of each adjustment, and generating encryption strategy implementation records;

[0040] S603: Through the implementation record of the encryption strategy, continuously monitor and evaluate the effectiveness of new encryption measures, check the integrity and confidentiality of data flows, record key adjustments and improvement measures, and generate data security solutions.

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

[0042] The data registration module collects the manager data 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] The identifier allocation module identifies the data owner based on the data owner profile index, automatically allocates data verification and update tasks, allocates an identifier to each task, and obtains a task allocation record;

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

[0045] The data analysis module manually reviews the data items of the power equipment through the summary record of the verification results, determines the data content that needs to be corrected, determines the problems that need to be addressed, and obtains the audit adjustment record;

[0046] The security monitoring module monitors the operating data of the power equipment, calculates the information entropy value, marks data abnormal events below the safety threshold, records the abnormal time and data source, analyzes potential security risks, and formulates a data security plan based on the audit adjustment records.

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

[0048] In the present invention, by accurately recording the information of the person in charge of the power equipment data and establishing a clear chain of responsibility, the responsibility traceability and transparency of data governance are improved. The responsibilities, permissions and activities of each data owner are recorded, and the responsibility allocation is optimized, so that the person in charge of each link is clear and traceable. When creating a data governance task for power equipment, the data person in charge is automatically identified and data verification and update tasks are assigned to enhance the efficiency and accuracy of task execution and reduce human errors and delays. In the data verification link, the data is compared with the authoritative data source to effectively mark deviations and missing data items, directly improving the accuracy and reliability of the data. The data items that need to be updated and corrected are determined by manual review to ensure the quality of the data and the timeliness of the update. Monitoring the data flow in the operation of power equipment and calculating the information entropy can timely discover anomalies in the data flow and strengthen the monitoring and security of the equipment. By analyzing data security risks and adjusting encryption measures according to data sensitivity, data protection is strengthened, security risks are reduced, and the data governance efficiency of power equipment is significantly improved to ensure the high quality and efficient use of data governance. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

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

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

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a method for intelligent management of power equipment data based on a data owner responsibility chain, comprising the following steps:

[0060] S1: Record the information of the data owner 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 person in charge of the associated equipment data according to the selection, assign data verification and update tasks, assign identifiers to the tasks, and generate task assignment records;

[0062] S3: Use task allocation records to analyze the power equipment information that has not passed data verification, compare the data with the authoritative data source, mark deviations and missing data items, and generate a summary record of verification results;

[0063] S4: Summarize and record 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 during the review process, and generate the review adjustment record;

[0064] S5: According to the audit adjustment records, monitor the data flow in the operation of the power equipment, calculate the information entropy of the data packet, and if the information entropy is lower than the safety threshold, record the abnormal time, data flow source and security risk, and generate the entropy value assessment result;

[0065] S6: Based on the entropy assessment results, analyze data security risks, adjust encryption measures according to the sensitivity and risk level of the data, implement protection protocols, and generate data security plans by continuously tracking encryption effects and verifying the effectiveness of adjustment measures.

[0066] The data owner profile index includes the index of each data owner's responsibilities, permissions, activity records and personal information. The task assignment record includes the identification information of the data owner, the information of the assigned data verification and update tasks, and the task identifier. The verification result summary record includes the deviations in the data verification process of the power equipment information, missing data items and the comparison results with the authoritative data source. The audit adjustment record includes the description of the data governance issue, the record of the actual verification situation, the adjustment measures taken, the adjusted data status and timestamp. The entropy assessment results include the recorded abnormal time, the source of the data flow, the calculated information entropy value and the security risk level. The data security plan includes encryption measures adjusted according to the sensitivity and risk level of the data, the continuous tracking of encryption effect evaluation and the security update plan.

