Method and system for measuring full life cycle quality maturity of power grid basic data

Abstract

The invention provides a power grid basic data full life cycle quality maturity measurement method and system. The method comprises the steps of obtaining to-be-evaluated power grid basic data and obtaining a main body stage power grid data quality score set according to a preset index calculation rule; obtaining a main view angle and life cycle stage power grid data quality score set, and obtaining main view angle comprehensive and life cycle stage power grid comprehensive data quality scores; obtaining a comprehensive quality score of the power grid basic data based on a preset weight; and based on the score, a preset power grid data maturity level and a preset threshold sequence, obtaining a subject, a stage maturity level and a comprehensive preliminary level, and based on an access rule, determining a comprehensive quality maturity level corresponding to the to-be-evaluated power grid basic data. According to the power grid basic data full life cycle quality maturity measurement method provided by the invention, the situation that the power grid basic data quality maturity level is divided only through a threshold value is avoided, and the scoring accuracy of the power grid basic data is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid data management and data quality evaluation, and particularly relates to a power grid basic data full life cycle quality maturity measurement method and system. BACKGROUND

[0002] In today's era, digital transformation has become a global consensus and development trend. As one of the key elements of digital transformation of power grid companies, power grid basic data plays an increasingly important role in the construction of new power systems. The quality of power grid basic data is directly related to the safe and stable operation of new power systems. Therefore, scientific and effective measurement and evaluation of the quality of power grid basic data can provide strong support for the construction of new power systems and is an important prerequisite for ensuring the safe and stable operation of power systems. Currently, there are relevant standards for data quality evaluation: GB / T 36344-2018 "Information Technology Data Quality Evaluation Index" specifies the data quality evaluation index, including normativity, completeness, accuracy, consistency, timeliness, and accessibility; ISO 25012 standard defines a general data quality model for computer systems; and ISO 25024 standard further provides a quality measurement method for the data quality model defined in ISO 25012. These standards focus on the quality of data itself and ignore the different demands of different subjects at different stages of the data life cycle, making it impossible to measure the business value of data. At the same time, the data management capability maturity model (CMM, DCMM, etc.) uses a ladder-like hierarchical structure to measure the evolution path of capability, from the initial level of disorder to the optimized level, providing a clear advancement blueprint for organizations. However, traditional maturity evaluation objects are mostly data management capabilities, with less research on the maturity of data quality itself. The perspective is essentially single, and the differentiated quality demands of multiple subjects such as data producers, users, and operators are not explicitly integrated. Based on this, to scientifically and effectively measure the quality of power grid basic data, we need to draw on the ideas of data quality evaluation methods and data management capability maturity models, focus on the characteristics of power grid basic data, combine the evolving value demands of power grid basic data in the process of power grid business digital transformation, and comprehensively analyze the value of power grid basic data in various stages and the use scenarios of various subjects. A gradient-level data use quality maturity model is established, and a corresponding measurement method is proposed. Currently, there are related technical solutions: Solution 1 selects two types of indicators, basic layer and criterion layer, according to the objects to be evaluated; matches the corresponding data evaluation rule set for basic layer data evaluation indicators and criterion layer data evaluation indicators, and assigns weight values W and expected values E to each criterion layer data evaluation indicator; and performs two-layer verification on the preprocessed power grid basic data objects, finally calculates a comprehensive evaluation result. The evaluation and verification process uses multiple quality dimensions to reflect the quality level of data resources, to more accurately evaluate the quality of data; Solution 2 proposes a data quality dynamic evaluation analysis method and system based on fusion factors, the core of which is to realize dynamic evaluation of data quality through time decay function and improved evidence theory.The technical points include constructing a multi-dimensional quality factor set, dynamically adjusting the evaluation period based on a time decay function to adapt to different stages of the data life cycle, using an improved D-S evidence theory to solve multi-factor conflicts, and generating a comprehensive quality score based on non-conflicting factors and an improved entropy weight-TOPSIS algorithm; the weight threshold is self-adaptively adjusted by the standard deviation of the historical score to optimize the evaluation stability.

[0003] Under the prior art background, the prior art scheme still has obvious deficiencies in the application of power grid basic data quality measurement: a unified and static weight system is adopted, the differentiated needs of different business roles for data quality are not considered, and the value change in the whole life cycle process from data generation to disappearance cannot be adapted. In addition, the output is a static comprehensive score, which can only reflect the current quality state of the data, and cannot reflect the value play degree of the data in various links and use scenarios of various subjects; only the quality index dimension (such as accuracy, consistency, etc.) is used to evaluate the data quality, and the differentiated needs of different roles such as data producers, users and operators are not considered. In addition, the division of the life cycle into three stages of collection period, active period and archiving period is relatively rough, and the quality of the power grid basic data in each stage of the whole life cycle cannot be accurately measured. SUMMARY

[0004] The present application aims to provide a power grid basic data whole life cycle quality maturity measurement method and system to solve the above technical problems and improve the scoring accuracy of power grid basic data by avoiding the division of power grid basic data quality maturity level only by threshold.

[0005] To solve the above technical problems, the present application provides a power grid basic data whole life cycle quality maturity measurement method, comprising: acquiring power grid basic data to be evaluated; calculating the power grid basic data to be evaluated based on a preset index calculation rule to obtain an initial power grid data quality score set; obtaining a subject stage power grid data quality score set based on the initial power grid data quality score set; obtaining a subject perspective power grid data quality score set and a life cycle stage power grid data quality score set based on the subject stage power grid data quality score set; obtaining a subject perspective power grid comprehensive data quality score and a life cycle stage power grid comprehensive data quality score based on the subject perspective power grid data quality score set and the life cycle stage power grid data quality score set; obtaining a power grid basic data comprehensive quality score based on a preset subject perspective weight, a preset life cycle stage weight, the subject perspective power grid comprehensive data quality score and the life cycle stage power grid comprehensive data quality score; obtaining the subject maturity level, the stage maturity level and the preliminary comprehensive level based on the subject maturity level, the stage maturity level, the preliminary comprehensive level and the preset maturity level access rule, determining the comprehensive quality maturity level corresponding to the to-be-evaluated power grid basic data, and realizing the measurement of the quality maturity of the power grid basic data in the whole life cycle. obtaining the subject maturity level, the stage maturity level and the preliminary comprehensive level based on the subject maturity level, the stage maturity level, the preliminary comprehensive level and the preset maturity level access rule, determining the comprehensive quality maturity level corresponding to the to-be-evaluated power grid basic data, and realizing the measurement of the quality maturity of the power grid basic data in the whole life cycle.

[0006] In the above scheme, the subject stage power grid data quality score set is aggregated according to the data quality subject dimension to obtain the subject perspective power grid data quality score set, and the subject stage power grid data quality score set is aggregated according to the life cycle stage dimension to obtain the life cycle stage power grid data quality score set, realizing the disintegration from 'two-dimensional score' to'single dimension score', meeting the demand of analyzing power grid data quality from different perspectives. Then, the subject perspective power grid comprehensive data quality score and the life cycle stage power grid comprehensive data quality score are calculated, the scattered single subject perspective score and single life cycle stage score are integrated respectively to obtain the subject perspective power grid comprehensive data quality score and the life cycle stage power grid comprehensive data quality score, providing key input for subsequent calculation of the power grid basic data comprehensive quality score. Subsequently, the power grid basic data comprehensive quality score is obtained by weighting calculation of the subject perspective power grid comprehensive data quality score and the life cycle stage power grid comprehensive data quality score through the preset subject perspective weight and the preset life cycle stage weight, realizing the overall quantitative evaluation of the power grid basic data quality, and being able to intuitively reflect the overall quality level of the power grid basic data. Further, through the subject perspective power grid data quality score set, the life cycle stage power grid data quality score set and the power grid basic data comprehensive quality score, the preset power grid data maturity level and the preset threshold sequence are compared to respectively determine the subject maturity level, the stage maturity level and the preliminary comprehensive level, providing a preliminary basis for subsequent determination of the comprehensive quality maturity level corresponding to the to-be-evaluated power grid basic data, realizing the preliminary classification of the power grid basic data quality level. Finally, through the subject maturity level, the stage maturity level and the preliminary comprehensive level, combined with the preset maturity level access rule, the maturity level meeting the rule is selected as the comprehensive quality maturity level corresponding to the to-be-evaluated power grid basic data, ensuring to meet the actual requirements of power grid business, avoiding the division of power grid basic data quality maturity level only through threshold, and improving the scoring accuracy of the power grid basic data.

