Method for evaluating maturity of power grid enterprise data asset management system

Abstract

The invention discloses a power grid enterprise data asset management system maturity evaluation method, and relates to the technical field of data asset management, and the method comprises the steps: constructing a DCMM-based data asset management evaluation index system, determining an evaluation object, setting an evaluation index, formulating an expert scoring standard, and standardizing a positive index and a negative index; constructing a total deviation model of values and correlation degrees of evaluation indexes corresponding to the evaluation object and all other evaluation objects; constructing a target function; the optimal solution of the target function is solved; performing normalization processing on the optimal solution to obtain the weight of the evaluation index; establishing a grey relational degree model, and taking the weight of the evaluation index as the weight of calculation weighting; calculating a comprehensive score; and analyzing a data asset management evaluation index system through the comprehensive score, determining a system problem, and performing targeted optimization. According to the invention, comprehensive score calculation, weak point analysis and improvement, evaluation and management capability improvement and decision support enhancement can be realized.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of data asset management, and specifically to a maturity evaluation method for a data asset management system of a power grid enterprise. Background Art

[0002] With the rapid development of the digital economy, various industries have gradually regarded data as an inevitable element of enterprise development. From big data to artificial intelligence, from production-end data to sales-end data, from personal data to public data, while shaping new momentum for the digital economy, they are also putting forward new requirements for data asset management.

[0003] Although power grid companies are different from data-based Internet companies, they have also accumulated a huge amount of data resources in the development of traditional power livelihood businesses. In order to activate the accumulated data and release the power of development momentum, power grid companies need to improve the data asset management capabilities of the traditional power industry. To achieve this goal, it is necessary not only to enter data assets into tables, value assessment, pricing and other related work, but also to evaluate and optimize the maturity of the corresponding data asset management system.

[0004] In the process of data asset management, how to evaluate the maturity of the existing data asset management system of power grid enterprises and make targeted improvements is one of the difficulties today; how to objectively and impartially quantify and comprehensively score the subjective and objective evaluation indicators in the evaluation index system based on a variety of data asset management systems and goals for different data asset management situations is a link that is lacking in existing technologies. In addition, the use of comprehensive calculations to analyze and improve weaknesses is also a way to improve evaluation and management capabilities. After calculating the comprehensive score, it is also necessary to clarify the meaning of the score and think about the next step of optimization work based on the evaluation.

[0005] To sum up, being able to unify quantitative standards, calculate comprehensive scores, analyze and improve weaknesses, enhance evaluation and management capabilities, and strengthen decision-making support data asset management evaluation methods are issues that urgently need to be resolved by personnel in this technical field. Summary of the invention

[0006] Based on this, the purpose of the present invention is to provide a method for evaluating the maturity of a data asset management system of a power grid enterprise to solve the technical problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The maturity evaluation method of the data asset management system of power grid enterprises includes the following steps:

[0009] Step 1: Build a data asset management evaluation index system based on DCMM, determine the evaluation object, set the evaluation index, formulate expert scoring standards, and conduct expert scoring on the subjective indicators in the evaluation index;

[0010] Step 2: Standardize the positive indicators in the data asset management evaluation index system; Standardize the negative indicators in the data asset management evaluation system;

[0011] Step 3: construct a total deviation model of the values ​​of the evaluation indicators corresponding to the evaluation object and all other evaluation objects; construct a total deviation model of the correlation between the evaluation object and other evaluation objects;

[0012] Step 4: construct the objective function through the total deviation model of the evaluation index value and the total deviation model of the correlation degree; and find the optimal solution of the objective function;

[0013] Step 5: Normalize the optimal solution to obtain the weight of the evaluation index;

[0014] Step 6: Establish a grey correlation model, take the weight of the evaluation index as the weight for calculating weighted, determine the reference sequence by taking the maximum value of the positive index and the minimum value of the negative index, and calculate the correlation coefficient of each evaluation index;

[0015] Step 7: Calculate the comprehensive score;

[0016] Step 8. Analyze the data asset management evaluation index system through comprehensive scoring, identify system problems, and use pre-acquired optimization methods to optimize system problems in a targeted manner.