[0067] See also Figure 2 , record the data owner 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 in the following steps:

[0068] S101: Based on the data governance object information, enter the name, department and role of the data owner into the target data set, assign a unique identifier to each record, and record the timestamps of creation and update. The execution process of generating the basic file of the data owner is as follows;

[0069] Based on the data governance object information, the name, department and role of the data owner are entered into the target data set. The process involves multiple steps. The system needs to extract the personal information of the data owner from the data governance object information. The information includes name, department and role. After the information is extracted, data cleaning and format standardization are performed to meet the database entry requirements. The system generates a unique identifier for each record, which involves an algorithm, such as the UUID generation algorithm, to ensure the uniqueness of each data owner's record in the data set. At the same time, the system records the creation and update timestamps of each piece of data. The timestamp is generated using the server's system time to ensure the accuracy of the recorded time. The entire process is automated to ensure efficient and accurate data entry and generate a basic file for the data owner.

[0070] S102: Use the data owner basic file to enter the responsibilities and permissions of each data owner into the data set, synchronously record the modified timestamp and version number when each data is updated, check the data integrity and consistency, and generate the execution process of the responsibility file record as follows;

[0071] Enter the responsibilities and permissions of each data owner into the data set, according to formula V new =V old +1, calculate the new version number. In the formula, V old Represents the old version number, V new Represents the updated version number. Detailed explanation of the formula and the process of formula calculation: Considering the data update instance, the original version number of the data owner is set to 2. When the responsibility information is updated, the version number should be increased according to the formula, and the new version number is 3. The calculation process ensures that each data update can be traced to maintain the integrity and consistency of the data.

[0072] S103: Record the activity log of each data owner through the responsibility file record, including data access, modification and security events, link the activities with the personal file of the data owner, establish the data owner responsibility chain, verify the traceability of the activity record, and generate the execution process of the data owner file index as follows;

[0073] Through responsibility file records, the activity log of each data owner is recorded. The process includes monitoring and recording the data owner's data access, modification and security events. The activity log is recorded automatically. The system creates log entries in the database according to the data owner's operations. The entries record the access time, operation type and operator's identity information in detail. The system also links each log to the data owner's personal file to establish a clear chain of responsibility. The establishment of a chain of responsibility is crucial to tracking data activities. It allows organizations to quickly locate the person responsible and generate a data owner file index when data leakage or misoperation occurs.

[0074] See also Figure 3 ,Based on the data owner profile index, create power equipment data governance tasks, automatically identify data owners, assign data verification and update tasks, and assign identifiers. The specific steps for generating task assignment records are:

[0075] S201: Using the data owner file index, identifying the person in charge of the power equipment data, and matching the identification result with the person in charge file to generate the matching record of the person in charge of the power equipment data is performed as follows;

[0076] The data owner profile index is used to identify the person in charge of the power equipment data. The process involves index retrieval and matching. The data owner profile index contains detailed information about each person in charge of the 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. In the process of matching 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 name, responsibilities and matched power equipment data of the person in charge are recorded to generate a matching record of the person in charge of the power equipment data.

[0077] S202: Based on the matching records of the data responsible persons of the power equipment, data verification and update tasks are automatically assigned to the relevant data responsible persons. Each task is accompanied by instructions and deadlines, and the content of the task is recorded. The execution process of generating data management tasks is as follows;

[0078] Automatically assign data verification and update tasks to the relevant data responsible person, according to the formula T = t start +d, calculate the task deadline. In the formula, t start 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. Formula explanation and formula calculation derivation process: Set the start time of the task assignment to October 1, and the task duration to 30 days. According to the formula, the deadline of the task is October 31. The calculation ensures that each task has a clear deadline, so that the person in charge can arrange his or her work plan reasonably and avoid overdue.

