Power grid cost project feature reserve quality evaluation method and system

Abstract

The invention provides a power grid cost project feature reserve quality evaluation method and system, and belongs to the technical field of power grid cost project feature reserve quality evaluation. In order to solve the technical problems of incomplete evaluation analysis, insufficient risk assessment and non-uniform quantification standard in the existing cost class project reserve quality process, the adopted technical scheme is as follows: combining the cost class project data of each batch of a power grid in previous years and the power grid cost class project period and characteristic data; establishing a technical index library as an evaluation model by determining evaluation indexes; inputting the data in the technical index library into an analytic hierarchy process weighting module, converting qualitative data into quantitative data, and constructing an analytic hierarchy process weight model; the entropy weight of each index is calculated based on the information entropy according to the dispersion degree of each index in the technical index library, and then the entropy weight is corrected according to the index to obtain an objective index weight; the method is applied to power grid cost project feature reserve quality evaluation.

Description

Technical Field

[0001] This invention provides a method and system for evaluating the quality of characteristic reserves of power grid cost projects, belonging to the technical field of quality evaluation of characteristic reserves of power grid cost projects. Background Technology

[0002] In the daily production and management of power grid companies, cost-related projects are an important aspect of cost expenditure control. These projects are closely related to the company's daily operations and involve multiple departments. As the amount of cost-related projects gradually increases each year, and the types of projects are numerous and the scope is wide, project reserves, as a key link before project implementation, play a decisive role in project quality control and resource optimization. A comprehensive evaluation is conducted during the project entry review stage, and the results are of great significance to the annual budget allocation and project priority ranking, providing strong support for cost-related project decisions.

[0003] In the current cost project management process, project units, as the main entities submitting project requests, propose project needs based on the company's key tasks and their own operational realities. After review and approval, these requests are added to the project request pool. For projects included in the pool, each implementing unit conducts feasibility studies and prepares project proposals, submits them for feasibility review, and awaits project release for implementation after passing the review. However, this management and control scheme relies on the experience and judgment of financial personnel or experts, lacks quantitative analysis based on historical data, and has the following shortcomings:

[0004] The evaluation indicators in the project reserve review process are relatively singular, with most evaluations focusing only on economic indicators and ignoring the importance of the project in terms of strategic alignment, social benefits, and risk control.

[0005] The evaluation lacks a systematic approach, with no logical relationship between the various audit and evaluation indicators. The evaluation process is highly subjective, and a unified and scientific evaluation system has not been formed.

[0006] Poor dynamic adaptability; existing audit and evaluation methods are often fixed and unchanging, making it difficult to adapt to the characteristics of different types of projects.

[0007] The evaluation results are not deeply integrated with the annual budget allocation rules of the power grid (such as prioritizing high reliability improvement projects) and cannot directly support the project priority ranking.

[0008] Currently, a comprehensive and systematic evaluation system has not been established and applied in the reserve stage of power grid cost-related projects, and the linkage between the review results and the annual budget allocation and project priority ranking of the power grid is weak. In view of the problems existing in the quality evaluation and analysis of cost-related projects, the management of cost expenditures is relatively extensive, the risk assessment is insufficient, and the quantitative standards are not uniform, it is necessary to propose a new reserve quality evaluation scheme to achieve accurate evaluation of the quality of cost-related project reserves and improve the input-output efficiency and management level of power grid companies' cost projects. Summary of the Invention

[0009] To address the technical problems existing in the background art, the present invention provides a method for evaluating the quality of characteristic reserves of power grid cost projects, comprising the following evaluation steps:

[0010] Step 1: Combining data from previous years' power grid cost projects, as well as data on the cycle and characteristics of power grid cost projects, a technical indicator library is constructed as an evaluation model by determining the main evaluation indicators and primary and secondary evaluation indicators;

[0011] Step 2: Input the data from the technical indicator library into the hierarchical analysis weighting module to convert qualitative data into quantitative data and construct the hierarchical analysis weight model;

[0012] Step 3: Based on the dispersion of each indicator in the technical indicator library, calculate the weight coefficient of each indicator based on information entropy, and then correct the entropy weight according to the indicator to obtain the objective indicator weight.

[0013] Step 4: Based on the principle of minimum information entropy, the weights obtained by the analytic hierarchy process of evaluation factors and the weights obtained by the entropy weight method are combined to determine the final weights of the cost project reserve evaluation indicators.