[0017] Preferably, the evaluation indicators include primary indicators, secondary indicators of eight capability domains, and tertiary indicators;

[0018] The three-level indicators are divided into subjective indicators and objective indicators;

[0019] The third-level indicator attributes include positive indicators and negative indicators.

[0020] Preferably, the first-level indicator is identified as the maturity of the data asset management system (A);

[0021] The secondary indicators of the eight capability domains specifically include: data strategy (A1), data governance (A2), data architecture (A3), data application (A4), data security (A5), data quality (A6), data standards (A7), and data life cycle (A8).

[0022] Preferably, the three-level indicators specifically include:

[0023] Data strategy (A1) consists of data strategy planning (A11), data strategy implementation (A12), and data strategy evaluation (A13);

[0024] Data governance (A2) consists of data governance organization (A21), data system construction (A22), and data governance communication (A23);

[0025] Data architecture (A3) consists of data model (A31), data distribution (A32), data integration and sharing (A33), and metadata management (A34);

[0026] Data application (A4) consists of data analysis (A41), data open sharing (A42), and data services (A43);

[0027] Data security (A5) consists of data security strategy (A51), data security management (A52), and data security audit (A53);

[0028] Data quality (A6) consists of data quality requirements (A61), data quality checks (A62), and data quality analysis (A63);

[0029] Data standards (A7) include business terms (A71), reference data and master data (A72), data elements (A73), and indicator data (A74);

[0030] The data life cycle (A8) consists of data requirements (A81), data design and development (A82), data operation and maintenance (A83), and data retirement (A84).

[0031] Preferably, the subjective indicators are: data strategy planning (A11), data strategy implementation (A12), data governance organization (A21), data system construction (A22), data security strategy (A51), data security management (A52), data security audit (A53), data quality requirements (A61), data quality analysis (A63), business terms (A71), reference data and master data (A72), data elements (A73), indicator data (A74), and data requirements (A81).

[0032] Preferably, the normalized representation of the positive indicator is:

[0033]

[0034] The normalized representation of the negative indicator is:

[0035]

[0036] Among them, i: represents the evaluation index, including positive and negative indicators;

[0037] j: indicates the evaluation object;

[0038] P ij : represents the standardized value of the i-th evaluation index of the j-th evaluation object;

[0039] V ij : represents the value of the i-th evaluation index of the j-th evaluation object;

[0040] n: represents the number of evaluation objects;

[0041] min(V ij ): represents the minimum value of the i-th evaluation index of the j-th evaluation object;

[0042] max(V ij ): represents the maximum value of the i-th evaluation index of the j-th evaluation object.

[0043] Preferably, the total deviation model of the values ​​of the evaluation indicators corresponding to the evaluation object and all other evaluation objects is:

[0044]

[0045] The total deviation model of the correlation between the evaluation object and other evaluation objects is:

[0046]

[0047] Among them, m: represents the number of evaluation indicators;

[0048] ω i : represents the weight of the i-th evaluation index, and ω i ≥0;

[0049] k: represents all other evaluation objects;

[0050] F ij (ω): It is the sum of the values ​​of the evaluation index corresponding to the evaluation object j and all other evaluation objects.

[0051] Deviation;

[0052] F i (ω): It is the total deviation of the correlation between evaluation object j and other evaluation objects for evaluation index i.

[0053] Preferably, the objective function is:

[0054]

[0055] The optimal solution of the objective function is:

[0056]

[0057] The weight calculation model after normalization of the optimal solution of the objective function is:

[0058]

[0059] Among them, maxF(ω): represents the objective function;

[0060] ω i * : represents the optimal solution of the objective function.