[0007] Further, the subject stage power grid data quality score set is obtained based on the initial power grid data quality score set, and the subject stage power grid data quality score set comprises: The subjective weight vector obtaining operation and the objective weight vector obtaining operation are respectively performed on the initial power grid data quality score set to obtain a first subjective weight vector and a first objective weight vector; Based on the initial power grid data quality score set, the first subjective weight vector and the first objective weight vector, a subject stage power grid data quality score set is obtained.

[0008] In the above scheme, by obtaining the first subjective weight vector and the first objective weight vector, the subjective and objective basis can be provided for subsequent comprehensive weight calculation, and the one-sidedness of a single weight is avoided. Then, by the initial power grid data quality score set, the first subjective weight vector and the first objective weight vector, the subject stage power grid data quality score set is obtained, the conversion of the data quality index score to the "subject-life cycle stage" two-dimensional score is realized, and the foundation is laid for subsequent splitting of the subject perspective and the life cycle stage score.

[0009] Further, the subjective weight vector obtaining operation and the objective weight vector obtaining operation are respectively performed on the initial power grid data quality score set to obtain a first subjective weight vector and a first objective weight vector; the subjective weight vector obtaining operation comprises: Based on the initial power grid data quality score set, a first factor and a second factor are obtained; Based on the first factor and other factors, a first fuzzy judgment vector is obtained; Based on the other factors and the second factor, a second fuzzy judgment vector is obtained; Based on the first fuzzy judgment vector and the second fuzzy judgment vector, a nonlinear constraint optimization model is established; Based on a preset constraint condition, the nonlinear constraint optimization model is solved with the minimum judgment error as the target to obtain a fuzzy weight vector; Based on the fuzzy weight vector, the first subjective weight vector is obtained.

[0010] In the above scheme, the first factor and the second factor are selected from the initial power grid data quality score set, which provides a reference benchmark for subsequent pairwise comparison, avoiding the lack of primary and secondary orientation in weight allocation. Then, the first factor and other factors are compared pairwise to obtain the first fuzzy judgment vector, which is converted into a quantifiable fuzzy value to provide a subjective judgment basis for subsequent weight calculation. Next, the other factors and the second factor are compared pairwise to obtain the second fuzzy judgment vector, which further improves the data dimension of subjective judgment. Subsequently, the first fuzzy judgment vector and the second fuzzy judgment vector are used to construct a nonlinear constraint optimization model, which converts subjective judgment into a mathematical optimization problem to provide model support for scientific weight solution. Further, by presetting the constraint condition, the nonlinear constraint optimization model is solved with the objective of minimizing the judgment error, and the obtained fuzzy weight vector realizes the conversion of the quantified weight of the subjective judgment vector. Finally, the fuzzy weight vector is converted into the first subjective weight vector, which provides standardized subjective weight data for subsequent combination with objective weight.

[0011] Further, the initial power grid data quality score set is subjected to a subjective weight vector acquisition operation and an objective weight vector acquisition operation respectively to obtain a first subjective weight vector and a first objective weight vector; the objective weight vector acquisition operation comprises: acquiring a historical subject stage power grid data quality score set; standardizing the historical subject stage power grid data quality score set and calculating information entropy; based on the information entropy, obtaining the first objective weight vector.

[0012] In the above scheme, the historical subject stage power grid data quality score set is acquired to provide sufficient historical data support for subsequent calculation of objective weight, avoiding the reliance on current data only, and ensuring that the weight can reflect the long-term data quality characteristic law. Then, the historical subject stage power grid data quality score set is standardized to eliminate dimensional differences, and the information entropy is calculated to quantify the dispersion degree of each data quality index, providing a quantitative basis for objective weight calculation from the data characteristic level. Then, the information entropy is converted into the first objective weight vector, ensuring that the weight can objectively reflect the index importance determined by the data itself characteristics, avoiding the one-sidedness of subjective experience.

[0013] Further, the initial power grid data quality score set, the first subjective weight vector and the first objective weight vector are used to obtain a subject stage power grid data quality score set; comprising: based on the first subjective weight vector and the first objective weight vector, obtaining a first comprehensive weight vector; based on the initial power grid data quality score set and the comprehensive weight vector, obtaining a weighted initial power grid data quality score set; determine a positive ideal solution set and a negative ideal solution set based on the set of weighted initial power grid data quality scores; obtain a subject-stage power grid data quality score set based on the set of weighted initial power grid data quality scores, the positive ideal solution set, and the negative ideal solution set.

[0014] In the above scheme, the first comprehensive weight vector is obtained through the first subjective weight vector and the first objective weight vector, which realizes the fusion of subjective and objective weights and avoids the one-sidedness of a single weight. Then, the set of weighted initial power grid data quality scores is obtained through the set of initial power grid data quality scores and the first comprehensive weight vector, which converts the original scores of the indicators into weighted scores reflecting the differences in weights, laying a foundation for the subsequent determination of ideal solutions and calculation of the subject-stage power grid data quality score set. Then, the positive ideal solution set and the negative ideal solution set are determined through the set of weighted initial power grid data quality scores, which establishes a benchmark for quality scoring and provides a reference standard for measuring the data quality level to be evaluated. Subsequently, the subject-stage power grid data quality score set is obtained through the set of weighted initial power grid data quality scores, the positive ideal solution set, and the negative ideal solution set, which realizes the conversion from the weighted scores of the indicators to the quality scores at the "subject-stage" level and provides core data for the subsequent splitting of the subject perspective and the life cycle stage scores.

[0015] Further, the subject-stage power grid data quality score set is obtained based on the subject-stage power grid data quality score set, including: performing a subjective weight vector obtaining operation and an objective weight vector obtaining operation on the subject-stage power grid data quality score set to obtain a second subjective weight vector and a second objective weight vector; obtain the subject perspective power grid data quality score set based on the subject-stage power grid data quality score set, the second subjective weight vector, and the second objective weight vector; performing a subjective weight vector obtaining operation and an objective weight vector obtaining operation on the subject-stage power grid data quality score set to obtain a third subjective weight vector and a third objective weight vector; obtain the life cycle stage power grid data quality score set based on the subject-stage power grid data quality score set, the third subjective weight vector, and the third objective weight vector.

[0016] In the above scheme, the second subjective weight vector and the second objective weight vector are obtained by respectively performing the subjective weight vector obtaining operation and the objective weight vector obtaining operation on the subject-stage power grid data quality score set, which provides the subjective and objective basis for subsequent calculation of the comprehensive weight under the subject perspective, and ensures that the weight fits the actual needs of the subject for the stage. Then, the subject-perspective power grid data quality score set is obtained through the subject-stage power grid data quality score set, the second subjective weight vector and the second objective weight vector, realizing the conversion from the "subject-stage" two-dimensional score to the "single subject perspective" score, and meeting the needs of analyzing the data quality from the subject dimension. Then, the third subjective weight vector and the third objective weight vector are obtained by respectively performing the subjective weight vector obtaining operation and the objective weight vector obtaining operation on the subject-stage power grid data quality score set, which provides the subjective and objective basis for subsequent calculation of the comprehensive weight under the life cycle stage perspective, and ensures that the weight fits the actual needs of the life cycle stage for the subject. Finally, the life cycle stage power grid data quality score set is obtained through the subject-stage power grid data quality score set, the third subjective weight vector and the third objective weight vector, realizing the conversion from the "subject-stage" two-dimensional score to the "single life cycle stage" score, and meeting the needs of analyzing the data quality from the life cycle dimension.

[0017] Further, the subject-perspective power grid comprehensive data quality score and the life cycle stage power grid comprehensive data quality score are obtained based on the subject-perspective power grid data quality score set and the life cycle stage power grid data quality score set; comprising: The fourth subjective weight vector and the fourth objective weight vector are obtained by respectively performing the subjective weight vector obtaining operation and the objective weight vector obtaining operation on the subject-perspective power grid data quality score set; The subject-perspective power grid comprehensive data quality score is obtained based on the subject-perspective power grid data quality score set, the fourth subjective weight vector and the fourth objective weight vector; The fifth subjective weight vector and the fifth objective weight vector are obtained by respectively performing the subjective weight vector obtaining operation and the objective weight vector obtaining operation on the life cycle stage power grid data quality score set; The life cycle stage power grid comprehensive data quality score is obtained based on the life cycle stage power grid data quality score set, the fifth subjective weight vector and the fifth objective weight vector.