[0079] S203: Through the data management task, a unique identifier is created for each assigned task, the creation time and the person in charge information of the task are recorded, the tracking management of each task is verified, and the execution process of generating the task assignment record is as follows;

[0080] Through data management tasks, a unique identifier is created for each assigned task. The generation of the identifier is a key step to ensure the uniqueness and traceability of task management. During the creation process, the system will use the current date and time combination encryption algorithm to generate a unique identifier, and record the creation time and person in charge information of the task. 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 linked to the detailed file of the person in charge through the database. The tracking 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] See also Figure 4 , using task allocation records, analyzing the power equipment information that failed data verification, comparing the data with the authoritative data source, marking deviations and missing data items, and generating a summary record of verification results are as follows:

[0082] S301: Based on the task allocation record, screen the power equipment information that has not passed the initialization data verification, record the person in charge and the problem point of the power equipment, and generate the initialization problem power equipment record. The execution process is as follows;

[0083] Based on the task allocation records, the power equipment information that fails the initialization data verification is screened. The screening process mainly relies on the automated detection function of the data verification system. The system checks the integrity 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, incorrect or inconsistent data. The system also records the person in charge of each device, including the device ID, name of the person in charge, problem description and related timestamps. This is the basis for subsequent verification and data quality improvement work, which is achieved by matching with the data owner file index. Recording the person in charge and the problem points of the power equipment is to provide a basis for subsequent problem solving and data quality improvement, and to generate initialization problem power equipment records.

[0084] S302: Using the initialization problem power equipment record, performing a comparative analysis on each data item, comparing it with the authoritative data source, marking the deviation and missing of the data, and generating the deviation comparison record. The execution process is as follows;

[0085] Compare the data with the authoritative data source and use the formula D = ∑|d i -s i |, calculate the total deviation of the data. In the formula, d i Represents the data value of the power device, s i represents the corresponding data value of the authoritative data source, and D represents the total deviation. Detailed explanation of the formula and the process of formula calculation and derivation: Set the voltage value of the power equipment to 220V, and the standard voltage value of the authoritative data source to 230V. According to the formula, the deviation of the data point is 10V. If there are 5 similar data point deviations in total, the accumulated total deviation is 50V. The calculation process helps identify and quantify data deviations, providing an accurate measurement benchmark for data verification and correction.

[0086] S303: Through the deviation comparison record, the marked deviations and missing data items are summarized, key information is extracted and improvement plans are proposed, the integrity of the power equipment data quality control is verified, and the execution process of generating the verification result summary record is as follows;

[0087] Through the deviation comparison records, the marked deviations and missing data items are summarized. The steps involve batch data processing and analysis. The system automatically summarizes all recorded deviations, including numerical deviations and data missing. The extraction of key information targets important data points that affect equipment performance and safety, such as voltage, current and other key parameters. Improvement plans are proposed based on deviation data and the actual needs of equipment operation. Proposals include optimization of data collection process, updating of calibration methods, etc. The system verifies the integrity of power equipment data quality control by comparing and analyzing data records to ensure that each data meets the established quality standards and generates a summary record of verification results.

[0088] See also Figure 5 , summarize the verification results and record them, verify the actual situation based on the verification results, determine the issues that need to be governed, complete data governance, and generate audit adjustment records. The specific steps are:

[0089] S401: Based on the verification result summary record, identify multiple types of data anomalies and volatility, track and analyze the source of each type of data, extract the key sources of deviation, and obtain the execution process of the data problem list as follows;

[0090] Extracting key sources of deviation from the verification result summary records involves data sources such as system logs, user input records, and external data interfaces, which are crucial to identifying anomalies. For each type of data, calculate volatility indicators and abnormal ratios. For example, for user input records, the standard deviation of the average daily input can be calculated to identify abnormal fluctuations. For system logs, the daily changes in error rates can be analyzed to detect potential problems. The analysis results will be used to track the root causes of problematic data. During the identification process, the processing and analysis of various types of data need to ensure the accuracy of the algorithm and the integrity of the data, so as to accurately track the specific problems of each type of data and form a list of data problems.