[0061] Preferably, the grey relational model is:

[0062]

[0063] The comprehensive score calculation formula is:

[0064]

[0065] Among them, r ij : It is expressed as the correlation coefficient between the i-th evaluation index of the j-th evaluation object and the reference sequence;

[0066] P ia is the reference value of the i-th evaluation index;

[0067] m: represents the number of evaluation indicators;

[0068] min min(|P ia -P ij |) is the minimum difference of the sequence;

[0069] max max(|P ia -P ij |) is the maximum difference of the sequence;

[0070] ρ: represents the resolution coefficient, with a value range of [0,1],

[0071] C j : Expressed as a comprehensive score;

[0072] ω i : Represented as weight.

[0073] Preferably, the optimization means include data quality, data security, data value, institutional optimization at the data supervision level, personnel allocation optimization, and management efficiency optimization.

[0074] In summary, the present invention mainly has the following beneficial effects:

[0075] In the present invention, through this method, both subjective and objective indicators can be quantified objectively and fairly, solving the problem of inconsistent indicator quantification in the current evaluation system. The unified quantification standard helps to compare data asset management work between different departments and different levels horizontally and vertically, and improves the accuracy and comparability of the evaluation.

[0076] The comprehensive score can fully reflect the overall maturity level of the data asset management system of power grid enterprises, avoiding the one-sidedness of single evaluation indicators. Through comprehensive calculation, the weak links and potential risks in data asset management can be more accurately identified, providing strong support for subsequent improvement work.

[0077] Weakness analysis based on comprehensive scores can clearly point out specific problems in data asset management, such as low data quality, high data security risks, insufficient data utilization, etc. Targeted improvement measures can be formulated for the analyzed weaknesses to improve the efficiency and effectiveness of data asset management.

[0078] The application of this method can promote the standardization, standardization and refinement of data asset management in power grid enterprises and improve the overall management level. Through regular maturity evaluation, enterprises can continuously review and optimize the data asset management system, forming a virtuous cycle of continuous improvement.

[0079] The maturity evaluation results can provide decision support for enterprise management, helping them to more accurately understand the current status and future development direction of data asset management. Decisions based on the evaluation results can be more scientific and reasonable, helping enterprises to maximize the use and value creation of data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a flow chart for realizing the method for evaluating the maturity of the data asset management system of a power grid enterprise of the present invention;

[0081] Figure 2 Schematic diagram of the DCMM-based index system of the present invention;

[0082] Figure 3 The present invention is a flow chart for calculating the comprehensive evaluation score. DETAILED DESCRIPTION

[0083] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0084] like Figure 1 As shown in the figure, the maturity evaluation method of the data asset management system of the power grid enterprise includes the following steps:

[0085] Step 1: Build a data asset management evaluation index system based on DCMM, determine the evaluation object, set the evaluation index, formulate expert scoring standards, and conduct expert scoring on the subjective indicators in the evaluation index;

[0086] Step 2: Standardize the positive indicators in the data asset management evaluation index system; Standardize the negative indicators in the data asset management evaluation system;

[0087] Step 3: construct a total deviation model of the values ​​of the evaluation indicators corresponding to the evaluation object and all other evaluation objects; construct a total deviation model of the correlation between the evaluation object and other evaluation objects;

[0088] Step 4: construct the objective function through the total deviation model of the evaluation index value and the total deviation model of the correlation degree; and find the optimal solution of the objective function;

[0089] Step 5: Normalize the optimal solution and finally obtain the weight of the evaluation index;

[0090] Step 6: Establish a grey correlation model, use the weight of the final evaluation index as the weight for calculating weighted, determine the reference sequence by taking the maximum value of the positive index and the minimum value of the negative index, and calculate the correlation coefficient of each evaluation index;

[0091] Step 7: Calculate the comprehensive score;

[0092] Step 8. Analyze the data asset management evaluation index system through comprehensive scoring, identify system problems, and use pre-acquired optimization methods to optimize system problems in a targeted manner.