[0018] In the scheme, the fourth subjective weight vector and the fourth objective weight vector are acquired to provide scientific subjective and objective weight basis for subsequent calculation of the subject perspective comprehensive score. Then, the subject perspective scores are aggregated based on the subject perspective power grid data quality score set, the fourth subjective weight vector and the fourth objective weight vector to acquire the subject perspective power grid comprehensive data quality score, realize the integration from the "single subject perspective score" to the "overall subject perspective comprehensive score", and intuitively reflect the overall level of the power grid data quality under the multiple subject perspectives. Then, the fifth subjective weight vector and the fifth objective weight vector are acquired by respectively performing the subjective weight vector acquisition operation and the objective weight vector acquisition operation on the life cycle stage power grid data quality score set, to provide scientific subjective and objective weight basis for subsequent calculation of the life cycle stage comprehensive score. Finally, the life cycle stage scores are aggregated based on the life cycle stage power grid data quality score set, the fifth subjective weight vector and the fifth objective weight vector to acquire the life cycle stage power grid comprehensive data quality score, realize the integration from the "single life cycle stage score" to the "overall life cycle stage comprehensive score", and intuitively reflect the overall level of the power grid data quality in the whole life cycle process.

[0019] The application provides a power grid basic data whole life cycle quality maturity measurement system, which comprises a data acquisition module, an initial calculation module, a subject stage score extraction module, a multi-dimensional data splitting module, a double-dimensional comprehensive calculation module, a comprehensive quality score calculation module, a maturity level preliminary determination module and a maturity level final determination module, and specifically comprises the following: The data acquisition module is used for acquiring power grid basic data to be evaluated. The initial calculation module is used for calculating the power grid basic data to be evaluated based on a preset index calculation rule to acquire an initial power grid data quality score set. The subject stage score extraction module is used for acquiring a subject stage power grid data quality score set based on the initial power grid data quality score set. The multi-dimensional data splitting module is used for acquiring a subject perspective power grid data quality score set and a life cycle stage power grid data quality score set based on the subject stage power grid data quality score set. The double-dimensional comprehensive calculation module is used for acquiring a subject perspective power grid comprehensive data quality score and a life cycle stage power grid comprehensive data quality score based on the subject perspective power grid data quality score set and the life cycle stage power grid data quality score set. The comprehensive quality score calculation module is used for acquiring a power grid basic data comprehensive quality score based on a preset subject perspective weight, a preset life cycle stage weight, the subject perspective power grid comprehensive data quality score and the life cycle stage power grid comprehensive data quality score. The maturity level preliminary determination module is configured to obtain a subject maturity level, a stage maturity level and a comprehensive preliminary level based on the subject perspective power grid data quality score set, the life cycle stage power grid data quality score set, the power grid basic data comprehensive quality score, a preset power grid data maturity level and a preset threshold sequence. The maturity level final determination module is configured to determine a comprehensive quality maturity level corresponding to the power grid basic data to be evaluated based on the subject maturity level, the stage maturity level, the comprehensive preliminary level and a preset maturity level access rule, so as to realize measurement of the full life cycle quality maturity of the power grid basic data.

[0020] The application provides a power grid basic data full life cycle quality maturity measurement system, in actual application, only a multi-dimensional data splitting module is adopted, main stage power grid data quality score sets are aggregated according to data quality main body dimensions, main body perspective power grid data quality score sets are obtained, main stage power grid data quality score sets are aggregated according to life cycle stage dimensions, life cycle stage power grid data quality score sets are obtained, the decomposition from 'two-dimensional score' to'single-dimensional score' is realized, and the demand of analyzing power grid data quality from different perspectives is met. Then a double-dimensional comprehensive calculation module is adopted, main body perspective power grid comprehensive data quality scores and life cycle stage power grid comprehensive data quality scores are calculated, the scattered single main body perspective scores and single life cycle stage scores are integrated respectively, the main body perspective power grid comprehensive data quality scores and the life cycle stage power grid comprehensive data quality scores are obtained, and key inputs for subsequent calculation of power grid basic data comprehensive quality scores are provided. Subsequently, a comprehensive quality score calculation module is adopted, the main body perspective power grid comprehensive data quality scores and the life cycle stage power grid comprehensive data quality scores are weighted and calculated through preset main body perspective weights and preset life cycle stage weights, power grid basic data comprehensive quality scores are obtained, overall quantitative evaluation of power grid basic data quality is realized, and the overall quality level of power grid basic data can be directly reflected. Further, a maturity level preliminary determination module is adopted, the main body perspective power grid data quality score sets, the life cycle stage power grid data quality score sets and the power grid basic data comprehensive quality scores are compared with preset power grid data maturity levels and preset threshold sequences, the main body maturity level, the stage maturity level and the comprehensive preliminary level are respectively determined, preliminary basis for determination of the comprehensive quality maturity level corresponding to the subsequent power grid basic data to be evaluated is provided, and preliminary classification of the power grid basic data quality level is realized. Finally, a maturity level final determination module is adopted, the main body maturity level, the stage maturity level and the comprehensive preliminary level are combined, the maturity level meeting the rules is screened out as the comprehensive quality maturity level corresponding to the power grid basic data to be evaluated in combination with preset maturity level access rules, it is ensured that the actual requirements of power grid business are met, it is avoided that only the threshold is used to divide the power grid basic data quality maturity level, and the scoring accuracy of the power grid basic data is improved.

[0021] Further, the main stage score extraction module is used for obtaining the main stage power grid data quality score set based on the initial power grid data quality score set, and includes: The subjective weight vector acquisition operation and the objective weight vector acquisition operation are respectively performed on the initial power grid data quality score set, and the first subjective weight vector and the first objective weight vector are obtained. The main stage power grid data quality score set is obtained based on the initial power grid data quality score set, the first subjective weight vector and the first objective weight vector.

[0022] In the above scheme, by obtaining the first subjective weight vector and the first objective weight vector, the subjective and objective basis can be provided for subsequent comprehensive weight calculation, and the one-sidedness of single weight is avoided. Then, by the initial power grid data quality score set, the first subjective weight vector and the first objective weight vector, the subject stage power grid data quality score set is obtained, the conversion of the data quality index score to the "subject-life cycle stage" two-dimensional score is realized, and the foundation for subsequent splitting of the subject perspective and the life cycle stage score is laid.

[0023] Further, the initial power grid data quality score set is subjected to a subjective weight vector obtaining operation and an objective weight vector obtaining operation respectively to obtain the first subjective weight vector and the first objective weight vector; the subjective weight vector obtaining operation comprises: Based on the initial power grid data quality score set, a first factor and a second factor are obtained; Based on the first factor and other factors, a first fuzzy judgment vector is obtained; Based on the other factors and the second factor, a second fuzzy judgment vector is obtained; Based on the first fuzzy judgment vector and the second fuzzy judgment vector, a nonlinear constraint optimization model is established; Based on a preset constraint condition, the nonlinear constraint optimization model is solved with the minimum judgment error as the target to obtain a fuzzy weight vector; Based on the fuzzy weight vector, the first subjective weight vector is obtained.

[0024] In the above scheme, by the initial power grid data quality score set, the first factor and the second factor are screened out to provide a reference benchmark for subsequent pairwise comparison and avoid the lack of primary and secondary guidance in weight allocation. Then, by pairwise comparison of the first factor and other factors, the first fuzzy judgment vector is obtained, which is converted into a quantifiable fuzzy value to provide a subjective judgment basis for subsequent weight calculation. Next, by pairwise comparison of the other factors and the second factor, the second fuzzy judgment vector is obtained to further improve the subjective judgment data dimension. Subsequently, by the first fuzzy judgment vector and the second fuzzy judgment vector, a nonlinear constraint optimization model is constructed to convert the subjective judgment into a mathematical optimization problem and provide model support for scientific weight solving. Further, by a preset constraint condition, the nonlinear constraint optimization model is solved with the minimum judgment error as the target to obtain a fuzzy weight vector, which realizes the conversion of the subjective judgment vectorization weight. Finally, the fuzzy weight vector is converted into the first subjective weight vector to provide standardized subjective weight data for subsequent combination with the objective weight. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 A schematic diagram of a power grid basic data quality maturity model provided by an embodiment of the present application; Figure 2A flow chart of a power grid basic data full life cycle quality maturity measurement method provided by an embodiment of the present application is provided. Figure 3 An architecture diagram of a power grid basic data full life cycle quality maturity measurement system provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0027] As shown in Figure 1 The three dimensions of the power grid basic data quality maturity model are: data quality index dimension, data quality subject dimension and data quality process dimension.