[0014] Step 5: Evaluate and score all power grid cost projects based on the final cost project reserve evaluation index weights, intelligently sort the projects according to their scores, and assess the quality of cost project reserves during the entry review stage based on the scores.

[0015] The specific method for step two is as follows:

[0016] The decision objectives and decision objects to be analyzed are established into a hierarchical structure. The objective layer is defined as the reserve quality rating index for power grid cost projects, the criterion layer is the multiple dimensions of reserve quality evaluation, and the scheme layer is the specific evaluation index.

[0017] First, a judgment matrix is ​​constructed. Each option is compared pairwise, and its importance is assessed, resulting in nine importance levels and their assigned values. The matrix formed by the pairwise comparison results is used as the judgment matrix, and 'a' is defined as... ij The results of the importance comparison between factor i and factor j, a i For aj The relative importance, using a ij It is represented by a scale value, which ranges from 1, 2, ..., 9 and their reciprocals;

[0018] Define the element at the next higher level as B. k The elements of its next level are A1, A2, ..., A n Then, element B k As the standard, element A i With element A j Compare the two elements to determine which one is more relevant to element B. k The influence is greater, thus a judgment matrix is ​​constructed;

[0019] Then, normalization is performed to find the largest eigenvalue λ. max and eigenvector W:

[0020] The sum-product method is used to normalize the elements in each column of the judgment matrix. The calculation formula is as follows:

[0021] (i=1,2,......n)(1);

[0022] The normalized judgment matrix is ​​summed row by row, using the following formula:

[0023] (i=1,2,......n)(2);

[0024] Using the normalized judgment matrix Find the eigenvector W i The calculation formula is:

[0025] (3);

[0026] To prevent the judgment results from being invalid due to subjective factors during actual operation, it is necessary to perform a consistency check on the calculation results. The calculation formula for the check is as follows:

[0027] (4);

[0028] To measure the magnitude of CI, the random consistency index RI is introduced, with the expression:

[0029] (5);

[0030] λ max To determine the largest eigenvalue of the judgment matrix, CI is the consistency index and RI is the average random consistency index. The value of RI is determined by referring to the order of the judgment matrix.

[0031] Verify whether the judgment matrix has satisfactory consistency:

[0032] The CI and the random consistency index RI are compared to obtain the test coefficient CR, which is expressed as follows:

[0033] (6);

[0034] When the result calculated by CR is less than 0.1, it indicates that the judgment matrix is ​​consistent, that is, the weights are reasonable. If it is inconsistent, it needs to be reconstructed until it matches the result.

[0035] The Analytic Hierarchy Process (AHP) was employed, incorporating expert scoring. A judgment matrix was constructed based on expert scores regarding the importance of various cost-related project reserve quality evaluation indicators. A questionnaire was designed to assess the importance of each indicator to reserve quality, and a judgment matrix was built accordingly. The weight values ​​for each indicator were determined, and the questionnaire results were compiled to generate the judgment matrix tables. Finally, the final AHP weight W was obtained by multiplying the proportion of each indicator in the criterion layer and the alternative layer. i .

[0036] The specific method for step three is as follows:

[0037] For evaluation factors with different properties, it is necessary to normalize the positive and negative indicators separately. For positive indicator factors, Equation (7) is used, and for negative indicator factors, Equation (8) is used. The calculation formula is as follows:

[0038] (7);

[0039] (8);

[0040] In the formula, R ij X represents the normalized value of the i-th evaluation factor under the j-th factor; ij X represents the calculated value of the corresponding evaluation factor. Minj X represents the minimum value of evaluation factor j; Maxj This represents the maximum value of evaluation factor j; the normalized result of the cost item evaluation index in each scheme layer is calculated according to the above formula;

[0041] Then calculate the entropy value of each factor, using the following formula:

[0042] (9);

[0043] In the formula, K = 1 / ln; n represents the total number of evaluation factors; E j This represents the entropy value corresponding to the j-th evaluation factor; in actual calculations, since the index can have a value of 0, to avoid ln M ij Meaningless, making ;

[0044] The weights of the evaluation factors are calculated using the following formula:

[0045] (10);

[0046] In the formula, W j Let be the objective weight of the j-th evaluation factor;

[0047] Based on the above formula, the weight information of cost item evaluation indicators in each scheme layer based on the entropy method is obtained, including information entropy E. j Coefficient of difference D j Entropy weight W j .