[0093] Construct a data asset management evaluation index system based on DCMM, determine the evaluation object, set the evaluation index, formulate expert scoring standards, and conduct expert scoring on the subjective indicators in the evaluation index;

[0094] Among them, the evaluation index system based on DCMM should be constructed in combination with the characteristics of data assets and management of the power grid enterprises. In the setting of the three-level indicators, some indicators are subjective, such as data strategy planning A11, data strategy implementation A12, data governance organization A21, data system construction A22, data security strategy A51, data security management A52, data security audit A53, data quality requirements A61, data quality analysis A63, business terms A71, reference data and master data A72, data element A73, etc. The processing of these subjective indicators should be quantified by means such as fuzzy matrix and expert scoring method. Here, the expert scoring method is used for scoring. If the measurement involves "yes", "yes" is set as 1 point, and "no" is set as 0 point; if it does not involve "yes", it is set in the range of 1-5 points according to the actual situation. If other objective indicators have no relevant data due to the non-existence of relevant systems or related work, refer to the scoring standard involving "yes" in the measurement, and unify it as 0 points. However, if there is a better measurement standard, it can be replaced by itself. After summarizing the subjective indicator scores and objective indicator data, these data need to be classified.

[0095] like Figure 2 As shown in the figure, the indicator system based on DCMM is as follows:

[0096] The first-level indicator is identified as data asset management system maturity A;

[0097] The secondary indicators of the eight capability domains specifically include: data strategy A1, data governance A2, data architecture A3, data application A4, data security A5, data quality A6, data standards A7, and data life cycle A8;

[0098] The three-level indicators include:

[0099] Data strategy A1 consists of data strategy planning A11, data strategy implementation A12, and data strategy evaluation A13;

[0100] Data governance A2 consists of data governance organization A21, data system construction A22, and data governance communication A23;

[0101] Data architecture A3 consists of data model A31, data distribution A32, data integration and sharing A33, and metadata management A34;

[0102] Data application A4 consists of data analysis A41, data openness and sharing A42, and data services A43;

[0103] Data security A5 consists of data security strategy A51, data security management A52, and data security audit A53;

[0104] Data quality A6 consists of data quality requirements A61, data quality checks A62, and data quality analysis A63;

[0105] Data standard A7 includes business terms A71, reference data and master data A72, data elements A73, and indicator data A74;

[0106] The data life cycle A8 consists of data requirements A81, data design and development A82, data operation and maintenance A83, and data retirement A84;

[0107] Among them, the subjective indicators are: data strategy planning A11, data strategy implementation A12, data governance organization A21, data system construction A22, data security strategy A51, data security management A52, data security audit A53, data quality requirements A61, data quality analysis A63, business terms A71, reference data and master data A72, data element A73, indicator data A74, data requirements A81.

[0108] It should be noted that if other objective indicators have no relevant data or incomplete relevant data, they will be converted into subjective indicators.

[0109] A grey correlation model is established, and the weight of the evaluation index is used as the weight for calculating weighted values. The reference sequence is determined by taking the maximum value of the positive index and the minimum value of the negative index, and the correlation coefficient of each evaluation index is calculated; the comprehensive score is calculated.

[0110] First, the optimal solution of the objective function can be determined based on the principle of maximizing the deviation to determine the score weight.

[0111] Then, based on the grey relational model, the data of each indicator are compared with the reference sequence to determine the grey relational coefficient.

[0112] Finally, the comprehensive score is calculated based on the weight of the maximized deviation and the correlation coefficient of the grey correlation degree.

[0113] In this embodiment, the evaluation object should be determined first. When calculating the score weight based on the maximum deviation, the summarized positive and negative indicators should be standardized first. Then construct the total deviation model, and then construct the objective function. After the optimal solution processing and normalization processing, the score weight calculation model is obtained to calculate the required management system maturity evaluation score weight. According to the gray correlation model, the indicator data is associated with the reference sequence, and the correlation coefficient of the comprehensive score is solved by the correlation coefficient calculation formula. Finally, the weighted algorithm is combined with the required score weight to obtain the final comprehensive evaluation score. If it is classified by the subordinate companies of the groups at all levels and the power and other business departments within the enterprise, it can be calculated by classification.