[0028] Data quality indicator dimension is the basis of model and measurement method. Through defining the evaluation indicators that measure the inherent quality of data fields, it reflects the pros and cons of data in the content level. According to the commonly used data quality dimensions in the industry and combined with the characteristics of power grid basic data, the following core indicators are defined: 1. Integrity: refers to the degree to which data elements are assigned values according to data rules. Integrity measures whether power grid basic data is recorded and stored completely, whether there are missing or null values. The analysis and decision of power grid advanced application depends on the integrity of power grid basic data. 2. Accuracy: refers to the degree to which data accurately represents the true value of the real entity (actual object) it describes. Accuracy measures the closeness of power grid basic data value to the true value. The safe and stable operation of power grid depends on the accuracy of power grid basic data. 3. Standardization: refers to the degree to which data conforms to data standards, data models, business rules, metadata or authoritative reference data. Standardization focuses on the unity of power grid data structure and form. Data sharing between power grid heterogeneous systems cannot be separated from the standardization of power grid basic data. 4. Consistency: refers to the degree to which data is consistent with other data used in a specific context. Consistency focuses on the consistency of power grid basic data values and logic in different systems, different links and different tables. Power grid data is usually distributed in multiple professional systems, and the development of multi-source fusion cross-business advanced application depends on the consistency of power grid basic data. 5. Uniqueness: refers to the degree to which data is not repeated within a specific range. Uniqueness measures whether there are repeated elements in power grid basic data. The accurate identification of each device node and logical node constituting the power grid depends on the uniqueness of power grid basic data. 6. Timeliness: refers to the correctness of data in time change. Timeliness focuses on the degree to which the timestamp, frequency distribution, delay time and time sequence relationship of power grid basic data meet the business requirements. The running state of power grid changes rapidly, so the timeliness requirement of power grid basic data is very high. 7. Accessibility: refers to the degree to which data can be accessed. Accessibility measures the ease and efficiency of authorized users or systems accessing power grid basic data. The realization of the value of power grid basic data and the development of business depend on its accessibility. 8. Reliability: refers to the reliability of the source and the process of data collection, transmission and processing. Reliability focuses on the stability and credibility of the power grid basic data supply process. The monitoring, analysis and control of power grid depend on continuous and stable data flow, i.e. the reliability of power grid basic data.

[0029] The data quality subject dimension introduces the perspective of the subject of power grid basic data use, thereby changing the data quality evaluation from a pure technical problem to a problem closely related to business needs. In a power grid enterprise, the entire life cycle of data from generation to extinction mainly involves three types of subjects: 1. Data producer: refers to an entity or system that generates, collects or creates data, which can include sensors, applications, database systems, devices, etc. The data producer is mainly responsible for generating, collecting and delivering raw data to the data management system in the data collection stage. 2. Data user: refers to an individual or system that uses data within or outside the enterprise to support decision-making, analysis, reporting or other business activities, which can include business analysts, decision-makers, applications, etc. In the data processing and use stage, the data user uses the prepared data to analyze, explore and make decisions. 3. Data operator: refers to an individual or system responsible for applying data to actual business scenarios within or outside the enterprise. This can include developers, application designers, business systems, etc. In the data use and exchange stage, the data operator uses the prepared data to design and develop applications, business systems, and apply data to actual business processes.

[0030] The data quality process dimension places the evaluation of data quality in the context of the entire life cycle of data, and the evaluation activities are carried out throughout each stage of the data from generation to extinction. This embodiment combines the characteristics of power grid basic data and divides the data life cycle into the following six stages: 1. Data collection: refers to the process of obtaining raw data through sensors, devices, manual entry, etc. 2. Data transmission: refers to the transmission process of data from the collection end to the storage or processing end. 3. Data storage: refers to the process of persisting data to storage media such as databases, data warehouses, etc. 4. Data processing: refers to the process of cleaning, converting, calculating, aggregating, etc. of stored data. 5. Data exchange and use: refers to the process of sharing and exchanging data between different systems or entities, and applying it to business analysis, decision support, operation control, etc. to realize the value of data. 6. Data destruction: refers to the process of securely destroying expired or useless data.

[0031] The embodiment provides a power grid basic data full life cycle quality maturity measurement method, and a flowchart thereof is shown in Figure 2 , which comprises: Step S1: obtaining power grid basic data to be evaluated; Step S2: calculating the power grid basic data to be evaluated based on the preset index calculation rule to obtain an initial power grid data quality score set; Step S3: obtaining a subject stage power grid data quality score set based on the initial power grid data quality score set; Step S4: Based on the power grid data quality score set for the main phase, obtain the power grid data quality score set from the main perspective and the power grid data quality score set for the life cycle phase; Step S5: Based on the power grid data quality score set from the subject perspective and the power grid data quality score set for each life cycle stage, obtain the comprehensive power grid data quality score from the subject perspective and the comprehensive power grid data quality score for each life cycle stage. Step S6: Based on the preset subject perspective weight, preset life cycle stage weight, subject perspective power grid comprehensive data quality score and life cycle stage power grid comprehensive data quality score, obtain the power grid basic data comprehensive quality score; Step S7: Based on the power grid data quality score set from the subject's perspective, the power grid data quality score set for each life cycle stage, the comprehensive quality score of the power grid basic data, the preset power grid data maturity level, and the preset threshold sequence, obtain the subject maturity level, the stage maturity level, and the comprehensive preliminary level. Step S8: Based on the main body maturity level, stage maturity level, comprehensive preliminary level and preset maturity level admission rules, determine the comprehensive quality maturity level corresponding to the power grid basic data to be evaluated, so as to realize the measurement of the quality maturity of the power grid basic data throughout the entire life cycle.

[0032] In this embodiment, for ease of explanation, the main dimension of data quality is defined as follows: (m=3), define the data quality process dimension as (n=6), define the data quality indicator dimensions as follows: (p=8). Based on the preset index calculation rules, the basic data of the power grid to be evaluated are calculated to obtain the index of the basic data of the power grid to be evaluated in index dimension I. ( Initial power grid data quality score at p=8) This process yields an initial set of power grid data quality scores. The power grid data quality scores for the main phase are then aggregated according to the main data quality dimension to obtain a main-perspective set. Similarly, the power grid data quality scores for the main phase are aggregated according to the lifecycle phase dimension to obtain a lifecycle phase set. This decomposition from "two-dimensional scores" to "single-dimensional scores" meets the need for analyzing power grid data quality from different perspectives. Next, the comprehensive power grid data quality score from the main perspective and the comprehensive power grid data quality score for the lifecycle phase are calculated. The dispersed single-subject-perspective scores and single-lifecycle-phase scores are then integrated to obtain the comprehensive power grid data quality score from the main perspective and the comprehensive power grid data quality score for the lifecycle phase, providing crucial input for subsequent calculations of the comprehensive quality score of basic power grid data. Finally, the main perspective weights are preset. Preset lifecycle stage weights ( ), and the overall data quality score of the power grid from the perspective of the main body. and the overall data quality score of the power grid at each stage of its life cycle Perform weighted calculation: The overall quality score of the power grid basic data is obtained. This enables a comprehensive quantitative assessment of the quality of basic power grid data, providing a direct reflection of its overall quality level. Furthermore, it allows for the aggregation of power grid data quality scores from a subject-specific perspective. Lifecycle stage power grid data quality score set Combine the comprehensive quality score of the power grid basic data with the preset power grid data maturity level and preset threshold sequence. The maturity levels of the main bodies are defined respectively. , Stage Maturity Level , and comprehensive preliminary level , Among them, the threshold score satisfies , It is the minimum value that the quality score can reach. This is the maximum value that the quality score can reach. It can provide a preliminary basis for determining the comprehensive quality maturity level corresponding to the power grid basic data to be evaluated, and achieve a preliminary classification of the power grid basic data quality level. Q maturity levels were defined. Finally, the main maturity levels were determined. Stage Maturity Level Comprehensive preliminary level Combined with the preset maturity level admission rules (specifically: if the overall quality maturity level is...) for However, if the admission rule function for the level is not met, the overall quality maturity level will be downgraded to [level]. Or lower, until the admission rule function of the level is met (admission rule function of the level): ,in The level does not exceed Meeting the admission rules The effective level in. It is a level The admission rule function, the input is and The output is Or False (when the actual and Satisfying the rules and The level range is , i.e. admit the level L, otherwise False, then downgrade the level, so as to screen out the maturity level meeting the rule , as the corresponding comprehensive quality maturity level of the grid foundation data to be evaluated, to ensure that it meets the actual requirements of the grid business, and avoid dividing the grid foundation data quality maturity level by threshold only, highlighting the important role of certain subjects or life cycle stages in the grid foundation data quality evaluation.