[0048] The specific method for step four is as follows:

[0049] The weights obtained using the analytic hierarchy process (AHP) and the entropy weight method are combined to calculate the final weights of the cost project reserve evaluation indicators. The calculation formula is as follows:

[0050] (11);

[0051] In the formula, W k For comprehensive weighting; W 1k W represents the weight corresponding to the k-th indicator in the analytic hierarchy process. 2k This represents the weight value corresponding to the k-th index in the entropy weight method.

[0052] The combined weights based on the AHP-entropy method are calculated using the above formula. The combined weights of each scheme layer are then statistically analyzed, including the weights W from the analytic hierarchy process. i Entropy method weight W j Combination weight W j '.

[0053] The evaluation model constructed in step one can also be constructed using fuzzy comprehensive evaluation method and neural network evaluation method, or using data envelopment analysis method and grey relational analysis method.

[0054] Step four, determining the index weights, can also be achieved using principal component analysis or the Delphi method.

[0055] The evaluation system adopted to implement a quality evaluation method for characteristic reserves of power grid cost projects includes the following evaluation and analysis modules:

[0056] Evaluation index construction module: used to construct an evaluation model after comprehensively considering the project's strategic fit index, technical feasibility index, economic rationality index, social benefit index and risk controllability;

[0057] Indicator weight determination module: The weight of each evaluation indicator is determined by using a combination of subjective and objective methods;

[0058] Evaluation data collection module: Used to collect relevant evaluation data for cost-related projects, which is obtained through databases of cost-related projects from previous years, market research, and expert evaluation.

[0059] Evaluation Result Analysis and Application Module: Evaluates and scores all cost-related projects, intelligently sorts them according to their scores, and evaluates the quality of cost-related projects in the review and approval stage based on their scores.

[0060] The strategic alignment indicators include: policy compliance, development plan matching, urgency of implementation, plan completeness, and performance indicators;

[0061] The technical feasibility indicators include: technology maturity, technological innovation, implementation conditions, and rationality of compilation;

[0062] The economic rationality indicators include: budget preparation rationality, input-output efficiency, cost control capability, and revenue sustainability;

[0063] The social benefit indicators include: job creation benefits, scope of social impact, and ecological and environmental impact.

[0064] The controllability of the risks mentioned includes policy risks, market risks, technological risks, and management risks.

[0065] The evaluation result analysis and application module analyzes the evaluation results, establishes a mapping rule between the evaluation results and budget allocation, defines that projects with an evaluation score of ≥75 will be automatically included in the annual priority implementation list, and outputs the priority ranking.

[0066] The beneficial effects of this invention compared to existing technologies are as follows: This invention provides a method and system for evaluating the quality of reserve characteristics of power grid cost-related projects. This scheme comprehensively considers indicators from multiple dimensions, which can fully reflect the quality of reserve cost-related projects, and the evaluation indicators are more comprehensive. When using this evaluation method to evaluate cost projects, it combines subjective experience judgment and objective weighting of the analytic hierarchy process, which can effectively combine subjective and objective factors to form complementary advantages, improve the rationality and scientificity of the evaluation results, and automate the mapping rules between the evaluation results and budget allocation and project priority ranking, which can maximize the role of the evaluation results, improve the quality of project reserves, and further enhance the guidance, pertinence and effectiveness of resource allocation. Attached Figure Description

[0067] The present invention will be further described below with reference to the accompanying drawings:

[0068] Figure 1 The flowchart illustrates the steps of the method for evaluating the quality of power grid cost-related project feature reserves according to the present invention. Detailed Implementation

[0069] like Figure 1 As shown, this invention addresses the problems of incomplete evaluation and analysis, insufficient risk assessment, and inconsistent quantitative standards in the existing process of evaluating the quality of cost-related project reserves. It proposes a method and system for evaluating the quality of characteristic reserves of power grid cost-related projects, enabling accurate evaluation of the quality of cost-related project reserves and improving the input-output efficiency and management level of power grid companies' cost projects.

[0070] The power grid cost project management and evaluation scheme provided by this invention integrates professional indicators and quantitative models for power grid cost projects, and establishes a comprehensive, scientific, and dynamically adjustable pre-project reserve evaluation indicator system and method. By constructing multi-level and multi-dimensional evaluation indicators, it achieves accurate matching between evaluation results and power grid operation needs. This invention establishes a standardized scoring mechanism based on historical data and industry standards to improve the comparability and credibility of evaluation results. Combining data from previous batches of cost projects, as well as the cycle and characteristics of power grid cost projects, and incorporating a reasonable weight allocation and dynamic adjustment mechanism, it achieves objective and accurate evaluation of the early reserve and entry stage of cost projects. Simultaneously, it evaluates and scores all cost projects, intelligently sorts them according to their scores, and uses these scores to assess the quality of cost project reserves during the entry review stage, supporting project priority decisions and optimizing cost resource allocation.

