A quantitative assessment technique for data quality of nuclear power equipment

Abstract

This invention provides a quantitative assessment method for nuclear power equipment data quality, comprising: acquiring multiple business objects in the nuclear power production field; ranking each business object by importance to obtain high-priority, medium-priority, and low-priority business objects; determining quality assessment rules according to the business objects in descending order of priority; verifying the data corresponding to the business objects according to the quality assessment rules to obtain quality problem records; classifying and judging the quality problem records to determine and output the corresponding level of each quality problem record; acquiring the rectified data corresponding to the quality problem records; verifying the data quality and determining compliance of the rectified data to obtain a compliance confirmation result. This invention achieves a quantitative and systematic assessment of nuclear power equipment data quality through priority ranking, six-dimensional quality assessment, graded judgment, and closed-loop rectification.

Description

Technical Field

[0001] This invention relates to the field of data quality management technology, and more specifically, to a quantitative assessment method for data quality of nuclear power equipment. Background Technology

[0002] Data serves as a crucial basis for key business processes in nuclear power plants, including decision-making, equipment maintenance, and safety assurance. Its quality directly impacts the safety, reliability, and economic efficiency of nuclear power operations. With the accelerated digital transformation of nuclear power plants, the amount of data generated during nuclear power production is exploding, and the types and complexity of this data are also increasing. However, the quality of data in the nuclear power production field currently faces numerous challenges, such as inaccurate, inconsistent, incomplete, untimely, non-unique, and invalid data. These problems severely restrict the improvement of operational efficiency and management level of nuclear power enterprises. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a quantitative assessment method for data quality of nuclear power equipment, addressing the problems existing in the prior art.

[0004] The technical solution adopted by this invention to solve its technical problem is: a quantitative assessment method for data quality of nuclear power equipment, the method comprising the following steps: Step S1: Obtain multiple business objects in the nuclear power production field, sort each business object by importance, and obtain high-priority business objects, medium-priority business objects, and low-priority business objects; Step S2: Determine the quality assessment rules according to the business object in order of priority from high to low, and perform data quality verification on the data corresponding to the business object according to the quality assessment rules to obtain quality problem records; Step S3: Classify the quality problem records, determine and output the corresponding level of the quality problem records; Step S4: Obtain the rectified data corresponding to the quality problem record, perform data quality verification and compliance determination on the rectified data, and obtain the compliance confirmation result.

[0005] Furthermore, in step S1, the ranking of importance of each business object includes: Based on the aforementioned business objects, obtain business importance scores, strategic relevance scores, business operation goal relevance scores, and cross-domain application impact scores; The business importance score, the strategic relevance score, the business operation objective relevance score, and the cross-domain application impact score are summed to obtain the total score for each business object, and the priority of each business object is determined based on the total score.

[0006] Further, in step S2, the data quality verification of the data corresponding to the business object includes: The corresponding data quality dimensions that need to be verified are determined based on the business object, wherein the data quality dimensions include at least one quality assessment indicator; Perform the corresponding verification operation based on the data quality dimension to obtain the quality problem record corresponding to the business object.

[0007] Furthermore, the quality assessment indicators include at least one of accuracy, completeness, consistency, timeliness, uniqueness, and effectiveness.

[0008] Furthermore, the verification operation includes: When the quality assessment indicator is accuracy, at least one of cross-validation, outlier detection algorithm or logical verification method shall be used to perform the verification. When the quality assessment indicator is integrity, at least one of the following methods shall be used to perform the verification: standard dataset comparison method, time series integrity analysis, or correlation data verification method. When the quality assessment index is consistent, at least one of the following methods shall be used to perform the verification: cross-system comparison method, format verification algorithm or historical data tracing method. When the quality assessment indicator is timeliness, at least one of the following methods shall be used to perform the verification: time limit standard verification method, real-time transmission monitoring model or timestamp verification method. When the quality assessment indicator is unique, at least one of the following methods shall be used to perform the verification: primary key index deduplication algorithm, hash algorithm deduplication method, or input permission control verification. When the quality assessment indicator is valid, at least one of the following methods shall be used to perform the verification: business rule verification method, applicability verification method, or equipment status association verification method.

[0009] Furthermore, the step S3 of classifying and determining the quality problem records includes: Obtain at least one grading parameter from the quality problem record; The grading judgment parameters are judged according to the preset grading rules to determine the level corresponding to the quality problem record; The levels include at least one of severe, important, and general levels.

[0010] Furthermore, the preset hierarchical rules include: When the grading judgment parameters meet the first preset condition, the quality problem record is determined to be of the severe level; When the grading determination parameters meet the second preset condition, the quality problem record is determined to be of the importance level; When the grading determination parameters meet the third preset condition, the quality problem record is determined to be of the general level.

[0011] Furthermore, the classification and determination parameters include at least one of the following: equipment association level data, problem type, data exceeding limit value, number of affected systems, data type, business impact degree, economic loss estimate, data priority score, number of recurrences, problem occurrence field, problem occurrence frequency, data access status, data application value, resource consumption status, and quality indicator deviation. The first preset condition includes at least one of the following: the device association level data is nuclear safety level equipment and the problem type belongs to a preset high-risk type; the data type is global master data and the number of affected systems reaches a preset threshold; the data type is emergency response data and affects the initiation of emergency procedures; failure to rectify will lead to preset major consequences. The second preset condition includes at least one of the following: the device association level data is important operational level equipment and the problem type belongs to the preset operational impact type; the data type is core operational data and the business impact reaches the preset level of operational efficiency loss; the quality indicator deviation is the cross-system consistency rate and is lower than the preset threshold and affects business collaboration; the data priority score reaches high priority and the number of recurrences reaches the preset number of times. The third preset condition includes at least one of the following: the device association level data is a general-level device and the problem occurrence field is a non-critical field; the problem occurrence frequency is single and occasional and the data is not called by the business system; the data application value is data redundancy with no actual application value; and the deviation of the quality indicator is within the preset tolerance range and has no chain effect.