[0033] Specifically, the preset maturity level admission rule should meet the following conditions: first, the rule must be quantifiable, determinable and unambiguous, and should avoid ambiguous expressions such as "higher level" and "important stage". The rule should be expressed using the data quality maturity level of each subject perspective and the data quality maturity level of each life cycle stage . For example, the admission rule of the comprehensive quality maturity level may be: all subject perspective grid data quality maturity levels are not less than and the data quality maturity levels of the data collection, processing, exchange and use stages are not less than . Second, the rule must be progressive. If the admission rule of the higher level is met, the admission rule of the lower level must also be met. Third, no admission rule should be set for .

[0034] Further, the subject stage grid data quality score set is obtained based on the initial grid data quality score set, comprising: performing a subjective weight vector obtaining operation and an objective weight vector obtaining operation on the initial grid data quality score set respectively to obtain a first subjective weight vector and a first objective weight vector; obtaining the subject stage grid data quality score set based on the initial grid data quality score set, the first subjective weight vector and the first objective weight vector.

[0035] In this embodiment, the initial grid data quality scores are aggregated according to the comprehensive data quality subject dimension S and the data quality process dimension L. For a subject , according to the different degrees of attention of the subject to the indicators of the data life cycle stage , the subjective weight vector obtaining operation is performed on the initial grid data quality score set, and the weight is configured for the indicators : by obtaining the first subjective weight vector and the first objective weight vector, the subjective and objective basis can be provided for subsequent comprehensive weight calculation, avoiding the one-sidedness of a single weight. Then, by the first subjective weight vector and the first objective weight vector, the first comprehensive weight vector is obtained, and combined with the initial grid data quality score set Obtain the set of power grid data quality scores for the main phase This enables the transformation of data quality indicator scores into a two-dimensional score of "subject-lifecycle stage," laying the foundation for subsequent separation of subject perspective and lifecycle stage scores.

[0036] Furthermore, subjective weight vector acquisition operations and objective weight vector acquisition operations are performed on the initial power grid data quality score set to obtain a first subjective weight vector and a first objective weight vector; the subjective weight vector acquisition operation includes: Based on the initial set of power grid data quality scores, the first and second factors are obtained; Based on the first factor and other factors, obtain the first fuzzy judgment vector; Based on other factors and the second factor, obtain the second fuzzy judgment vector; A nonlinear constraint optimization model is established based on the first fuzzy judgment vector and the second fuzzy judgment vector. Based on preset constraints, the nonlinear constraint optimization model is solved with the goal of minimizing the judgment error, and the fuzzy weight vector is obtained. The first subjective weight vector is obtained based on the fuzzy weight vector.

[0037] In this embodiment, the fuzzy optimal and worst method is used to determine the subjective weight vector: through the initial set of power grid data quality scores, in a factor set with p factors to be weighted (the factors to be weighted are the 8 core indicators of the data quality index dimension). Select the most important factors As the first factor and the least important factor As a second factor, it provides a reference benchmark for subsequent pairwise comparisons, avoiding a lack of primary and secondary guidance in weight allocation. Then, using triangular fuzzy numbers, the selected factors that have passed the first factor are... Other factor The importance of each molecule is compared pairwise to obtain the first fuzzy judgment vector, which is then converted into a quantifiable fuzzy value to provide a subjective judgment basis for subsequent weight calculations. It is the primary factor. Other factors Compared to the corresponding triangular fuzzy number; then, other factors... With the selected second factor The importance of each pair of elements is compared to obtain the second fuzzy judgment vector. This will further refine the data dimensions for subjective judgment. Other factors With the second factor Compared to the corresponding triangular fuzzy number; the triangular fuzzy number can be determined through Table 1: Table 1 correspondence rules of importance degree and triangular fuzzy number Subsequently, a nonlinear constraint optimization model is constructed by the first fuzzy judgment vector and the second fuzzy judgment vector, and subjective judgment is converted into a mathematical optimization problem to provide model support for scientific solution of the weight, specifically as follows: wherein, is a judgment error, used to measure the consistency degree between subjective judgments. is a fuzzy weight of the first factor, is a fuzzy weight of the second factor. By presetting a constraint condition, the nonlinear constraint optimization model is solved to obtain a fuzzy weight vector wherein is the optimal fuzzy weight of the factor k, realizing the conversion of the subjective judgment vectorization weight. Finally, the fuzzy weight vector is converted into the first subjective weight vector: the fuzzy weight is converted into a real value , , and then the gradient average comprehensive expression method is used to defuzzify to obtain the final subjective weight vector , thereby providing standardized subjective weight data for subsequent combination with the objective weight.

[0038] Further, the initial power grid data quality score set is subjected to a subjective weight vector acquisition operation and an objective weight vector acquisition operation to obtain a first subjective weight vector and a first objective weight vector; the objective weight vector acquisition operation includes: acquiring a historical subject stage power grid data quality score set; standardizing the historical subject stage power grid data quality score set to calculate information entropy; based on the information entropy, acquiring the first objective weight vector.

[0039] In the embodiment, the historical subject stage power grid data quality score set is acquired to provide sufficient historical data support for subsequent calculation of the objective weight, avoid relying only on current data, and ensure that the weight can reflect the long-term data quality characteristic law. The data in the historical subject stage power grid data quality score set is subjected to matrix standardization processing. Assuming that there are g data sources and p factors to be assigned weight (the factors to be assigned weight are 8 core indexes of the data quality index dimension), the original matrix formed is as follows: wherein denotes the quality score of the dth data source under the kth to-be-weighted factor. Then, the historical subject stage power grid data quality score set is standardized. Since the to-be-weighted factors in this embodiment are all positive indicators, the standardization formula is: so as to eliminate the dimensional difference, and the information entropy is calculated again. First, the index proportion is calculated, and the formula is: wherein, is the proportion of the dth data source in the kth to-be-weighted factor; then the information entropy is calculated, and the formula is: wherein is the entropy value of the to-be-weighted factor , which quantifies the dispersion degree of each data quality indicator through information entropy, and provides a quantitative basis for data characteristics at the level of objective weight calculation. Then, the information entropy is converted into the first objective weight vector: the weight of each to-be-weighted factor is determined, and the formula is: wherein, is the objective weight of the fth to-be-weighted factor; the final objective weight vector is obtained, which ensures that the weight objectively reflects the importance of the indicator determined by the characteristics of the data itself, and avoids the one-sidedness of subjective experience.

[0040] Further, the subject stage power grid data quality score set is obtained based on the initial power grid data quality score set, the first subjective weight vector and the first objective weight vector; including: a first comprehensive weight vector is obtained based on the first subjective weight vector and the first objective weight vector; a weighted initial power grid data quality score set is obtained based on the initial power grid data quality score set and the comprehensive weight vector; a positive ideal solution set and a negative ideal solution set are determined based on the weighted initial power grid data quality score set; a subject stage power grid data quality score set is obtained based on the weighted initial power grid data quality score set, the positive ideal solution set and the negative ideal solution set.