[0091] S402: According to the data problem list, data governance measures are set, data adjustments are made for the identified problems, obvious errors are manually corrected, data processing and batch update operations are performed, and the execution process for obtaining a summary of data governance results is as follows;

[0092] Carry out data governance according to the data problem list and follow the formula P adj =P orig +ΔP, calculate the adjusted data P adj In the formula, P orig represents the original data, and ΔP represents the adjustment amount based on the problem list. Detailed explanation of the formula and the process of formula calculation and derivation: To achieve data accuracy and consistency, P origTaken from the original data values ​​stored in the database, ΔP is determined by the type of error identified in the data problem list and the specific circumstances of the 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. orig =100, the error increases by 10%, then ΔP = -10, substituting into the formula to get P adj =100-10=90, which shows that the data adjustment process is clear and ensures data consistency.

[0093] S403: Review the adjusted data set through the data governance result summary, verify the data consistency and completeness, check whether the update of the data set complies with business rules and requirements, and obtain the execution process of the review adjustment record as follows;

[0094] Through the summary of data governance results, the adjusted data set is reviewed. The process focuses on the verification of data consistency and integrity, especially the updated data set must meet business rules and requirements. The review of the data set includes data integrity check, consistency verification and business rule compliance assessment. The data integrity check ensures that all necessary fields are filled and there are no missing data items. The consistency verification confirms that the data has not been illegally tampered with by comparing the records before and after the data. The business rule compliance assessment ensures that the data update complies with business logic and operational requirements, ensures the quality of the data set, and meets business needs. In this way, the update of the data set can reflect the effectiveness of data governance activities, and provide effective support for further business decisions and obtain audit adjustment records.

[0095] See also Figure 6 ,According to the audit adjustment records, the data flow in the operation of the power equipment is monitored, and the 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 evaluation result are:

[0096] S501: The execution process of using audit adjustment records to monitor the data flow of power equipment during operation, calculating the information entropy of the data flow in real time, evaluating 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 use the formula Calculate information entropy. In the formula, p i represents the probability of event i, H i Represents information entropy. Detailed explanation of the formula and the process of formula calculation: There are four data stream events, and the probability of each event is 0.25. According to the formula, the information entropy contribution of each event is -0.25log20.25=0.5. The total information entropy H i4×0.5=2. The calculation process helps evaluate the volatility of data flow. The level of information entropy can be used to understand the randomness and predictability of data. Low information entropy indicates high predictability of data, while high information entropy indicates high randomness of data. The generated real-time entropy value monitoring records will be used for further data security analysis.

[0098] S502: Based on the real-time entropy monitoring record, the sliding window technology is used to calculate the information entropy of each data flow, and compare it with the set security threshold, identify and record the abnormal time and source of the data flow below the threshold, and generate the low entropy abnormal record. 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 represents the information entropy at time point i, represents the average information entropy of all data points in 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 the time window t. The sliding window W is determined to be 5, which means that the calculation includes the data of the current and previous four time points. Real-time information entropy H i It is calculated by extracting data streams from system logs and using the Shannon entropy formula. The information entropy values ​​from t-4 to t are set to 1.2, 1.5, 1.4, 1.6, and 1.3 respectively. Calculate the average information entropy of all data points in the sliding window Right now

[0105] The weight 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 weighted effect of the square root of the entropy deviation, which is suitable for data flow environments that are more sensitive to mutations. β is used to strengthen the relative change of information entropy so that the calculation results can 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 results are 0.2, 0.1, 0, 0.2, and 0.1 respectively.