[0012] Further, in step S4, the data quality verification and compliance determination of the rectified data to obtain the compliance confirmation result includes: Perform data quality tracking and verification on the rectified data for at least one preset period and obtain the verification results for each period; When the verification results of all cycles meet the preset quality threshold, the compliance confirmation result is determined.

[0013] Furthermore, after step S4, the method further includes: Obtain the rectification completion confirmation form and supporting materials for the rectification process, perform system compliance verification on the supporting materials for the rectification process, and when the verification passes, link and store the two together to form a closed-loop record of quality issues; wherein the supporting materials for the rectification process include at least one of equipment calibration records, process optimization documents, and personnel training records, and the system compliance verification includes at least one of file format verification, mandatory field verification, and file integrity verification.

[0014] The present invention provides a quantitative assessment method for nuclear power equipment data quality, which has the following beneficial effects: Step S1: Obtain multiple business objects in the nuclear power production field, rank each business object by importance, and obtain high-priority, medium-priority, and low-priority business objects; Step S2: Determine quality assessment rules according to the business objects in descending order of priority, and perform data quality verification on the data corresponding to the business objects according to the quality assessment rules to obtain quality problem records; Step S3: Classify and determine the quality problem records, and output the corresponding level of each quality problem record; Step S4: Obtain the rectified data corresponding to the quality problem records, perform data quality verification and compliance determination on the rectified data, and obtain a compliance confirmation result. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a logical flowchart of the technical method for quantitatively assessing the data quality of nuclear power equipment. Detailed Implementation

[0016] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the invention are now described in detail with reference to the accompanying drawings. In the following description, specific details such as particular structures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0017] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a quantitative assessment technique for nuclear power equipment data quality in one embodiment of this application.

[0018] The technical solution adopted by this invention to solve its technical problem is: a quantitative assessment method for data quality of nuclear power equipment, the method comprising the following steps: Step S1: Obtain multiple business objects in the nuclear power production field, sort each business object by importance, and obtain high-priority business objects, medium-priority business objects, and low-priority business objects; It should be noted that the business objects in the nuclear power production field include equipment classification data, reliability model library data, configuration configuration library data, equipment operating parameter data, maintenance work order data, and spare parts data. By prioritizing these business objects according to their importance, data quality assessment resources can be allocated rationally, allowing for in-depth assessment of core business objects first, thus avoiding inefficiencies caused by the dispersion of assessment resources.

[0019] Step S2: Determine the quality assessment rules based on the business object according to the priority from high to low, and perform data quality verification on the data corresponding to the business object according to the quality assessment rules to obtain quality problem records; In this step, it should be noted that different evaluation rules and frequencies are used for business objects of different priorities. High-priority business objects are evaluated using comprehensive rules, with a verification frequency of real-time or daily; medium-priority business objects are evaluated using core rules, with a verification frequency of weekly or monthly; and low-priority business objects are evaluated using simplified rules, with a verification frequency of quarterly or as needed.

[0020] Step S3: Classify and determine the level of the quality problem records, and output the corresponding level of the quality problem records; In this step, it's important to note that the grading determination uses a rule of "marking upon meeting any one of the core conditions," combined with a comprehensive assessment across four dimensions: "data importance, business impact, consequences of the failure, and compliance risk." Different levels correspond to different handling timelines and processes. For example: critical issues jeopardize the safe operation of the unit / equipment, triggering the highest priority handling and requiring immediate action; important issues affect the unit's operational efficiency but pose no safety risk and must be handled within the specified timeframe; general issues have minimal business impact, involving only non-critical field / format defects, and can be handled according to standard procedures. The grading determination is executed automatically by the system, but manual review and confirmation are supported.

[0021] Step S4: Obtain the data after rectification corresponding to the quality problem record, perform data quality verification and compliance determination on the data after rectification, and obtain the compliance confirmation result.

[0022] In this step, it should be noted that the compliance determination adopts a triple standard of "quantitative indicator verification + qualitative root cause treatment + periodic stability verification." All standards must be met simultaneously for automatic "compliance" to be determined, and manual review and confirmation are supported. Quantitative indicator verification requires that the six core indicators of data quality after rectification reach preset quality thresholds (e.g., accuracy 100%, timeliness 100%, completeness 100%, uniqueness 100%, effectiveness 100%), and the indicator values ​​of individual problematic data after rectification must meet the corresponding business rule requirements. Qualitative root cause treatment requires that the root cause of the problem has been accurately located using a fishbone diagram and the 5 Whys analysis method, and that all rectification measures corresponding to the root cause type (human / machine / material / method / environment) have been implemented. The responsible party has submitted a rectification completion confirmation form and uploaded supporting materials, which have passed system compliance verification, and preventive measures have been solidified in the system. The periodic stability verification requires continuous periodic observation after rectification is completed (30 days for severe problems, 15 days for important problems, and 7 days for general problems). During the observation period, no similar quality problems are reproduced, and the six performance indicators remain stable above the preset threshold.

[0023] Furthermore, in step S1, ranking the importance of each business object includes: obtaining business importance score, strategic relevance score, business operation goal relevance score, and cross-domain application impact score based on the business object; summing the business importance score, strategic relevance score, business operation goal relevance score, and cross-domain application impact score to obtain the total score of each business object; and determining the priority of each business object based on the total score.