[0071] Furthermore, the present invention provides a quality evaluation system for the characteristic reserves of power grid cost projects, which mainly includes the following evaluation and analysis modules:

[0072] (1) Evaluation index construction module:

[0073] This module, after comprehensively considering multiple dimensions such as project strategic alignment, technical feasibility, economic rationality, social benefits, and risk controllability, is used to construct a comprehensive and scientific evaluation indicator system. This includes determining specific indicators for each dimension. For example, strategic alignment indicators include policy compliance, development plan matching, urgency of implementation, plan completeness, and performance indicators; technical feasibility indicators include technology maturity, technological innovation, implementation conditions, and rationality of budget preparation; economic rationality indicators include budget preparation rationality, input-output efficiency, cost control capability, and sustainability of benefits; social benefit indicators include job creation benefits, social impact scope, and ecological and environmental impact; and risk controllability includes policy risk, market risk, technological risk, and management risk.

[0074] (2) Indicator weight determination module:

[0075] This module employs scientific methods to determine the weights of each evaluation indicator, primarily using a combination of subjective and objective approaches. First, it uses the Analytic Hierarchy Process (AHP) to establish a hierarchical model and calculate the weight coefficients of each indicator. Then, it uses the entropy weighting method to correct for subjective factors. Finally, based on the principle of minimum information entropy, it integrates the weights obtained from the AHP and the entropy weighting method. This approach fully considers the expertise and experience of the expert database while minimizing the influence of subjective factors on the evaluation data. This module ensures both the scientific rationality of the evaluation process and methods and the interpretability of the evaluation results.

[0076] (3) Evaluation data collection module:

[0077] This module is used to collect relevant evaluation data for cost-related projects, and obtains data through various methods such as historical batches of cost-related projects databases, market research, and expert evaluation.

[0078] (4) Evaluation Result Analysis and Application Module:

[0079] This module evaluates and scores all cost-related projects, intelligently sorts them according to their scores, and assesses the quality of cost-related projects in the review and approval stage based on their scores. This module not only provides evaluation results but also conducts in-depth analysis of the results and establishes mapping rules between evaluation results and budget allocation. For example, projects with an evaluation score of ≥75 will be automatically included in the annual priority implementation list, and the priority ranking will be output.

[0080] Based on the above evaluation and analysis module, this invention also provides a method for evaluating the quality of characteristic reserves of power grid cost projects. First, a comprehensive evaluation index system is constructed through the evaluation index system construction module. Then, the weight of each index is determined using the index weight determination module. The evaluation data acquisition module collects relevant data of the project. Then, the data is processed and evaluated through the evaluation model construction module. Finally, the evaluation result analysis and application module analyzes and applies the evaluation results.

[0081] Furthermore, in an embodiment of the present invention, a method for evaluating the quality of characteristic reserves of power grid cost-related projects is also proposed, specifically including the following evaluation steps:

[0082] Step 1: Combining historical data on power grid cost projects from previous years, as well as data on the project cycles and characteristics, a technical indicator library is constructed as the evaluation model by determining the main evaluation indicators and primary and secondary evaluation indicators. This specifically includes:

[0083] By constructing a hierarchical structure and conducting a systematic analysis of the problem, the focus is clearly defined on the objectives to be achieved. The problem is then decomposed into different indicators around these objectives, and the logical relationships between these indicators, including horizontal influence relationships and vertical hierarchical relationships, are clarified to create a hierarchical indicator system model. In the embodiments of this invention, after screening, the main evaluation indicators for the criteria layer include: strategic alignment, technical feasibility, economic rationality, social benefits, and risk controllability. Specifically, strategic alignment indicators include policy compliance, development plan matching, urgency of implementation, plan completeness, and performance indicators; technical feasibility indicators include technological maturity, technological innovation, implementation conditions, and rationality of budget preparation; economic rationality indicators include budget preparation rationality, input-output efficiency, cost control capability, and sustainability of benefits; social benefits indicators include employment-generating benefits, social impact scope, and ecological and environmental impact; and risk controllability includes policy risk, market risk, technological risk, and management risk, as shown in Table 1 below.