[0108] 2. Application weights and adjustment coefficients:

[0109] for

[0110] for

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

[0112]

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

[0114] S503: Analyze the potential security risks of each abnormal data flow through low entropy value abnormal records, summarize risk factors and improvement measures, verify the security and integrity of power equipment data, and generate the execution process of entropy value evaluation results as follows;

[0115] Through low entropy abnormal records, the potential security risks of each abnormal data flow are analyzed. The steps involve in-depth analysis of abnormal low entropy data flows to identify potential data consistency and security issues. Low entropy values ​​indicate that there are a large number of repeated or predictable data in the data flow, which is caused by system errors, data tampering or external interference. The analysis process includes identifying the specific patterns of abnormal data flows, comparing them with normal operating data, and comparing them with security event records. The steps of summarizing risk factors and improvement measures include determining the strengthening of data protection measures, optimizing data processing processes and setting up early warning systems. Verifying the security and integrity of power equipment data is achieved by implementing improvement measures and monitoring the effects, generating entropy evaluation results.

[0116] See also Figure 7 , based on the entropy value assessment results, analyze the data security risks, adjust the encryption measures according to the sensitivity and risk level of the data, and continuously track the encryption effect. The specific steps for generating a data security plan are as follows:

[0117] S601: Use the entropy value evaluation result to prioritize the security risks in the data flow, refer to the sensitivity and risk level of the data, adjust the encryption measures, match the security requirements of the differentiated levels, and generate the execution process of the adjusted encryption policy as follows;

[0118] The entropy assessment results are used to prioritize the security risks in the data flow. The process requires that each security risk in the entropy assessment results be classified according to the degree of damage caused and the probability of occurrence. The priority is determined based on the sensitivity and risk level of the data. Highly sensitive data or high-risk issues will be given priority. The sorted results will be used to adjust encryption measures to match differentiated levels of security requirements. For example, for extremely sensitive data, stronger encryption technology such as AES-256 will be used. For less sensitive data, lighter encryption measures such as AES-128 will be used. This differentiated security strategy ensures the rational allocation of resources while ensuring data security and generating an adjusted encryption strategy.

[0119] S602: Based on the adjusted encryption strategy, new encryption measures are implemented, including adjusting the key strength and changing the encryption method, comparing the security performance changes before and after encryption, and recording the time and effect of each adjustment. The execution process of 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 the formula, k represents the key length, S represents the security level coefficient, and E represents the encryption strength. Detailed explanation of the formula and the derivation process of the formula calculation: 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. The calculation helps determine the security performance of the encryption measures and ensure the confidentiality and security of the data. The generated encryption policy implementation record records the time of each adjustment, the encryption strength and effect before and after the adjustment in detail, and provides data support for verifying the encryption effect and further policy adjustments.

[0121] S603: By recording the implementation of encryption policies, continuously monitoring and evaluating the effectiveness of new encryption measures, checking the integrity and confidentiality of data flows, and recording key adjustments and improvement measures, the execution process of generating data security solutions is as follows;

[0122] Through encryption policy implementation records, the effectiveness of new encryption measures is continuously monitored and evaluated. The steps involve analyzing the data in the implementation records to determine the effectiveness and feasibility of the encryption measures. The monitoring process includes checking the integrity and confidentiality of the data flow to ensure that no unauthorized access or data leakage occurs. Recording key adjustments and improvement measures is to continuously improve data security. The effectiveness of new encryption measures is evaluated by comparing the changes in security performance before and after encryption, such as reducing the data leakage rate, increasing the difficulty of authorization for data access, etc. The implementation details, effect evaluation and future security plans of the encryption strategy are explained in detail to guide the company's data security management and policy optimization, and generate data security solutions.

[0123] See also Figure 8, a power equipment data intelligent management system based on data owner responsibility chain, the power equipment data intelligent management system based on data owner responsibility chain is used to execute the above-mentioned power equipment data intelligent management method based on data owner responsibility chain, the system includes:

[0124] The data registration module collects the manager data 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 the task allocation record;

[0126] The data verification module uses the task allocation record to check the power equipment data items, compare them with the authoritative data source, mark the deviations and missing parts in the data, and generate a summary record of the verification results;

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

[0128] The security monitoring module is based on auditing and adjusting records, monitoring the operating data of power equipment, calculating information entropy values, marking data anomaly events below the safety threshold, recording the anomaly time and data source, analyzing potential security risks, and formulating data security plans.