[0041] In this embodiment, the first comprehensive weight vector is obtained by combining the first subjective weight vector and the first objective weight vector, with a preset subjective weight preference coefficient a and an objective weight preference coefficient β: , , p = 8 (the to-be-weighted factor is 8 core indicators of the data quality indicator dimension), which realizes the fusion of subjective and objective weights and avoids the one-sidedness of a single weight. Then, the TOPSIS method is used to calculate the quality score of the aggregated to-be-evaluated factor, and a weighted initial power grid data quality score set is obtained through the initial power grid data quality score set and the first comprehensive weight vector: wherein, is the quality weighted score of the kth evaluated factor in the weighted initial power grid data quality score set, is the comprehensive weight of the kth evaluated factor in the first comprehensive weight vector, is the quality original score of the kth evaluated factor in the initial power grid data quality score set. Thus, the index original score is converted into a weighted score reflecting the difference in weights, laying the foundation for subsequent determination of ideal solutions and calculation of the body phase power grid data quality score set. Then, by weighting the initial power grid data quality score set, as well as the weighted scores of the evaluated factors based on historical scores and the weighted scores of the evaluated factors based on experience, the positive ideal solution set and the negative ideal solution set are determined. ; ; wherein, is the maximum weighted score of the evaluated factor based on historical scores, is the maximum weighted score of the evaluated factor based on experience; is the minimum weighted score of the evaluated factor based on historical scores, is the minimum weighted score of the evaluated factor based on experience. The benchmark of quality score is established, providing a reference standard for measuring the quality level of the evaluated data. Subsequently, the first Euclidean distance is calculated by the weighted initial power grid data quality score set and the positive ideal solution set, the second Euclidean distance is calculated by the weighted initial power grid data quality score set and the negative ideal solution set, and the formula is: Then, the closeness D is calculated by the first Euclidean distance and the second Euclidean distance , and the formula is: According to the body phase power grid data quality score, the body phase power grid data quality score set is obtained (which is actually composed of different body phase power grid data quality scores under the combination of three body perspectives and six life cycle phases), realizing the conversion from index weighted score to "body-phase" level quality score, and providing core data for subsequent splitting of body perspective and life cycle phase scores.

[0042] Further, based on the subject phase power grid data quality score set, a subject perspective power grid data quality score set and a life cycle phase power grid data quality score set are obtained; comprising: The subjective weight vector obtaining operation and the objective weight vector obtaining operation are respectively performed on the subject phase power grid data quality score set to obtain a second subjective weight vector and a second objective weight vector; Based on the subject phase power grid data quality score set, the second subjective weight vector and the second objective weight vector, a subject perspective power grid data quality score set is obtained; The subjective weight vector obtaining operation and the objective weight vector obtaining operation are respectively performed on the subject phase power grid data quality score set to obtain a third subjective weight vector and a third objective weight vector; Based on the subject phase power grid data quality score set, the third subjective weight vector and the third objective weight vector, a life cycle phase power grid data quality score set is obtained.

[0043] In the embodiment, the subject phase power grid data quality score set is obtained by aggregating the data quality scores of each subject in the data quality subject dimension S, and the data quality scores of each life cycle phase in the data quality life cycle dimension L. The subjective weight vector obtaining operation and the objective weight vector obtaining operation in this step and the process of obtaining the subject perspective power grid data quality score set according to the subject phase power grid data quality score set, the second subjective weight vector and the second objective weight vector are consistent with the above-mentioned method. For the subject , according to the different attention degrees of the subjects to each phase of the data life cycle, the second subjective weight vector and the second objective weight vector are obtained by respectively performing the subjective weight vector obtaining operation and the objective weight vector obtaining operation on the subject phase power grid data quality score set, which provides the subjective and objective basis for subsequent calculation of the comprehensive weight under the subject perspective, and ensures that the weight fits the actual needs of the subject to the phase. Then, the second comprehensive weight vector is obtained by the subject phase power grid data quality score set, the second subjective weight vector and the second objective weight vector , and the subject perspective data quality score is obtained by combining the subject phase power grid data quality score set , so as to obtain the subject perspective power grid data quality score set, realize the conversion from the “subject-phase” two-dimensional score to the “single subject perspective” score, and meet the demand of analyzing the data quality from the subject dimension. Then, According to each life cycle phase in the data quality process dimension L, the data quality scores are aggregated. The subjective weight vector obtaining operation and the objective weight vector obtaining operation in this step and the process of obtaining the life cycle phase power grid data quality score set according to the subject phase power grid data quality score set, the third subjective weight vector and the third objective weight vector are consistent with the above-mentioned method. For the life cycle phase , according to the different participation degrees of each use subject of the data, the subjective weight vector acquisition operation and the objective weight vector acquisition operation are performed on the subject stage power grid data quality score set respectively, the third subjective weight vector and the third objective weight vector are acquired, the subjective and objective bases are provided for subsequent calculation of the comprehensive weight in the life cycle stage perspective, and the weight is ensured to be fitted to the actual needs of the subject in the life cycle stage. Finally, the third comprehensive weight vector is acquired through the subject stage power grid data quality score set, the third subjective weight vector and the third objective weight vector , and the subject stage power grid data quality score set is combined to acquire the data quality score of the life cycle stage , so that the subject stage power grid data quality score set is obtained, the conversion from the'subject-stage' two-dimensional score to the'single life cycle stage' score is realized, and the demand for analyzing the data quality from the life cycle dimension is met.

[0044] Further, the subject perspective power grid comprehensive data quality score and the life cycle stage power grid comprehensive data quality score are acquired based on the subject perspective power grid data quality score set and the life cycle stage power grid data quality score set; including: The subjective weight vector acquisition operation and the objective weight vector acquisition operation are performed on the subject perspective power grid data quality score set respectively, the fourth subjective weight vector and the fourth objective weight vector are acquired; The subject perspective power grid comprehensive data quality score is acquired based on the subject perspective power grid data quality score set, the fourth subjective weight vector and the fourth objective weight vector; The subjective weight vector acquisition operation and the objective weight vector acquisition operation are performed on the life cycle stage power grid data quality score set respectively, the fifth subjective weight vector and the fifth objective weight vector are acquired; The life cycle stage power grid comprehensive data quality score is acquired based on the life cycle stage power grid data quality score set, the fifth subjective weight vector and the fifth objective weight vector.

[0045] In the embodiment, the subjective weight vector acquisition operation and the objective weight vector acquisition operation in the step and the process of acquiring the subject perspective power grid comprehensive data quality score based on the subject perspective power grid data quality score set, the fourth subjective weight vector and the fourth objective weight vector are consistent with the method adopted above. According to the different importance degrees of each use subject of the power grid basic data, the fourth subjective weight vector and the fourth objective weight vector are acquired, scientific subjective and objective weight bases are provided for subsequent calculation of the subject perspective comprehensive score, and the weight is configured for the subject . Then, the fourth comprehensive weight vector is acquired through the fourth subjective weight vector and the fourth objective weight vector and combine the main perspective power grid data quality score set to obtain the main perspective power grid comprehensive data quality score , realize the integration from "single main perspective score" to "overall main perspective comprehensive score", and can intuitively reflect the overall level of power grid data quality under multiple main perspectives. The subjective weight vector acquisition operation and the objective weight vector acquisition operation in this step, and the process of obtaining the life cycle stage power grid comprehensive data quality score according to the life cycle stage power grid data quality score set, the fifth subjective weight vector and the fifth objective weight vector are consistent with the above-mentioned method. Then, for the stage Configure the weight, obtain the fifth subjective weight vector and the fifth objective weight vector by respectively performing the subjective weight vector acquisition operation and the objective weight vector acquisition operation on the life cycle stage power grid data quality score set, and provide scientific subjective and objective weight basis for subsequent calculation of the life cycle stage comprehensive score. Finally, the fifth comprehensive weight vector is obtained by combining the life cycle stage power grid data quality score set to obtain the life cycle stage power grid comprehensive data quality score , realize the integration from "single life cycle stage score" to "overall life cycle stage comprehensive score", and can intuitively reflect the overall level of power grid data quality in the whole life cycle process.