[0084] Table 1 Selection of Quality Evaluation Indicators for Cost-Related Project Reserves

[0085]

[0086] Step Two: Input the data from the technical indicator library into the hierarchical analysis weighting module to convert qualitative data into quantitative data and construct the hierarchical analysis weight model, specifically including:

[0087] The decision-making objectives and objects to be analyzed are established in a hierarchical structure. The objective layer refers to the purpose of the decision, the criteria layer refers to the factors to be considered, and the alternative layer refers to the objects of the decision. In this invention, the objective layer is the reserve quality rating index for power grid cost-related projects, the criteria layer is the five dimensions of reserve quality evaluation, and the alternative layer is the specific evaluation index. First, a judgment matrix is ​​constructed. When comparing each alternative pairwise, its importance is rated, giving nine importance levels and their assigned values. The matrix formed by the pairwise comparison results is called the judgment matrix, a. ij The results of the importance comparison between factor i and factor j, a i For a j The relative importance, usually a ij The values ​​are represented by scale values, which range from 1, 2, ..., 9 and their reciprocals. The meaning of each scale value is shown in Table 2 below.

[0088] Table 2 Scale Value Quantification Level Table

[0089]

[0090] The element at the next higher level is B. k The elements of its next level are A1, A2, ..., A n So, it's based on B. k As a standard, Ai With A j Compare the two factors to see which one is more relevant to B. k The influence is greater, and the judgment matrix constructed accordingly is shown in Table 3 below.

[0091] Table 3 General Form of the Judgment Matrix

[0092]

[0093] Normalization process to find the largest eigenvalue λ max And the eigenvector W.

[0094] Normalize the elements in each column using the sum-product method:

[0095] (i=1,2,......n)(1);

[0096] Add the normalized judgment matrices row by row:

[0097] (i=1,2,......n)(2);

[0098] The normalized judgment matrix Find the eigenvector W i :

[0099] (3);

[0100] In practice, due to the complexity of the evaluation problem and the diversity of subjective perceptions within the expert database, there is no fixed reference point when comparing each indicator pairwise. Therefore, to prevent invalid judgments due to subjective factors in practice, a consistency check is required. The check process is as follows:

[0101] (4);

[0102] To measure the magnitude of CI, the random consistency index RI is introduced, with the expression:

[0103] (5);

[0104] λ max To determine the largest eigenvalue of the matrix, CI is the consistency index, and RI is the average random consistency index. The values ​​of RI are shown in Table 4 below, depending on the order of the matrix.

[0105] Table 4. RI Order Comparison Table

[0106]

[0107] Considering that deviations in consistency may be caused by random factors, when verifying whether the judgment matrix has satisfactory consistency, it is necessary to compare CI with the random consistency index RI to obtain the test coefficient CR, as shown in the following formula:

[0108] (6);

[0109] When the result calculated by CR is less than 0.1, it indicates that the judgment matrix is ​​consistent, that is, the weights are reasonable. If it is inconsistent, it needs to be reconstructed until it matches the result.

[0110] This invention employs the Analytic Hierarchy Process (AHP) and expert scoring. A judgment matrix is ​​constructed based on experts' scores of the importance of cost-related project reserve quality evaluation indicators. To obtain objective, reliable, and persuasive indicator weights, a questionnaire is designed to construct the judgment matrix, focusing on the importance of each cost-related project indicator to reserve quality. The questionnaire was distributed to management, finance, departmental experts, electrical engineering experts, employees at all levels, and relevant department managers. Expert scores on the evaluation indicators were collected. 30 questionnaires were distributed on August 15, 2025, and 25 were returned on September 1, 2025. After removing 5 invalid questionnaires, 20 valid questionnaires were obtained. The weights from the collected questionnaires were analyzed to generate judgment matrices. Finally, the final AHP weight W is obtained by multiplying the proportion of each indicator in the criterion layer and the scheme layer. i .

[0111] Table 5. Weighting of Financial Performance Evaluation Indicators Using the Analytic Hierarchy Process (AHP)

[0112]

[0113] Step 3: Based on the dispersion of each indicator in the technical indicator library, calculate the entropy weight of each indicator based on information entropy, and then make certain adjustments to the entropy weight according to the indicator to obtain a more objective indicator weight, specifically including:

[0114] Since the evaluation factors have indicators of different properties, the positive and negative indicators are normalized separately. For the positive indicator factors, equation (1) is used, and for the negative indicator factors, equation (2) is used. The calculation formula for normalization is as follows:

[0115] (7);

[0116] (8);

[0117] In the formula, R ij X represents the normalized value of the i-th evaluation factor under the j-th factor;ij X represents the calculated value of the corresponding evaluation factor. Minj X represents the minimum value of evaluation factor j; Maxj This represents the maximum value of evaluation factor j; the normalized results of the cost item evaluation indicators in each scheme layer are calculated according to the above formula and entered into the table.