[0129] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for intelligent management of power equipment data based on data owner responsibility chain, characterized in that: The following steps are involved: Record the data owner 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 file index; Based on the data owner profile index, create power equipment data governance tasks, automatically identify data owners, assign data verification and update tasks, assign identifiers, and generate task assignment records; Using the task allocation record, analyzing the power equipment information that failed the data verification, comparing the data with the authoritative data source, marking deviations and missing data items, and generating a summary record of the verification results; By summarizing the verification results and recording them, the actual situation is verified based on the verification results, the issues that need to be addressed are determined, data governance is completed, and audit adjustment records are generated; According to the audit adjustment record, the data flow in the operation of the power equipment is monitored, and the information entropy is calculated. If the information entropy is lower than the safety threshold, the abnormal time, the source of the data flow and the safety risk are recorded to generate an entropy value assessment result; Based on the entropy assessment results, data security risks are analyzed, encryption measures are adjusted according to the sensitivity and risk level of the data, and a data security plan is generated by continuously tracking the encryption effect.

2. The method for intelligent management of power equipment data based on data owner responsibility chain according to claim 1 is characterized in that: 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 owner, information on the assigned data verification and update tasks, and the task identifier; the verification result summary record includes deviations in the data verification process of power equipment information, missing data items and comparison results with authoritative data sources; the audit adjustment record includes a description of the data governance issue, a record of the actual verification situation, the adjustment measures taken, the adjusted data status and timestamp; the entropy value assessment result includes the recorded abnormal time, the source of the data flow, the calculated information entropy value and the security risk level; the data security plan includes encryption measures adjusted according to the sensitivity and risk level of the data, continuously tracked encryption effect evaluation and security update plan.

3. The intelligent management method for power equipment data based on data owner responsibility chain according to claim 1 is characterized in that: Record the data owner 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 in the following steps: Based on the data governance object information, enter the data owner's name, department, and role into the target data set, assign a unique identifier to each record, and record the creation and update timestamps to generate a basic file for the data owner; Using the data owner basic file, enter the responsibilities and permissions of each data owner into the data set, synchronously record the modified timestamp and version number each time the data is updated, check the data integrity and consistency, and generate a responsibility file record; Through the responsibility profile records, the activity log of each data owner is recorded, including data access, modification and security events, the activities are linked to the personal profile of the data owner, the data owner responsibility chain is established, the traceability of the activity records is verified, and the data owner profile index is generated.

4. The method for intelligent management of power equipment data based on data owner responsibility chain according to claim 1 is characterized in that: Based on the data owner profile index, the steps of creating a power equipment data governance task, automatically identifying the data owner, assigning data verification and update tasks, and assigning identifiers to generate task assignment records are as follows: The data owner file index is used to identify the person in charge of the power equipment data, and a matching record of the person in charge of the power equipment data is generated by matching the identification result with the person in charge file; Based on the matching records of the power equipment data responsible persons, data verification and update tasks are automatically assigned 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; Through the data management task, a unique identifier is created for each assigned task, the creation time and the person in charge information of the task are recorded, the tracking management of each task is verified, and the task assignment record is generated.

5. The method for intelligent management of power equipment data based on data owner responsibility chain according to claim 1 is characterized in that: The steps of using the task allocation record, analyzing the power equipment information that failed the data verification, comparing the data with the authoritative data source, marking the deviation and missing data items, and generating the verification result summary record are as follows: Based on the task allocation record, filter the information of the power equipment that has not passed the initialization data verification, record the person in charge and the problem point of the power equipment, and generate the initialization problem power equipment record; Using the initialized problematic power equipment record, performing comparative analysis on each data item, comparing it with the authoritative data source, marking the deviation and missing of the data, and generating a deviation comparison record; Through the deviation comparison record, the marked deviations and missing data items are summarized, key information is extracted and improvement plans are proposed, the integrity of the power equipment data quality control is verified, and a summary record of the verification results is generated.