[0024] In this step, it should be noted that the design of the four scoring dimensions fully considers the special characteristics of the nuclear power industry: the business importance dimension focuses on the safety level of equipment and the consequences of failure; the strategic relevance dimension focuses on the degree to which the data supports core strategic indicators such as nuclear safety, unit availability, and reliability of key equipment; the business operation objective relevance dimension focuses on the necessity of the data in the business execution process and the weight of the consequences of data errors / missing data; and the cross-domain application impact dimension focuses on the sharing and use of data among multiple systems and departments, including whether it is enterprise-level master data, how many key business systems reference it, and how many core business domains it covers.

[0025] Specifically, the calculation logic for the business importance score is as follows: First, match the level of the data-related equipment (nuclear safety level equipment: base score 4 points, important level 3 points, general level 1 point). Then, search core business documents such as the "Nuclear Safety Analysis Report," "Equipment Failure Emergency Plan," and "Statutory Testing and Verification Specifications" to see if the data appears (add 1 point if it appears). If the missing / incorrect data directly leads to unit shutdown, safety accident, or compliance penalty, it is directly judged as 5 points. The strategic relevance score is determined based on the level of strategic indicators supported by the data: supporting core strategic indicators (nuclear safety, unit availability, key equipment reliability) earns 4 points, supporting important operational indicators earns 3 points, and supporting only internal management, statistics, and record-keeping earns 1 point. The business operation objective relevance score is determined comprehensively based on the necessity of the data in business execution (high support 4 points, medium support 3 points, low support 1 point) and the weight of the consequences of data errors / missing data (extremely high weight 4 points, high weight 3 points, low weight 1 point). The cross-domain application impact score is determined by a combination of factors, including whether the data is enterprise-level master data (master data base score 4 points), the number of times it is referenced by key business systems (≥3 systems get 3 points, 1-2 systems get 1 point), and cross-domain influence (high 4 points, medium 3 points, low 1 point). The priority determination rules are as follows: a total score ≥18 points indicates high priority (first-mover advantage), 11-17 points indicates medium priority (second-mover advantage), and ≤10 points indicates low priority (assessed as needed). As shown in Table 1, equipment classification scores 5 points in all four dimensions, totaling 20 points, which is considered high priority. The reliability model library scores 19 points, which is also considered high priority. The configuration library scores 17 points, which is considered medium priority. The business importance score is automatically calculated by extracting information from three dimensions: "nuclear power equipment classification + core business document association + failure impact." The strategic relevance score is based on "the degree of matching between data and enterprise strategic implementation indicators + data support for enterprise strategic priority." The business operation goal relevance score is manually calculated by "the degree of data support for business operation goals + indicator impact weight." The cross-domain application impact score is based on "data standard attributes, logical entities + master data + cross-domain impact." Specific scoring results are shown in Table 1.

[0026] Table 1. Example of Key Data Scoring Furthermore, in step S2, the data quality verification of the data corresponding to the business object includes: determining the corresponding data quality dimension to be verified based on the business object, wherein the data quality dimension includes at least one quality assessment indicator; performing the corresponding verification operation based on the data quality dimension to obtain the quality problem record corresponding to the business object.

[0027] In this step, it's important to note that the data types for equipment management differ across different business objects, and the dimensions of data quality that need to be verified also vary. For example, equipment operating parameter data (such as reactor coolant temperature and pressure) primarily verifies accuracy and timeliness; unique identifier data such as equipment numbers and electronic record numbers primarily verifies uniqueness and validity; structured data such as equipment electronic records, configuration libraries, and reliability model libraries primarily verifies completeness; cross-departmental shared data such as equipment IDs, fault classification codes, and spare parts numbers primarily verifies consistency; and all equipment management data primarily verifies validity.

[0028] Specifically, the process begins by determining the corresponding equipment management data (such as equipment operation parameter tables, equipment electronic history tables, maintenance work order tables, etc.) based on the business object. Then, the data type is determined (core business data, structured data, cross-departmental shared data, real-time operational data, unique identifier data, or full equipment management data). Next, the data quality dimensions requiring verification are determined based on the data type. Finally, the corresponding verification operations are performed. Core business data is verified for accuracy, structured data for completeness, cross-departmental shared data for consistency, real-time operational data for timeliness, unique identifier data for uniqueness, and full equipment management data for validity.

[0029] Furthermore, the quality assessment indicators include at least one of the following: accuracy, completeness, consistency, timeliness, uniqueness, and effectiveness.

[0030] Specifically, accuracy refers to the degree to which data values ​​match actual physical objects or business facts, and is the core foundation of data quality; completeness refers to whether the data fully covers all fields and records required by the business, without any omissions or gaps; consistency refers to the degree to which the expression and value of the same data remain consistent across different systems, departments, and time points; timeliness refers to the degree to which the time interval between data generation and usability meets business needs; uniqueness refers to the degree to which core identifier data is unique across the entire system; and validity refers to the degree to which data conforms to business rules, format standards, and usage scenario requirements, and can support business applications. Quality assessment rules include data elements (business objects, logical data entities, attributes, data types), rule elements (rule number, rule name, rule type, rule object, judgment logic, verification frequency, rule priority, key quality indicators), and management elements (data source, responsible entity), ensuring that the assessment process is quantifiable and implementable. Quality issue records are categorized into at least one of the following: data entry errors, data transmission errors, data acquisition errors, consistency issues, completeness issues, validity issues, and uniqueness issues. Specific examples of assessment rules are shown in Table 2.