[0118] Then calculate the entropy value of each factor, using the following formula:

[0119] (9);

[0120] In the formula, K = 1 / ln; n represents the total number of evaluation factors; E j This represents the entropy value corresponding to the j-th evaluation factor; in actual calculations, since the index can have a value of 0, to avoid ln M ij Meaningless, making .

[0121] The weights of the evaluation factors are calculated using the following formula:

[0122] (10);

[0123] In the formula, W j Let be the objective weight of the j-th evaluation factor.

[0124] Based on the above formula, the weight information of cost item evaluation indicators in each scheme layer based on the entropy method is obtained, including information entropy Ej, difference coefficient Dj, and entropy weight Wj, and entered into Table 6.

[0125] Table 6 Weights of Cost Item Evaluation Indicators Based on Entropy Method

[0126]

[0127] Step 4: Based on the principle of minimum information entropy, combine the weights obtained using the analytic hierarchy process (AHP) and the weights obtained using the entropy weight method to calculate the final weights of the cost project reserve evaluation indicators. The calculation formula is as follows:

[0128] (11);

[0129] In the formula, W k For comprehensive weighting; W 1k W represents the weight corresponding to the k-th indicator in the analytic hierarchy process. 2k This represents the weight value corresponding to the k-th index in the entropy weight method.

[0130] The combined weights based on the AHP-entropy method are calculated according to the above formula. The combined weights of each scheme layer are statistically analyzed, including the weights Wi of the analytic hierarchy process, the weights Wj of the entropy method, and the combined weights Wj', and then entered into Table 7.

[0131] Table 7. Combination weights based on AHP-entropy method

[0132]

[0133] The weights of the multiple sub-evaluation indicators determined by the above steps yield the final cost project reserve evaluation indicator weight calculation model, as follows:

[0134] Cost-related project reserve quality = 0.1421 * strategic alignment + 0.2315 * technical feasibility + 0.2512 * economic rationality + 0.0839 * social benefits + 0.2913 * risk controllability;

[0135] Strategic alignment = 0.2167 * policy compliance + 0.1942 * development plan matching + 0.1985 * urgency of implementation + 0.0957 * plan completeness + 0.2949 * performance indicators;

[0136] Technical feasibility = 0.2691 * Technology maturity + 0.0981 * Technology innovation + 0.3110 * Implementation conditions + 0.3218 * Reasonableness of preparation;

[0137] Economic rationality = 0.2305 * Budget preparation rationality + 0.2607 * Input-output efficiency + 0.2882 * Cost control capability + 0.2205 * Revenue sustainability;

[0138] Social benefits = 0.2610 * job creation benefits + 0.3647 * scope of social impact + 0.3743 * ecological and environmental impact;

[0139] Risk controllability = 0.2345 * policy risk + 0.2039 * market risk + 0.2822 * technology risk + 0.2794 * management risk.

[0140] Step 5: All power grid cost-related projects are evaluated and scored based on the final cost project reserve evaluation index weights. The projects are then intelligently sorted according to their scores to assess the quality of cost-related project reserves during the entry review stage. The 15 power grid cost-related project reserves from recent years are ranked in descending order of their comprehensive scores, and a comprehensive score and ranking table of power grid cost-related project reserve quality is generated. The results are shown in Table 8.

[0141] Table 8. Comprehensive Scoring and Ranking of Power Grid Cost-Related Project Reserve Quality

[0142]

[0143] Based on the original data and the comprehensive scores of each project, Project G is a key project to ensure terminal online rates, improve the application level of distribution network automation, and enhance the reliability of power grid supply. It is also a project the company has been undertaking for many years. Detailed specifications for material types and quantities were developed based on project requirements, and all materials were procured through e-commerce. The budget preparation is highly reasonable, and cost control and risk management are highly manageable; therefore, it receives the highest comprehensive score. Project J is a management innovation technology promotion project that comprehensively supports power grid management and power supply services, achieving effective allocation of human, financial, and material resources. It promotes project-based and lean management of cost-related projects through management innovation, demonstrating high strategic alignment, strong technological innovation, and a complete project implementation plan; therefore, it receives a relatively high comprehensive score. Project M is a new project with a lack of reference for costs and project implementation, low plan completeness, and low budget preparation and cost control capabilities; therefore, it receives the lowest comprehensive score. Project C is a safety tool and equipment procurement project. Due to the large variety of safety tools and equipment, the significant price differences between each type, and varying wear and tear rates, specific costs are difficult to estimate precisely, resulting in low economic rationality and a low comprehensive score.