6. The method for intelligent management of power equipment data based on data owner responsibility chain according to claim 1 is characterized in that: The steps of summarizing the verification results and recording them, verifying the actual situation based on the verification results, determining the issues that need to be managed, completing data management, and generating the audit adjustment records are as follows: Based on the summary records of the verification results, multiple types of data anomalies and volatility are identified, the source of each type of data is tracked and analyzed, key sources of deviations are extracted, and a list of data problems is obtained; According to the data problem list, set data governance measures, make data adjustments for the identified problems, manually correct obvious errors, perform data processing and batch update operations, and obtain a summary of data governance results; Through the data governance results summary, review the adjusted data set, verify data consistency and completeness, check that the update of the data set complies with business rules and requirements, and obtain the audit adjustment record.

7. The method for intelligent management of power equipment data based on data owner responsibility chain according to claim 1 is characterized in that: According to the audit adjustment record, the data flow in the operation of the power equipment is monitored, and the 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 steps for generating the entropy value evaluation result are as follows: The audit adjustment record is used to monitor the data flow of the power equipment during operation, calculate the information entropy of the data flow in real time, evaluate the volatility of the data, and generate a real-time entropy value monitoring record; Based on the real-time entropy monitoring record, the sliding window technology is used to calculate the information entropy of each data flow, and compare it with the set security threshold, identify and record the abnormal time and source of the data flow below the threshold, and generate a low entropy abnormal record; Through the low entropy value abnormal records, the potential security risks of each abnormal data flow are analyzed, risk factors and improvement measures are summarized, the security and integrity of the power equipment data are verified, and the entropy value evaluation results are generated.

8. The method for intelligent management of power equipment data based on data owner responsibility chain according to claim 7 is characterized in that: The formula for the sliding window technique is as follows: 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 represents the information entropy at time point i, represents the average information entropy of all data points in 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.

9. The method for intelligent management of power equipment data based on data owner responsibility chain according to claim 1 is characterized in that: Based on the entropy value assessment results, the data security risks are analyzed, the encryption measures are adjusted according to the sensitivity and risk level of the data, and the encryption effect is continuously tracked to generate the data security plan. The specific steps are as follows: Using the entropy value assessment result, the security risks in the data stream are prioritized, and the encryption measures are adjusted with reference to the sensitivity and risk level of the data to match the security requirements of the differentiated levels, thereby generating an adjusted encryption strategy; Based on the adjusted encryption strategy, implement new encryption measures, including adjusting key strength and changing encryption methods, comparing security performance changes before and after encryption, and recording the time and effect of each adjustment, and generating encryption strategy implementation records; Through the implementation records of the encryption strategy, continuously monitor and evaluate the effectiveness of new encryption measures, check the integrity and confidentiality of data flows, record key adjustments and improvement measures, and generate data security plans.

10. An intelligent power equipment data management system based on data owner responsibility chain, characterized in that: According to the method for intelligent management of power equipment data based on the data owner responsibility chain according to any one of claims 1 to 9, the system comprises: The data registration module collects the manager data of 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 owner based on the data owner profile index, automatically allocates data verification and update tasks, allocates an identifier to each task, and obtains a task allocation record; The data verification module uses the task allocation record to check the power equipment data items, compare with the authoritative data source, mark the deviations and missing parts in the data, and generate a summary record of the verification results; The data analysis module manually reviews the data items of the power equipment through the summary record of the verification results, determines the data content that needs to be corrected, determines the problems that need to be addressed, and obtains the audit adjustment record; The security monitoring module monitors the operating data of the power equipment, calculates the information entropy value, marks data abnormal events below the safety threshold, records the abnormal time and data source, analyzes potential security risks, and formulates a data security plan based on the audit adjustment records.

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