[0046] The embodiment provides a power grid basic data whole life cycle quality maturity measurement system, please see Figure 3 , comprising a data acquisition module, an initial calculation module, a main stage score extraction module, a multi-dimensional data splitting module, a double-dimensional comprehensive calculation module, a comprehensive quality score calculation module, a maturity level preliminary determination module and a maturity level final determination module, specifically: The data acquisition module is used to obtain the power grid basic data to be evaluated; The initial calculation module is used to calculate the initial power grid data quality score set based on the preset index calculation rule. The main stage score extraction module is used to obtain the main stage power grid data quality score set based on the initial power grid data quality score set. The multi-dimensional data splitting module is used to obtain the main perspective power grid data quality score set and the life cycle stage power grid data quality score set based on the main stage power grid data quality score set. The double-dimensional comprehensive calculation module is used to obtain the main perspective power grid comprehensive data quality score and the life cycle stage power grid comprehensive data quality score based on the main perspective power grid data quality score set and the life cycle stage power grid data quality score set. The comprehensive quality score calculation module is used to obtain the comprehensive quality score of power grid basic data based on preset subject perspective weights, preset life cycle stage weights, subject perspective power grid comprehensive data quality score and life cycle stage power grid comprehensive data quality score. The preliminary maturity level determination module is used to obtain the main body maturity level, stage maturity level and comprehensive preliminary level based on the main body's perspective power grid data quality score set, the life cycle stage power grid data quality score set, the comprehensive quality score of power grid basic data, the preset power grid data maturity level and the preset threshold sequence. The maturity level final determination module is used to determine the comprehensive quality maturity level corresponding to the power grid basic data to be evaluated based on the main maturity level, stage maturity level, comprehensive preliminary level and preset maturity level admission rules, so as to realize the measurement of the quality maturity of the power grid basic data throughout its entire life cycle.

[0047] This embodiment provides a power grid infrastructure data lifecycle quality maturity measurement system. In practical applications, it only requires an initial calculation module to obtain the indicators of the power grid infrastructure data to be evaluated in indicator dimension I. ( Initial power grid data quality score at p=8) This process yields an initial set of power grid data quality scores. A multi-dimensional data splitting module aggregates the power grid data quality scores for the main phase according to the main data quality dimension, resulting in a main-perspective set. It also aggregates the main-perspective scores for the lifecycle phase according to the lifecycle phase dimension, resulting in a lifecycle phase set. This decomposition from "two-dimensional scores" to "single-dimensional scores" meets the need for analyzing power grid data quality from different perspectives. Then, a dual-dimensional comprehensive calculation module calculates the comprehensive power grid data quality score for the main perspective and the comprehensive power grid data quality score for the lifecycle phase. This integrates the dispersed single-subject-perspective scores and single-lifecycle-phase scores, providing key inputs for subsequent calculations of the comprehensive quality score of basic power grid data. Finally, a comprehensive quality score calculation module is used, with preset subject-perspective weights... Preset lifecycle stage weights ( ), and the overall data quality score of the power grid from the perspective of the main body. and the overall data quality score of the power grid at each stage of its life cycle Perform weighted calculation: The overall quality score of the power grid basic data is obtained. This enables a comprehensive quantitative assessment of the quality of basic power grid data, providing a direct reflection of its overall quality level. Furthermore, a maturity level preliminary assessment module is employed, using a dataset of power grid data quality scores from a subject-specific perspective. Lifecycle stage power grid data quality score set Combine the comprehensive quality score of the power grid basic data with the preset power grid data maturity level and preset threshold sequence. The maturity levels of the main entities are defined respectively. , Stage Maturity Level , and comprehensive preliminary level , Among them, the threshold score satisfies , It is the minimum value that the quality score can reach. This is the maximum value that the quality score can reach. It can provide a preliminary basis for determining the comprehensive quality maturity level corresponding to the power grid basic data to be evaluated, and achieve a preliminary classification of the power grid basic data quality level. Q maturity levels were defined. Finally, the maturity level final determination module was used, based on the main maturity level... Stage Maturity Level Comprehensive preliminary level Combined with the preset maturity level admission rules (specifically: if the overall quality maturity level is...) for However, if the admission rule function for the level is not met, the overall quality maturity level will be downgraded to [level]. Or lower, until the admission rule function of the level is met (admission rule function of the level): ; in The level does not exceed Meeting the admission rules The effective level in. It is a level The admission rule function, the input is and The output is Or False (when the actual and Satisfying the rules and The level range is (If the condition is false, the maturity level is downgraded; otherwise, the condition is false). This process filters out maturity levels that meet the rules. As the corresponding comprehensive quality maturity level of the grid basic data to be evaluated, it ensures to meet the actual requirements of the grid business and avoids dividing the grid basic data quality maturity level by threshold only, highlighting the important role of certain subjects or life cycle stages in the grid basic data quality evaluation.

[0048] Further, the subject stage score extraction module is configured to obtain a subject stage grid data quality score set based on the initial grid data quality score set; and the subject stage score extraction module comprises: The initial grid data quality score set is subjected to a subjective weight vector obtaining operation and an objective weight vector obtaining operation respectively to obtain a first subjective weight vector and a first objective weight vector; The subject stage grid data quality score set is obtained based on the initial grid data quality score set, the first subjective weight vector and the first objective weight vector.

[0049] In this embodiment, the initial grid data quality score set is aggregated based on the comprehensive data quality subject dimension S and the data quality process dimension L. For the subject , the initial grid data quality score set is subjected to a subjective weight vector obtaining operation according to the different degrees of attention to the indicators of the data life cycle stage , and the weight is configured for the indicators : By obtaining the first subjective weight vector and the first objective weight vector, the subjective and objective basis can be provided for subsequent comprehensive weight calculation to avoid one-sidedness of a single weight. Then, the first comprehensive weight vector is obtained based on the first subjective weight vector and the first objective weight vector, and the initial grid data quality score set is combined to obtain the subject stage grid data quality score set , realizing the conversion of the data quality indicator score to the “subject-life cycle stage” two-dimensional score and laying a foundation for subsequent splitting of the subject perspective and the life cycle stage score.

[0050] Further, the initial grid data quality score set is subjected to a subjective weight vector obtaining operation and an objective weight vector obtaining operation respectively to obtain a first subjective weight vector and a first objective weight vector; and the subjective weight vector obtaining operation comprises: The first factor and the second factor are obtained based on the initial grid data quality score set; The first fuzzy judgment vector is obtained based on the first factor and other factors; The second fuzzy judgment vector is obtained based on the other factors and the second factor; The nonlinear constraint optimization model is established based on the first fuzzy judgment vector and the second fuzzy judgment vector; Based on preset constraints, the nonlinear constraint optimization model is solved with the goal of minimizing the judgment error, and the fuzzy weight vector is obtained. The first subjective weight vector is obtained based on the fuzzy weight vector.

[0051] In this embodiment, the fuzzy optimal and worst method is used to determine the subjective weight vector: through the initial set of power grid data quality scores, in a factor set with p factors to be weighted (the factors to be weighted are the 8 core indicators of the data quality index dimension). Select the most important factors As the first factor and the least important factor As a second factor, it provides a reference benchmark for subsequent pairwise comparisons, avoiding a lack of primary and secondary guidance in weight allocation. Then, using triangular fuzzy numbers, the selected factors that have passed the first factor are... Other factor The importance of each molecule is compared pairwise to obtain the first fuzzy judgment vector, which is then converted into a quantifiable fuzzy value to provide a subjective judgment basis for subsequent weight calculations. It is the primary factor. Other factors Compared to the corresponding triangular fuzzy number; then, other factors... With the selected second factor The importance of each pair of elements is compared to obtain the second fuzzy judgment vector. This will further refine the data dimensions for subjective judgment. Other factors With the second factor Compared to the corresponding triangular fuzzy number; the triangular fuzzy number can be determined through Table 1: Table 1. Correspondence Rules between Importance and Triangular Fuzzy Number Subsequently, using the first and second fuzzy judgment vectors, a nonlinear constraint optimization model is constructed to transform subjective judgment into a mathematical optimization problem, providing model support for the scientific solution of weights. Specifically: in, , which represents the error in judgment, is used to measure the degree of consistency between subjective judgments. It is the fuzzy weight of the first factor. This refers to the fuzzy weights of the second factor. By solving a nonlinear constraint optimization model with the goal of minimizing the judgment error under preset constraints, the fuzzy weight vector is obtained. ,in This represents the optimal fuzzy weight for factor k, achieving the transformation of subjective judgment into vectorized weights. Finally, the fuzzy weight vector is transformed into the first subjective weight vector: first, the fuzzy weights... Convert to real value , Then, the gradient average synthesis representation is used to... Deblurring yields the final subjective weight vector. This provides standardized subjective weight data for subsequent integration with objective weights.