[0144] In another embodiment of the present invention, in determining the above-mentioned index weights, in addition to the analytic hierarchy process and the entropy weight method, principal component analysis, the Delphi method, and other methods can also be used to determine the index weights.

[0145] In terms of constructing the aforementioned evaluation models, in addition to fuzzy comprehensive evaluation and neural network evaluation, methods such as data envelopment analysis (DEA) and grey relational analysis can also be used.

[0146] To strengthen budget execution control and analysis, and improve cost management, this invention establishes a project pre-evaluation index system based on the reserve entry stage. This system, guided by business needs and aligned with the power grid company's development plan and key tasks, strictly controls the reserve entry process, conducts pre-evaluation scoring of all cost-related projects, intelligently sorts projects according to their scores, and incorporates them into the project demand database. This improves the quality of project reserves and further enhances the guidance, relevance, and effectiveness of resource allocation.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the quality of a reserve of features of a grid cost class project, characterized in that: The evaluation steps include the following: Step 1: Based on the data of previous batches of power grid cost projects, the cycle and characteristics of the power grid cost projects, the main evaluation indexes and the first-level and second-level evaluation indexes are determined to build a technical index library as an evaluation model; Step 2: The data in the technical index library is input into the analytic hierarchy process weighting module to convert the qualitative data into quantitative data and build an analytic hierarchy process weight model; Step 3: Based on the dispersion of each index in the technical index library, the weight coefficients of each index are calculated based on information entropy, and the index weight is modified according to the entropy weight to obtain an objective index weight; Step 4: Based on the minimum information entropy principle, the weight obtained by the evaluation factor analytic hierarchy process method and the weight obtained by the entropy weight method are combined to determine the final cost project reserve evaluation index weight; Step 5: Based on the final cost project reserve evaluation index weight, all the power grid cost projects are evaluated and scored, the projects are intelligently sorted according to the scores, and the quality of the cost project reserve in the storage and review stage is evaluated based on the scores.

2. The method for evaluating the quality of a reserve of features of grid cost-type projects according to claim 1, characterized in that: The specific method of step 2 is as follows: The hierarchical structure of the decision target and the decision object to be analyzed is established, the target layer is defined as the power grid cost project reserve quality evaluation index, the criterion layer is defined as multiple dimensions of the reserve quality evaluation, and the scheme layer is defined as specific evaluation indexes; Firstly, the judgment matrix is constructed, each scheme is compared with each other, the importance level is evaluated, nine importance levels and their assigned values are given, the matrix composed of the comparison results is taken as the judgment matrix, a ij is defined as the importance comparison result between factor i and factor j, a i is the relative importance of a j , the scale value of a ij is used to represent the relative importance of a j , the value range is between 1, 2, …, 9 and their reciprocals; Define the element at the next higher level as B. k The elements at the next level are A1, A2, ..., A n Then, element B k As the standard, element A i With element A j Compare the two elements to determine which one is more relevant to element B. k The influence is greater, thus a judgment matrix is ​​constructed; Then normalize to find the largest eigenvalue λ max and eigenvector W: The elements in each column of the judgment matrix are normalized by using the sum-product method, and the calculation formula is: (i = 1, 2,... n) (1); The normalized judgment matrix is added by row, and the calculation formula is: (i = 1,2,... n) (2); Using the normalized judgment matrix Finding eigenvectors W i The calculation formula is: （3）； In order to prevent the judgment result from being invalid due to subjective factors in the actual operation process, consistency check needs to be performed on the calculation result, and the calculation formula of the test process is: （4）； In order to measure the size of CI, a random consistency index RI is introduced, and the expression is: （5）； λ max To determine the largest eigenvalue of the judgment matrix, the CI is a consistency index, and the RI is an average random consistency index. The value of the RI is determined by comparing the order of the judgment matrix. The judgment matrix is tested for satisfactory consistency: The CI and the random consistency index RI are compared to obtain the test coefficient CR, and the expression is: （6）； When the result calculated by CR is less than 0.1, it indicates that the judgment matrix has consistency, that is, the weight is reasonable, if it is inconsistent, it needs to be rebuilt until the result meets the requirement; Adopt analytic hierarchy process, quote expert scoring method, according to the result of expert scoring on the importance of cost project reserve quality evaluation index to build judgment matrix, according to the importance of each index of cost project to reserve quality, design survey questionnaire to build judgment matrix, finally determine the weight value of each index, collate the survey questionnaire to get the weight result statistics of each judgment matrix table, finally according to the proportion of each index in the rule layer and the scheme layer, multiply to get the final analytic hierarchy process weight W i .