[0114] like Figure 3As shown in the figure, the calculation process of the comprehensive score of the data asset management system maturity evaluation is as follows:

[0115] The first step is to determine the number of evaluation objects n and the number of evaluation indicators m.

[0116] The second step is to standardize the positive and negative indicators.

[0117] The normalized formula of positive indicators is:

[0118]

[0119] The negative indicator standardization formula is:

[0120]

[0121] Among them, P ij is the standardized value of the i-th evaluation index of the j-th evaluation object, V ij is the value of the i-th evaluation index of the j-th evaluation object, m is the number of evaluation indexes, and n is the number of evaluation objects.

[0122] It should be noted that after the positive and negative indicators are standardized, their positive and negative effects are temporarily eliminated by the standardization process and become unified calculation data so that the score weight can be calculated by maximizing the deviation.

[0123] The third step is to establish a total deviation model between the evaluation object j and the indicator values ​​of all other evaluation objects for evaluation index i, and a total deviation model between the evaluation object and other evaluation correlations for index i, and construct the objective function based on the total deviation model.

[0124] For evaluation index i, the total deviation model of the evaluation object j and the index values ​​of all other evaluation objects is:

[0125] For evaluation index i, the total deviation model of the correlation between the evaluation object and other evaluations is:

[0126]

[0127] The objective function is:

[0128]

[0129] Among them, ω i is the weight of the i-th evaluation index, and ω i ≥0, F ij (ω) is the total deviation of the index values ​​of evaluation object j and all other evaluation objects, and k is all other evaluation objects; F i(ω) is the total deviation of the correlation between the evaluation object and other evaluation objects for the evaluation index i; maxF i (ω) is the objective function,

[0130] The fourth step is to optimize and normalize the objective function and calculate the score weight.

[0131] The optimal solution formula of the objective function is:

[0132]

[0133] The weight calculation formula after normalization of the optimal solution of the objective function is:

[0134]

[0135] Among them, ω i * is the optimal solution of the objective function, ω i is the weight of the i-th evaluation index, and ω i ≥0.

[0136] It can be seen from the above calculation formula that if the standardized data and total deviation of a certain evaluation indicator are known, the optimal solution of the objective function corresponding to the evaluation indicator and the score weight can be calculated.

[0137] Step 5: Calculate the correlation coefficient of the evaluation index based on the grey correlation model.

[0138] The correlation coefficient formula is:

[0139]

[0140] Among them, r ij is the correlation coefficient between the i-th evaluation index of the j-th evaluation object and the reference sequence, P ia is the reference value of the i-th evaluation index, m is the number of evaluation indicators, min min(|P ia -P ij |) is the minimum difference of the sequence, max max(|P ia -P ij |) is the maximum difference of the sequence, ρ is the resolution coefficient, the value range is [0,1], and it is generally taken as 0.5.

[0141] Based on the similarity between the curve shape of the comparison series and the curve shape of the reference sequence, it is determined that the reference sequence is the optimal value of each evaluation index, the positive index takes the maximum value, and the negative index takes the minimum value; however, when taking the value of ρ, it is only given as a general value. If the actual situation is different from the general situation, the value can be confidently determined according to objective real conditions, such as 0.4 or 0.8.

[0142] It can be foreseen that by calculating known data, the correlation coefficient of each evaluation index can be calculated using the grey correlation coefficient model.

[0143] Step 6: Calculate the comprehensive score based on the calculated score weight and correlation coefficient.

[0144] The formula for calculating the composite score is:

[0145]

[0146] Among them, C j is the comprehensive score, ω i is the weight, r ij is the correlation coefficient between the i-th evaluation index of the j-th evaluation object and the reference sequence.

[0147] Among them, when calculating the evaluation score for the data asset management system of a single power grid enterprise, the data assets can be classified according to business departments, the evaluation object can be set as the data asset management system of each business department, and the overall score can be calculated by weighting the proportion of business volume.

[0148] Based on the comprehensive score, the weaknesses of the data asset management system are analyzed, and targeted optimization is carried out by combining upper and lower management levels.