[0031] Table 2 Examples of Data Quality Assessment Rules Furthermore, the verification operation includes: when the quality assessment indicator is accuracy, performing verification using at least one of cross-validation, outlier detection algorithm, or logical verification method; Specifically, cross-validation verifies data accuracy by comparing online monitoring data from the same equipment with manual inspection data, data collected synchronously from different sensors, or benchmark data (such as equipment calibration records and legally mandated testing data). Outlier detection algorithms, based on the 3σ principle, box plot method, or isolated forest algorithm, automatically identify extreme data exceeding the thresholds of normal equipment operating parameters and determine data accuracy in conjunction with the business scenario. Logical verification verifies the logical rationality of data by establishing business logic rules (such as "when the equipment's operating status is 'running,' the running time should be greater than 0"). Accuracy evaluation indicators include data accuracy rate (number of accurate data entries / total number of data entries × 100%), data deviation rate (number of deviation data entries / total number of data entries × 100%), and the percentage of outlier data. Applicable scenarios include core business data such as equipment operating parameters, fault records, maintenance data, and calibration data.

[0032] When the quality assessment indicator is completeness, at least one of the following methods shall be used to perform the verification: standard dataset comparison method, time series completeness analysis, or correlation data verification method. Specifically, the standard dataset comparison method compares the data against the NB / T 20134-2012 standard and the nuclear power equipment management business specifications to compile a list of mandatory data items for scenarios such as equipment operation, failure, maintenance, and testing, and verifies data missing items one by one. Time series integrity analysis uses the sliding window method to detect blank intervals in high-frequency collected data (such as reactor coolant temperature and pressure data) within continuous time periods to assess data continuity. The correlation data verification method verifies the matching of correlated data (e.g., failure records must correspond to maintenance records, and test data must correspond to performance evaluation data); if no correlation exists, it is considered incomplete. Integrity assessment indicators include field completeness rate (number of non-empty fields / total number of fields × 100%), record completeness rate (number of complete data records / total number of records × 100%), and key field missing rate. Applicable scenarios include structured data such as equipment electronic records, configuration configuration libraries, and reliability model libraries.

[0033] When the quality assessment indicator is consistency, at least one of the following methods shall be used to perform the verification: cross-system comparison method, format verification algorithm or historical data tracing method. Specifically, the cross-system comparison method establishes unified standards for data format, statistical definitions, and unit conversions to compare differences in source data from operating systems, maintenance systems, and safety systems. Format verification algorithms automatically detect consistency in data field length, encoding rules (e.g., equipment code "CP + 4-digit base code + 6-digit equipment serial number + 4-digit check code"), and numerical format. The historical data tracing method uses version control technology to verify data modification records, ensuring data changes are traceable, logically consistent, and free from tampering or contradictions. Consistency assessment indicators include cross-system data consistency rate (number of consistent source data entries across multiple systems / total number of source data entries × 100%), format consistency rate, and unit consistency rate. This method is applicable to cross-departmental shared data such as equipment IDs, fault classification codes, and spare parts numbers.

[0034] When the quality assessment indicator is timeliness, at least one of the following methods shall be used for verification: time limit standard verification method, real-time transmission monitoring model or timestamp verification method. Specifically, the time limit standard verification method sets data collection, transmission, and storage time limits according to system importance (data latency ≤10 seconds for critical systems, ≤5 minutes for general systems) to verify whether data is available within the time limit. The real-time transmission monitoring model tracks the entire process of data generation, collection, transmission, and storage, presenting latency distribution through visualization tools; exceeding thresholds triggers warnings. The timestamp verification method compares the difference between the data collection timestamp and the actual business occurrence time, quantifying the proportion of time-out data. Timeliness assessment indicators include data timeliness rate (number of timely available data entries / total number of data entries × 100%) and data transmission latency (average interval from data generation time to storage completion time), applicable to scenarios such as real-time equipment operation data, fault alarm data, and emergency response related data.

[0035] When the quality assessment indicator is unique, at least one of the following methods shall be used to perform the verification: primary key index deduplication algorithm, hash algorithm deduplication method, or entry permission control verification. Specifically, the primary key index deduplication algorithm uses the device's unique ID and data acquisition number as core primary keys to build an index database, automatically identifying and counting duplicate data records. The hash algorithm deduplication method integrates data from multiple systems, calculating data feature hash values ​​to compare and eliminate duplicate data entries for the same event. Entry permission control verification checks the allocation of key data entry permissions, verifying the effectiveness of the pre-entry deduplication verification mechanism and preventing duplicate data at the source. Uniqueness assessment indicators include data duplication rate (number of duplicate data entries / total number of data entries × 100%) and unique identifier conflict count, applicable to unique identifier data such as device numbers, electronic record numbers, and maintenance work order numbers.

[0036] When the quality assessment indicator is valid, at least one of the following methods shall be used to perform the verification: business rule verification, applicability verification, or equipment status correlation verification.

[0037] Specifically, the business rule verification method verifies whether the data conforms to the business definition based on preset rules (such as fault code value range, equipment operating parameter thresholds, and date format "YYYY-MM-DD"). The applicability verification method assesses whether the data meets the usage requirements of specific business scenarios such as risk quantification analysis, maintenance plan formulation, and emergency decision-making, eliminating data with no practical application value. The equipment status correlation verification method determines whether the data truly reflects the actual operating status of the equipment (e.g., sensor data must be logically consistent with equipment maintenance records and operation logs); if inconsistent, it is deemed invalid. The validity evaluation indicators include data validity rate (number of data entries conforming to business rules / total number of data entries × 100%), format compliance rate, and rule compliance rate. The applicable scenarios are full-volume equipment management data, especially focusing on high-priority business object data (equipment classification, reliability model library, etc.).