[0052] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for measuring the quality maturity of power grid basic data throughout its entire lifecycle, characterized in that, include: Obtain basic data of the power grid to be evaluated; Based on the preset index calculation rules, the basic data of the power grid to be evaluated are calculated to obtain an initial set of power grid data quality scores. Based on the initial set of power grid data quality scores, obtain the set of power grid data quality scores for the main phase. Based on the power grid data quality score set of the main phase, obtain the power grid data quality score set from the main perspective and the power grid data quality score set of the life cycle phase; Based on the power grid data quality score set from the subject perspective and the power grid data quality score set from the life cycle stage, obtain the comprehensive power grid data quality score from the subject perspective and the comprehensive power grid data quality score from the life cycle stage. Based on preset subject perspective weights, preset life cycle stage weights, subject perspective power grid comprehensive data quality scores, and life cycle stage power grid comprehensive data quality scores, the comprehensive quality score of power grid basic data is obtained. Based on the power grid data quality score set from the subject's perspective, the power grid data quality score set for each life cycle stage, the comprehensive quality score of basic power grid data, the preset power grid data maturity level, and the preset threshold sequence, the subject maturity level, the stage maturity level, and the comprehensive preliminary level are obtained. Based on the main body maturity level, stage maturity level, comprehensive preliminary level and preset maturity level admission rules, the comprehensive quality maturity level corresponding to the power grid basic data to be evaluated is determined, so as to realize the measurement of the quality maturity of the power grid basic data throughout the entire life cycle.

2. The method for measuring the quality maturity of power grid basic data throughout its entire lifecycle, as described in claim 1, is characterized in that... The process of obtaining the main phase power grid data quality score set based on the initial power grid data quality score set includes: Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the initial power grid data quality score set respectively to obtain the first subjective weight vector and the first objective weight vector; Based on the initial set of power grid data quality scores, the first subjective weight vector, and the first objective weight vector, the set of power grid data quality scores for the main phase is obtained.

3. The method for measuring the quality maturity of power grid basic data throughout its entire lifecycle, as described in claim 2, is characterized in that... Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the initial power grid data quality score set respectively to obtain the first subjective weight vector and the first objective weight vector; The subjective weight vector acquisition operation includes: Based on the initial set of power grid data quality scores, the first and second factors are obtained; Based on the first factor and other factors, obtain the first fuzzy judgment vector; Based on other factors and the second factor, obtain the second fuzzy judgment vector; A nonlinear constraint optimization model is established based on the first fuzzy judgment vector and the second fuzzy judgment vector. Based on preset constraints, the nonlinear constraint optimization model is solved with the goal of minimizing the judgment error, and the fuzzy weight vector is obtained. The first subjective weight vector is obtained based on the fuzzy weight vector.

4. The method for measuring the quality maturity of power grid basic data throughout its entire lifecycle, as described in claim 2, is characterized in that... Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the initial power grid data quality score set respectively to obtain the first subjective weight vector and the first objective weight vector; The objective weight vector acquisition operation includes: Obtain the set of historical power grid data quality scores for the main historical period; Information entropy is calculated by standardizing the set of power grid data quality scores from the main historical periods. Based on information entropy, obtain the first objective weight vector.

5. A method for measuring the quality maturity of power grid basic data throughout its entire lifecycle, as described in claim 2, is characterized in that... The process of obtaining the power grid data quality score set for the main phase based on the initial power grid data quality score set, the first subjective weight vector, and the first objective weight vector includes: Based on the first subjective weight vector and the first objective weight vector, obtain the first comprehensive weight vector; Based on the initial power grid data quality score set and the comprehensive weight vector, obtain the weighted initial power grid data quality score set; Based on the weighted initial power grid data quality score set, the positive ideal solution set and the negative ideal solution set are determined; Based on the weighted initial power grid data quality score set, the positive ideal solution set, and the negative ideal solution set, the power grid data quality score set for the main stage is obtained.

6. The method for measuring the quality maturity of power grid basic data throughout its entire lifecycle, as described in claim 1, is characterized in that... The process involves obtaining a power grid data quality score set from the subject's perspective and a power grid data quality score set from the lifecycle stage, based on the power grid data quality score set for the subject's stage; including: Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the power grid data quality score set of the main phase respectively to obtain the second subjective weight vector and the second objective weight vector; Based on the power grid data quality score set of the main phase, the second subjective weight vector, and the second objective weight vector, obtain the power grid data quality score set from the subject's perspective. Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the power grid data quality score set of the main phase respectively to obtain the third subjective weight vector and the third objective weight vector; Based on the power grid data quality score set for the main phase, the third subjective weight vector, and the third objective weight vector, the power grid data quality score set for the life cycle phase is obtained.

7. The method for measuring the quality maturity of power grid basic data throughout its entire lifecycle, as described in claim 1, is characterized in that... The method involves obtaining a comprehensive power grid data quality score from the perspective of the main user and a comprehensive power grid data quality score from the lifecycle stage, based on the power grid data quality score set from the main user's perspective and the power grid data quality score set from the lifecycle stage; including: Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the power grid data quality score set from the subject's perspective respectively to obtain the fourth subjective weight vector and the fourth objective weight vector; Based on the power grid data quality score set from the subject's perspective, the fourth subjective weight vector, and the fourth objective weight vector, the comprehensive power grid data quality score from the subject's perspective is obtained. Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the power grid data quality score set of each life cycle stage to obtain the fifth subjective weight vector and the fifth objective weight vector; Based on the power grid data quality score set for each life cycle stage, the fifth subjective weight vector, and the fifth objective weight vector, the comprehensive power grid data quality score for each life cycle stage is obtained.

8. A system for measuring the quality maturity of power grid basic data throughout its entire lifecycle, characterized in that, It includes a data acquisition module, an initial calculation module, a main stage score extraction module, a multi-dimensional data splitting module, a two-dimensional comprehensive calculation module, a comprehensive quality score calculation module, a preliminary maturity level determination module, and a final maturity level determination module, specifically: The data acquisition module is used to acquire basic data of the power grid to be evaluated; The initial calculation module is used to calculate the basic data of the power grid to be evaluated based on the preset index calculation rules, and obtain an initial set of power grid data quality scores. The main phase score extraction module is used to obtain the main phase power grid data quality score set based on the initial power grid data quality score set. The multi-dimensional data splitting module is used to obtain a set of power grid data quality scores from the subject perspective and a set of power grid data quality scores from the life cycle stage, based on the power grid data quality score set of the subject stage. The dual-dimensional comprehensive calculation module is used to obtain the comprehensive power grid data quality score from the perspective of the subject and the comprehensive power grid data quality score from the life cycle stage based on the power grid data quality score set from the subject's perspective and the power grid data quality score set from the life cycle stage. The comprehensive quality score calculation module is used to obtain the comprehensive quality score of power grid basic data based on preset subject perspective weights, preset life cycle stage weights, subject perspective power grid comprehensive data quality score and life cycle stage power grid comprehensive data quality score. The maturity level preliminary determination module is used to obtain the main body maturity level, stage maturity level and comprehensive preliminary level based on the main body's perspective power grid data quality score set, the life cycle stage power grid data quality score set, the power grid basic data comprehensive quality score, the preset power grid data maturity level and the preset threshold sequence. The maturity level final determination module is used to determine the comprehensive quality maturity level corresponding to the power grid basic data to be evaluated based on the main maturity level, stage maturity level, comprehensive preliminary level and preset maturity level admission rules, so as to realize the measurement of the quality maturity of the power grid basic data throughout its entire life cycle.

9. A power grid basic data lifecycle quality maturity measurement system according to claim 8, characterized in that, The main phase score extraction module is used to obtain the main phase power grid data quality score set based on the initial power grid data quality score set; including: Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the initial power grid data quality score set respectively to obtain the first subjective weight vector and the first objective weight vector; Based on the initial set of power grid data quality scores, the first subjective weight vector, and the first objective weight vector, the set of power grid data quality scores for the main phase is obtained.

10. A power grid basic data lifecycle quality maturity measurement system according to claim 9, characterized in that, Perform subjective weight vector acquisition operation and objective weight vector acquisition operation on the initial power grid data quality score set respectively to obtain the first subjective weight vector and the first objective weight vector; The subjective weight vector acquisition operation includes: Based on the initial set of power grid data quality scores, the first and second factors are obtained; Based on the first factor and other factors, obtain the first fuzzy judgment vector; Based on other factors and the second factor, obtain the second fuzzy judgment vector; A nonlinear constraint optimization model is established based on the first fuzzy judgment vector and the second fuzzy judgment vector. Based on preset constraints, the nonlinear constraint optimization model is solved with the goal of minimizing the judgment error, and the fuzzy weight vector is obtained. The first subjective weight vector is obtained based on the fuzzy weight vector.

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