[0149] Summarize the comprehensive scores of each secondary indicator evaluation, or the overall evaluation comprehensive score;

[0150] Conduct score analysis on different management levels and identify weaknesses in the management system by referring to the standards;

[0151] The weak points are linked with the organizational structure, execution efficiency, assessment scores and other aspects. The management will spread the management deficiencies of a certain weak point to different areas where improvement can be actually achieved, such as optimizing staffing, optimizing the budget system, optimizing the assessment system, and improving the execution of management systems. The data quality, data security, data supervision and other aspects affected by the weak points are clarified, and the optimization measures are classified by the departments to which they belong and delegated to specific personnel in each department for execution, so as to connect the upper and lower organizational personnel levels of the management system.

[0152] Based on the goals of objectively evaluating the maturity of a data asset management system and improving the management level, an embodiment of the present invention provides a method for evaluating the maturity of a data asset management system of a power grid enterprise, constructs a data asset management evaluation index system based on DCMM, determines an evaluation object, sets evaluation indicators, formulates expert scoring standards, and performs expert scoring on subjective indicators in the evaluation indicators; standardizes positive indicators in the data asset management evaluation index system; standardizes negative indicators in the data asset management evaluation system; constructs a total deviation model of the values ​​of the evaluation indicators corresponding to the evaluation object and all other evaluation objects; constructs a total deviation model of the correlation between the evaluation object and other evaluation objects; constructs an objective function through the total deviation model of the value of the evaluation indicator and the total deviation model of the correlation; and seeks an optimal solution for the objective function; normalizes the optimal solution to obtain the weight of the evaluation indicator; establishes a gray correlation model, uses the weight of the evaluation indicator as the weight for calculating weighting, determines a reference sequence by taking the maximum value of the positive indicator and the minimum value of the negative indicator, and calculates the correlation coefficient of each evaluation indicator; calculates a comprehensive score; analyzes the data asset management evaluation index system through the comprehensive score, determines system problems, and optimizes the system problems in a targeted manner using pre-acquired optimization means. This patent takes into account the characteristics of data assets and their management in the power industry. In order to comprehensively evaluate the maturity of the data asset management system of power grid enterprises, the evaluation method provided by this patent can be used to determine the comprehensive maturity score of the data asset management system, and then targeted optimization and improvement can be carried out on weak points.

[0153] It should be understood that the order of execution of the steps in the above embodiments does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. If there is a special emphasis on the indicator system, or there is a more suitable standard for the measurement criteria, it is not necessary to completely follow the indicator system of the embodiments of the present invention.

[0154] It should be noted that, in this embodiment, the description of each embodiment has its own emphasis, and for the part that is not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0155] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0156] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned data asset management system maturity evaluation method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0157] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for evaluating the maturity of a data asset management system of a power grid enterprise, characterized in that: The following steps are involved: Step 1: Build a data asset management evaluation index system based on DCMM, determine the evaluation object, set the evaluation index, formulate expert scoring standards, and conduct expert scoring on the subjective indicators in the evaluation index; Step 2: Standardize the positive indicators in the data asset management evaluation index system; Standardize the negative indicators in the data asset management evaluation system; Step 3: construct a total deviation model of the values ​​of the evaluation indicators corresponding to the evaluation object and all other evaluation objects; construct a total deviation model of the correlation between the evaluation object and other evaluation objects; Step 4: construct the objective function through the total deviation model of the evaluation index value and the total deviation model of the correlation degree; and find the optimal solution of the objective function; Step 5: Normalize the optimal solution and finally obtain the weight of the evaluation index; Step 6: Establish a grey correlation model, use the weight of the final evaluation index as the weight for calculating weighted, determine the reference sequence by taking the maximum value of the positive index and the minimum value of the negative index, and calculate the correlation coefficient of each evaluation index; Step 7: Calculate the comprehensive score; Step 8. Analyze the data asset management evaluation index system through comprehensive scoring, identify system problems, and use pre-acquired optimization methods to optimize system problems in a targeted manner.

2. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 1, characterized in that: The evaluation indicators include primary indicators, secondary indicators of eight capability domains, and tertiary indicators; The three-level indicators are divided into subjective indicators and objective indicators; The third-level indicator attributes include positive indicators and negative indicators.

3. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 2, characterized in that: The first-level indicator is identified as the maturity of the data asset management system (A); The secondary indicators of the eight capability domains specifically include: data strategy (A1), data governance (A2), data architecture (A3), data application (A4), data security (A5), data quality (A6), data standards (A7), and data life cycle (A8).

4. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 3 is characterized in that: The three-level indicators specifically include: Data strategy (A1) consists of data strategy planning (A11), data strategy implementation (A12), and data strategy evaluation (A13); Data governance (A2) consists of data governance organization (A21), data system construction (A22), and data governance communication (A23); Data architecture (A3) consists of data model (A31), data distribution (A32), data integration and sharing (A33), and metadata management (A34); Data application (A4) consists of data analysis (A41), data open sharing (A42), and data services (A43); Data security (A5) consists of data security strategy (A51), data security management (A52), and data security audit (A53); Data quality (A6) consists of data quality requirements (A61), data quality checks (A62), and data quality analysis (A63); Data standards (A7) include business terms (A71), reference data and master data (A72), data elements (A73), and indicator data (A74); The data life cycle (A8) consists of data requirements (A81), data design and development (A82), data operation and maintenance (A83), and data retirement (A84).

5. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 4, characterized in that: The subjective indicators are: data strategy planning (A11), data strategy implementation (A12), data governance organization (A21), data system construction (A22), data security strategy (A51), data security management (A52), data security audit (A53), data quality requirements (A61), data quality analysis (A63), business terminology (A71), reference data and master data (A72), data element (A73), indicator data (A74), and data requirements (A81).

6. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 1, characterized in that: The normalized representation of the positive indicator is: The normalized representation of the negative indicator is: Among them, i: represents the evaluation index, including positive and negative indicators; j: indicates the evaluation object; P ij : represents the standardized value of the i-th evaluation index of the j-th evaluation object; V ij : represents the value of the i-th evaluation index of the j-th evaluation object; n: represents the number of evaluation objects; min(V ij ): represents the minimum value of the i-th evaluation index of the j-th evaluation object; max(V ij ): represents the maximum value of the i-th evaluation index of the j-th evaluation object.

7. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 6, characterized in that: The total deviation model of the evaluation index values ​​corresponding to the evaluation object and all other evaluation objects is: The total deviation model of the correlation between the evaluation object and other evaluation objects is: Among them, m: represents the number of evaluation indicators; ω i : represents the weight of the i-th evaluation index, and ω i ≥0; k: represents all other evaluation objects; F ij (ω): It is the total deviation of the evaluation index values ​​corresponding to evaluation object j and all other evaluation objects; F i (ω): It is the total deviation of the correlation between evaluation object j and other evaluation objects for evaluation index i.

8. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 7, characterized in that: The objective function is: The optimal solution of the objective function is: The weight calculation model after normalization of the optimal solution of the objective function is: Among them, maxF(ω): represents the objective function; ω i * : represents the optimal solution of the objective function.

9. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 8, characterized in that: The grey relational model is: The comprehensive score calculation formula is: Among them, r ij : It is expressed as the correlation coefficient between the i-th evaluation index of the j-th evaluation object and the reference sequence; P ia is the reference value of the i-th evaluation index; m: represents the number of evaluation indicators; min min(|P ia -P ij |) is the minimum difference of the sequence; max max(|P ia -P ij |) is the maximum difference of the sequence; ρ: represents the resolution coefficient, with a value range of [0,1], C j : Expressed as a comprehensive score; ω i : Represented as weight.

10. The method for evaluating the maturity of a data asset management system of a power grid enterprise according to claim 1, characterized in that: The optimization methods include data quality, data security, data value, institutional optimization at the data supervision level, personnel allocation optimization, and management efficiency optimization.

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