[0038] Furthermore, step S3, which involves classifying and determining the quality problem record, includes: obtaining at least one classification parameter for the quality problem record; judging the classification parameter according to a preset classification rule to determine the level corresponding to the quality problem record; wherein the level includes at least one of severe, important, and general levels.

[0039] In this step, it's important to note that the grading determination uses a rule of "marking if any core condition is met," combined with a comprehensive assessment of four dimensions: "data importance, business impact, consequences of the failure, and compliance risk." Different levels correspond to different handling timelines and processes: critical issues trigger the highest priority handling and must be addressed immediately; important issues impact operational efficiency and must be handled within a specified timeframe; general issues have minimal impact and can be handled according to standard procedures. The grading determination is executed automatically by the system, but manual review and confirmation are supported.

[0040] Specifically, the system automatically retrieves data from quality issue records, including equipment association level, issue type, data exceeding limits, number of affected systems, data type, business impact, estimated economic loss, data priority score, number of recurrences, fields where the issue occurred, frequency of occurrence, data access status, data application value, resource usage, and deviation of quality indicators, and matches these parameters with preset grading rules. If any severity condition is matched, the system outputs a severity level; if any importance condition is matched, the system outputs an importance level; if any general condition is matched, the system outputs a general level.

[0041] Furthermore, the preset grading rules include: when the grading judgment parameters meet the first preset condition, the quality problem record is determined to be of the severe level; when the grading judgment parameters meet the second preset condition, the quality problem record is determined to be of the important level; when the grading judgment parameters meet the third preset condition, the quality problem record is determined to be of the general level.

[0042] Specifically, the criteria for determining a severity level include: the problem is associated with nuclear safety-grade equipment and the problem type is missing data, data exceeding limits, or data error, where the data exceeding limits is ≥150% of the preset threshold or the data error directly causes misjudgment of equipment status; the problem involves enterprise-level global master data (such as functional location), the problem type is uniqueness conflict or format error, and it causes interruption of data collaboration among ≥3 critical business systems (operation / maintenance / safety); there are timeliness defects in fault alarm and emergency response data, and the emergency response process cannot be started normally due to missing data; if this quality problem is not rectified in a timely manner, it will directly lead to equipment failure, unplanned shutdown, safety incidents, or compliance penalties.

[0043] The criteria for determining the importance level include: the problem is related to important operational equipment, the problem type is inaccurate, incomplete, or inconsistent data, which does not affect the safe operation of the equipment but may cause misjudgment of maintenance strategies, mismatch of spare parts, and delays in operation and maintenance processes; core operational data (maintenance work orders, spare parts inventory, equipment runtime, etc.) contains errors, resulting in operational efficiency losses such as operation and maintenance response timeouts, decreased spare parts inventory turnover rate, and wasted manpower costs, and the estimated direct economic loss of a single data error is ≥ the preset threshold; general system data has timeliness issues, or cross-system data consistency rate is <80%, affecting inter-departmental business collaboration but not having a global impact; high-priority evaluation data (score ≥18 points) has non-safety quality issues, and the same type of problem occurs ≥3 times.

[0044] General-level judgment criteria include: the problem is associated with general-level devices and only occurs in non-critical fields (such as device remarks, data entry personnel, and data entry time), manifesting as format errors, minor omissions, character redundancy, etc., and does not affect device status judgment or business process progress; it is an occasional error in a single data entry, with no similar problems reproducing, and the erroneous data is only stored in the data storage layer and is not called or referenced by any business system; it is only data redundancy with no practical application value, does not occupy core storage resources, and does not affect data statistical analysis results; indicators such as format compliance rate and unit consistency rate slightly deviate from the threshold (deviation ≤ 5%), which can be resolved with simple format correction and have no subsequent chain reaction.

[0045] The conditions for level change include: if a quality problem is not closed within the predetermined rectification period, or if a similar problem occurs more than N times (e.g., 3 times) within a specific period, the system should automatically or with approval raise the problem level by one level (e.g., from 'important' to 'serious') and trigger a higher-level warning and supervision process.

[0046] Furthermore, the classification and judgment parameters include at least one of the following: equipment association level data, problem type, data exceeding limit value, number of affected systems, data type, business impact degree, economic loss estimate, data priority score, number of recurrences, problem occurrence field, problem occurrence frequency, data access status, data application value, resource consumption status, and quality indicator deviation. The first preset condition includes at least one of the following: the equipment associated with the data is nuclear safety level equipment and the problem type is a preset high-risk type; the data type is global master data and the number of affected systems reaches a preset threshold; the data type is emergency response data and affects the activation of the emergency process; failure to rectify will lead to preset major consequences. The second preset condition includes at least one of the following: the equipment association level data is important operational level equipment and the problem type belongs to the preset operational impact type; the data type is core operational data and the business impact reaches the preset level of operational efficiency loss; the quality indicator deviation is the cross-system consistency rate and is lower than the preset threshold and affects business collaboration; the data priority score reaches high priority and the number of recurrences reaches the preset number. The third preset condition includes at least one of the following: the device association level data is a general-level device and the field where the problem occurs is a non-critical field; the frequency of the problem is single and occasional and the data is not accessed by the business system; the data application value is data redundancy with no practical application value; and the deviation of the quality indicator is within the preset tolerance range and has no chain effect.

[0047] This invention categorizes and grades the problems identified in the assessment and handles them according to standard procedures to ensure timely rectification and effective implementation of assessment results. Specifically, the problem categories include data entry errors, data transmission errors, data collection errors, consistency issues, integrity issues, validity issues, and uniqueness issues. The problem grading combines four dimensions: data importance, business impact, consequences of failure, and compliance risks, classifying problems into three levels: severe, important, and general. Each level has clearly defined judgment criteria and handling timelines to ensure that high-risk problems are prioritized.