3. The method of claim 2, wherein: The specific method of step 3 is as follows: For indexes with different properties of the evaluation factors, the positive and negative indexes need to be normalized respectively, the positive index factor is processed by formula (7), and the negative index factor is processed by formula (8), and the calculation formula is: （7）； （8）； wherein R ij represents the normalized value of the i-th evaluation factor under the j-th factor; X ij represents the calculated value of the corresponding evaluation factor; X Minj represents the minimum value of the j-th evaluation factor; X Maxj represents the maximum value of the j-th evaluation factor; the normalized processing result of the cost item evaluation index in each scheme layer is calculated according to the above formula; Then the entropy value of each factor is calculated, and the calculation formula is: （9）； wherein K = 1 / lnn; n represents the total number of evaluation factors; E j represents the entropy value corresponding to the jth evaluation factor; In actual calculation, since the index has 0 value, in order to avoid ln M ij Nonsense, let ; The weight of the evaluation factor is calculated, and the calculation formula is: （10）； In the formula, W j is the objective weight of the jth evaluation factor; According to the above formula, the cost item evaluation index weight information in each scheme layer based on the entropy value method is obtained, including information entropy E j , difference coefficient D j , and entropy weight W j .

4. The method of claim 3, wherein: The specific method of step 4 is as follows: The weight obtained by the evaluation factor analytic hierarchy process method and the weight obtained by the entropy weight method are combined to calculate the final cost project reserve evaluation index weight, and the calculation formula is: （11）； In the formula, W k is a comprehensive weight; W 1k is a weight corresponding to the kth index of the analytic hierarchy process; W 2k is the weight value corresponding to the kth index of the entropy weight method; According to the above formula, the combination weight based on the AHP-entropy method is calculated, and the combination weights of each scheme layer are counted, including the analytic hierarchy process weight W i , the entropy method weight W j , and the combination weight W j '.

5. The method of claim 1, wherein: The evaluation model of step 1 can also use fuzzy comprehensive evaluation method and neural network evaluation method, or use data envelopment analysis method and grey correlation analysis method.

6. The method of claim 1, wherein: The index weight of step 4 can also use principal component analysis method and Delphi method.

7. An evaluation system for implementing the method for evaluating the quality of a reserve of features of a power grid cost type project according to any one of claims 1 to 6, characterized in that it comprises: The evaluation analysis module includes the following: The evaluation index construction module is used to build an evaluation model after comprehensively considering the strategic fit index, the technical feasibility index, the economic rationality index, the social benefit index and the risk controllability of the project; The index weight determination module: the weight of each evaluation index is determined by adopting the method of subjective and objective combination; The evaluation data acquisition module: used for collecting the related evaluation data of the cost type project reserve, and the data is obtained through the database of each batch of cost type projects in the past years, market research and expert evaluation; The evaluation result analysis and application module: all cost type projects are evaluated and scored, and the intelligent sorting is carried out according to the project score, and the quality of the cost type project reserve in the storage review stage is evaluated according to the score.

8. The evaluation system for use in the method for evaluating the quality of a reserve of characteristics of grid cost-type items according to claim 7, characterized in that it comprises: The strategic fit index includes: policy compliance, development planning matching degree, urgency of development, plan completeness, performance index; The technical feasibility index includes: technology maturity, technological innovation, implementation condition, compilation rationality; The economic rationality index includes: budget compilation rationality, input-output efficiency, cost control ability, income sustainability; The social benefit index includes: employment driving benefit, social influence range, ecological environment influence; The risk controllability includes policy risk, market risk, technical risk and management risk.

9. The evaluation system for use in the method for evaluating the quality of a reserve of features of grid cost-type projects according to claim 7, characterized in that it comprises: The evaluation result analysis and application module analyzes the evaluation result, establishes the mapping rule of the evaluation result and the budget allocation, defines that the project with the evaluation score greater than or equal to 75 points will be automatically included in the annual priority implementation list, and outputs the priority ranking.

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