[0048] The severe level corresponds to situations that endanger the safe operation of the unit or equipment, triggering the highest priority handling. Specifically, this includes: issues related to nuclear safety-grade equipment and the issue type being data missing, data exceeding limits, or data errors (data exceeding limits ≥ 150% of the preset threshold or data errors directly causing misjudgment of equipment status); issues involving enterprise-level global master data and the issue type being uniqueness conflict or format error, resulting in the interruption of data collaboration among ≥ 3 critical business systems; timeliness defects in fault alarms or emergency response data, causing the emergency response process to fail to start normally due to data missing; and quality issues that, if not rectified in a timely manner, will directly lead to equipment failure, unplanned shutdowns, safety incidents, or compliance penalties.

[0049] The "Important" level corresponds to situations that affect unit operational efficiency but do not pose a safety risk, including: issues related to critical operational equipment and the issue type being inaccurate, incomplete, or inconsistent data, which may lead to misjudgments in maintenance strategies, mismatches in spare parts, or delays in operation and maintenance processes; errors in core operational data, resulting in operational efficiency losses such as timeouts in operation and maintenance response, decreased spare parts inventory turnover, or wasted manpower costs, and the estimated direct economic loss from a single data error is ≥ the preset threshold; timeliness issues with general system data or cross-system data consistency rates < 80%, affecting inter-departmental business collaboration but not having a global impact; and high-priority assessment data exhibiting non-safety-related quality issues with similar issues occurring ≥ 3 times.

[0050] The general level corresponds to situations with minimal business impact, involving only non-critical fields or formatting defects. This includes: issues related to general-level devices and occurring only in non-critical fields, manifesting as format errors, minor missing characters, or character redundancy, not affecting device status assessment or business process progress; occasional errors in a single data entry, with no similar issues reproducing, and the erroneous data remaining only in the data storage layer and not referenced by any business system; data redundancy with no practical application value, not occupying core storage resources and not affecting data statistical analysis results; and minor deviations (≤5%) from thresholds for indicators such as format compliance rate and unit consistency rate, which can be resolved with simple format correction and have no subsequent chain reactions. Through this tiered judgment mechanism, this invention can scientifically distinguish the severity of problems and adopt differentiated handling strategies accordingly, ensuring efficient and accurate closed-loop management of data quality issues.

[0051] Preset high-risk types include missing data, data exceeding limits, and data errors; preset operational impact types include inaccurate data, incomplete data, and inconsistent data; preset thresholds include: data exceeding limits ≥150%, number of affected systems ≥3, cross-system consistency rate <80%, data priority score ≥18 points, number of recurrences ≥3 times, and quality indicator deviation ≤5%. The classification of nuclear safety-grade equipment, critical operational-grade equipment, and general-grade equipment is based on the equipment classification system. The determination of enterprise-level global master data is based on the master data judgment logic in cross-domain application impact scoring. Emergency response data includes fault alarm data and emergency response-related data. Core operational data includes maintenance work orders, spare parts inventory, and equipment runtime. Non-critical fields include equipment remarks, data entry personnel, and entry time.

[0052] This method also includes data quality audit and monitoring steps: a long-term audit and monitoring mechanism is built by combining manual sampling, automated verification, and statistical analysis. Manual sampling involves regular sampling and verification of high-priority data by professionals. Automated verification uses SQL scripts or data quality tools to automatically verify the "six properties" rules. Statistical analysis calculates indicators such as data mean, standard deviation, and frequency distribution to identify abnormal data. The audit and monitoring process includes collecting data from data sources such as equipment sensors, business systems, and file systems and transmitting it to the data quality management platform. Pre-set evaluation rules are used to verify each of the "six properties" of the data to identify quality problems. Detailed information such as the problem type, location, severity, and occurrence time is recorded, and problems are promptly reported to the data responsible party for confirmation and processing. The problem handling process also includes uniformly receiving and registering quality problem records, verifying the authenticity of problems and marking them as "confirmed," and forming cross-departmental teams to use fishbone diagrams and the 5 Whys analysis method to trace the root causes (people, machine, material, method, environment) and locate the occurrence stage of the problem.

[0053] Furthermore, in step S4, the data quality verification and compliance determination of the rectified data are carried out to obtain the compliance confirmation result, including: performing data quality tracking verification on the rectified data for at least one preset period and obtaining the verification results for each period; when the verification results of all periods meet the preset quality threshold, the compliance confirmation result is determined.

[0054] In this step, it's important to note that the compliance determination employs a triple standard: quantitative indicator verification, qualitative root cause remediation, and periodic stability verification. All standards must be met simultaneously for an automatic "compliance" determination. Quantitative indicator verification requires that the six core data quality indicators after rectification reach preset quality thresholds (e.g., 100% accuracy, 100% timeliness, 100% completeness, 100% uniqueness, and 100% effectiveness), and that the indicator values ​​for individual problematic data entries comply with the corresponding business rules. Qualitative root cause remediation requires that the root cause of the problem has been accurately identified, all rectification measures have been implemented, supporting materials have passed compliance verification, and preventative measures have been system-wide solidified. Periodic stability verification requires continuous periodic observation after rectification, with no recurrence of similar quality issues during the observation period, and the six indicators remaining consistently above the preset thresholds.

[0055] Specifically, firstly, data quality verification is performed on the rectified data to obtain quantitative indicator verification results; then, qualitative root cause management results are obtained, including whether the root cause of the problem has been accurately identified, whether all rectification measures have been implemented, whether supporting materials have passed compliance verification, and whether preventive measures have been systematically solidified; finally, the recurrence of similar problems is monitored within a preset observation period. Severe problems are observed for 30 days, important problems for 15 days, and general problems for 7 days. If the quality problem is cross-system or multi-field related, all related data indicators must remain compliant during the observation period, and no new quality problems should arise due to rectification. When all three conditions are met, a compliance confirmation result is output.

[0056] Furthermore, after step S4, the process also includes: obtaining the rectification completion confirmation form and supporting materials for the rectification process; performing system compliance verification on the supporting materials for the rectification process; and storing the two together when the verification is successful to form a closed-loop record of quality issues. The supporting materials for the rectification process include at least one of equipment calibration records, process optimization documents, and personnel training records. The system compliance verification includes at least one of document format verification, mandatory field verification, and document integrity verification.

[0057] In this step, it should be noted that the rectification completion confirmation form is submitted by the responsible party to confirm that all rectification measures have been implemented. Supporting materials for the rectification process are used to prove the authenticity and effectiveness of the rectification process, including equipment calibration records (proving that the data acquisition equipment has been calibrated), process optimization documents (proving that the process has been optimized), and personnel training records (proving that relevant personnel have received training). System compliance verification is used to ensure the completeness and standardization of supporting materials, including file format verification (e.g., whether PDF, DOC, etc., formats meet requirements), mandatory field verification (e.g., whether mandatory fields such as calibration date and calibration personnel are complete), and file integrity verification (e.g., whether the file is damaged and whether the page count is complete). After verification, the rectification completion confirmation form and supporting materials for the rectification process are linked and stored in the knowledge base, forming a closed-loop record of quality issues for subsequent querying and reference.

[0058] Specifically, after rectification is completed, the responsible party logs into the data quality management platform, fills out a rectification completion confirmation form, including the issue number, rectification measures, rectification completion time, and responsible person, and uploads supporting materials for the rectification process (such as scanned copies of equipment calibration records, process optimization documents, and personnel training attendance sheets). The system automatically verifies the compliance of the uploaded supporting materials: checking whether the file format is a system-supported format (such as PDF, JPG, DOC, etc.), checking whether required fields are complete (such as whether calibration date, calibration results, and calibration personnel are filled in in the calibration record), and checking whether the file is complete and readable. After the verification is passed, the system associates and stores the rectification completion confirmation form and supporting materials with the corresponding quality issue record, forming a complete closed-loop record of quality issues. Experience is accumulated in the knowledge base for all employees to refer to and learn from. The compliance confirmation results and closed-loop records should be used as input for the performance evaluation of the data responsibility department, as the basis for internal quality audits, and should be regularly summarized and analyzed before being submitted to the management review meeting for continuous optimization of data governance strategies.

[0059] This method also includes quality assessment and improvement steps. Based on the results of the "six characteristics" assessment, targeted improvement methods are developed to form a closed-loop optimization of "assessment-problem identification-rectification and improvement-reassessment": At the technical level, IoT automatic data collection devices are introduced to replace manual data entry, a data quality platform integrating the "six characteristics" assessment algorithm is built, and data cleaning tools such as deduplication, completion, format standardization, and logical correction are used to process problematic data in batches. A multi-system data synchronization interface is established to ensure data consistency. At the process level, a standardized data entry process is developed and a dual-person verification mechanism is implemented. The data collection cycle and transmission path are optimized and a transmission timeout retry mechanism is set. A data quality rule iteration mechanism is established, embedding data quality requirements into the entire process of data creation, collection, transmission, storage, use, and disposal, and setting quality verification points at each stage. At the institutional and personnel support level, the data responsibility entities of each business object are clarified to implement the principle of "whoever generates the data is responsible," data quality awareness training is conducted to explain the "six characteristics" standards, the "six characteristics" indicators are included in departmental and individual assessments and special rewards are established, and a typical problem case library is established for all employees to refer to and learn from.

[0060] This technical approach focuses on six core dimensions: accuracy, completeness, consistency, timeliness, uniqueness, and effectiveness. Its value lies primarily in providing a scientific and systematic quantitative assessment method for nuclear power data quality. This makes data quality assessment work more evidence-based and provides methodological support for data quality implementation and improvement.

[0061] It is important to note that all judgment thresholds and quantification rules described in this method are configurable parameters. Technical personnel can dynamically adjust and set them according to actual conditions to ensure the applicability of the classification. The effective implementation of this method relies on a clear data governance organizational structure as a guarantee. It is recommended to clarify the specific responsibilities of roles such as 'data owner' (the business department responsible for the data content) and 'data steward' (the technical role responsible for data standards and quality) in each step of this method (such as rule definition, problem identification, root cause analysis, and rectification acceptance) to ensure that the closed-loop management process of assessment, discovery, rectification, and improvement is implemented at the organizational level.

[0062] This invention is the first to construct a standardized evaluation framework for the entire nuclear power data quality chain, consisting of "business priority ranking - six-dimensional quality verification - multi-parameter risk rating - triple standard closed-loop acceptance", which realizes systematic closed-loop management from problem identification to governance verification.

[0063] Improved management efficiency: The governance mechanism based on priority and risk classification enables accurate identification and hierarchical governance of data quality issues, significantly improving the efficiency of management resource allocation and response, and forming a closed loop of sustainable data governance improvement.

[0064] Business value: The assessment results provide reliable and traceable high-quality data support for key business scenarios such as equipment condition monitoring, predictive maintenance, unit reliability management, life assessment and life extension demonstration, thereby improving the economy of unit operation and the scientific nature of decision-making while ensuring nuclear safety.

[0065] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A quantitative assessment technique for data quality of nuclear power equipment, characterized in that, Includes the following steps: Step S1: Obtain multiple business objects in the nuclear power production field, sort each business object by importance, and obtain high-priority business objects, medium-priority business objects, and low-priority business objects; Step S2: Determine the quality assessment rules according to the business object in order of priority from high to low, and perform data quality verification on the data corresponding to the business object according to the quality assessment rules to obtain quality problem records; Step S3: Classify the quality problem records, determine and output the corresponding level of the quality problem records; Step S4: Obtain the rectified data corresponding to the quality problem record, perform data quality verification and compliance determination on the rectified data, and obtain the compliance confirmation result.

2. The quantitative assessment technique for nuclear power equipment data quality according to claim 1, characterized in that, In step S1, ranking the importance of each business object includes: Based on the aforementioned business objects, obtain business importance scores, strategic relevance scores, business operation goal relevance scores, and cross-domain application impact scores; The business importance score, the strategic relevance score, the business operation objective relevance score, and the cross-domain application impact score are summed to obtain the total score for each business object, and the priority of each business object is determined based on the total score.

3. The quantitative assessment technique for nuclear power equipment data quality according to claim 1, characterized in that, In step S2, the data quality verification of the data corresponding to the business object includes: The corresponding data quality dimensions that need to be verified are determined based on the business object, wherein the data quality dimensions include at least one quality assessment indicator; Perform the corresponding verification operation based on the data quality dimension to obtain the quality problem record corresponding to the business object.

4. The quantitative assessment technique for nuclear power equipment data quality according to claim 3, characterized in that, The quality assessment indicators include at least one of the following: accuracy, completeness, consistency, timeliness, uniqueness, and effectiveness.

5. The quantitative assessment technique for nuclear power equipment data quality according to claim 4, characterized in that, The verification operation includes: When the quality assessment indicator is accuracy, at least one of cross-validation, outlier detection algorithm or logical verification method shall be used to perform the verification. When the quality assessment indicator is completeness, at least one of the following methods shall be used to perform the verification: standard dataset comparison method, time series completeness analysis, or correlation data verification method. When the quality assessment index is consistent, at least one of the following methods shall be used to perform the verification: cross-system comparison method, format verification algorithm or historical data tracing method. When the quality assessment indicator is timeliness, at least one of the following methods shall be used to perform the verification: time limit standard verification method, real-time transmission monitoring model or timestamp verification method. When the quality assessment indicator is unique, at least one of the following methods shall be used to perform the verification: primary key index deduplication algorithm, hash algorithm deduplication method, or input permission control verification. When the quality assessment indicator is valid, at least one of the following methods shall be used to perform the verification: business rule verification method, applicability verification method, or equipment status association verification method.

6. The quantitative assessment technique for nuclear power equipment data quality according to claim 1, characterized in that, The step S3 of classifying and determining the quality problem records includes: Obtain at least one grading parameter from the quality problem record; The grading judgment parameters are judged according to the preset grading rules to determine the level corresponding to the quality problem record; The levels include at least one of severe, important, and general levels.

7. The quantitative assessment technique for nuclear power equipment data quality according to claim 6, characterized in that, The preset hierarchical rules include: When the grading judgment parameters meet the first preset condition, the quality problem record is determined to be of the severe level; When the grading determination parameters meet the second preset condition, the quality problem record is determined to be of the importance level; When the grading determination parameters meet the third preset condition, the quality problem record is determined to be of the general level.

8. The quantitative assessment technique for nuclear power equipment data quality according to claim 7, characterized in that, The classification and determination parameters include at least one of the following: equipment association level data, problem type, data exceeding limit value, number of affected systems, data type, business impact degree, economic loss estimate, data priority score, number of recurrences, problem occurrence field, problem occurrence frequency, data access status, data application value, resource consumption status, and quality indicator deviation. The first preset condition includes at least one of the following: the device association level data is nuclear safety level equipment and the problem type belongs to a preset high-risk type; the data type is global master data and the number of affected systems reaches a preset threshold; the data type is emergency response data and affects the initiation of emergency procedures; failure to rectify will lead to preset major consequences. The second preset condition includes at least one of the following: the device association level data is important operational level equipment and the problem type belongs to the preset operational impact type; the data type is core operational data and the business impact reaches the preset level of operational efficiency loss; the quality indicator deviation is the cross-system consistency rate and is lower than the preset threshold and affects business collaboration; the data priority score reaches high priority and the number of recurrences reaches the preset number of times. The third preset condition includes at least one of the following: the device association level data is a general-level device and the problem occurrence field is a non-critical field; the problem occurrence frequency is single and occasional and the data is not called by the business system; the data application value is data redundancy with no actual application value; and the deviation of the quality indicator is within the preset tolerance range and has no chain effect.

9. The quantitative assessment technique for nuclear power equipment data quality according to claim 1, characterized in that, In step S4, the data quality verification and compliance determination of the rectified data to obtain the compliance confirmation result includes: Perform data quality tracking and verification on the rectified data for at least one preset period and obtain the verification results for each period; When the verification results of all cycles meet the preset quality threshold, the compliance confirmation result is determined.

10. The quantitative assessment technique for nuclear power equipment data quality according to claim 9, characterized in that, The process following step S4 also includes: Obtain the rectification completion confirmation form and supporting materials for the rectification process, perform system compliance verification on the supporting materials for the rectification process, and when the verification passes, link and store the two together to form a closed-loop record of quality issues; wherein the supporting materials for the rectification process include at least one of equipment calibration records, process optimization documents, and personnel training records, and the system compliance verification includes at least one of file format verification, mandatory field verification, and file integrity verification.

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