Construction method of power grid fixed-point industry assistance effect evaluation system

Abstract

The invention relates to the technical field of industry assistance effect analysis, in particular to a construction method of a power grid fixed-point industry assistance effect evaluation system, which comprises qualitative and quantitative analysis, grading and layering, index evaluation, analytic hierarchy process and index weight association. According to the construction method of the power grid fixed-point industry assistance effect evaluation system, evaluation index determination and benefit quantitative analysis are assisted on the basis of a public policy benefit evaluation theory, and the performance of an industry assistance project in multiple dimensions such as economic benefits, industrial development and social benefits is comprehensively measured by fusing the policy theory; therefore, the urban and rural gap and the effect and problem of rural construction are found, a scientific basis is provided for policy adjustment and optimization, evaluation dimensions are selected to finely decompose a complex decision target in industrial assistance work into a plurality of levels of multiple indexes, and then importance judgment of pairwise comparison is made for elements of each level, so that the evaluation accuracy is improved. And determining and associating the weight of each index element to form qualitative and quantitative comprehensive evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of analysis of the effectiveness of industrial assistance, and specifically to a method for constructing an evaluation system for the effectiveness of power grid targeted industrial assistance. Background Art

[0002] Constructing an effectiveness evaluation system can accurately measure the actual results after implementing assistance measures in specific villages, provide lessons for subsequent assistance work, summarize successful models and existing problems through the analysis of the effectiveness of industrial assistance in different villages and different industries, provide reference for carrying out industrial assistance work in other regions in the future, enable the assistance work to be continuously optimized and improved, and better adapt to the actual situation and development needs of different villages.

[0003] However, from the current situation of the analysis of the effectiveness of industrial assistance, it is difficult to quantify and accurately measure the indicators for the development of assistance industries in some regions, which may affect the comprehensiveness of the evaluation results. Secondly, there are differences in the selection of indicators and the distribution of weights in different studies, and there is a lack of a unified evaluation standard, which limits the comparability between research results, resulting in relatively weak long-term dynamic evaluation of the effectiveness of industrial assistance and making it difficult to comprehensively reflect the sustainable impact of industrial assistance. Therefore, it is necessary to design an evaluation system for the effectiveness of industrial assistance, and thus a method for constructing an evaluation system for the effectiveness of power grid targeted industrial assistance is proposed. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides a method for constructing an evaluation system for the effectiveness of power grid targeted industrial assistance, which has the advantages of hierarchical refinement of indicators and correlation of indicator weights, and solves the problems existing in the current industrial assistance work, such as difficult accurate measurement of indicators, establishment of a unified evaluation standard, and inability to conduct long-term dynamic evaluation of the effectiveness of industrial assistance.

[0006] (II) Technical Solutions

[0007] To achieve the above purpose of hierarchical refinement of indicators and correlation of indicator weights, the present invention provides the following technical solutions: A method for constructing an evaluation system for the effectiveness of power grid targeted industrial assistance, comprising the following steps:

[0008] S1. Qualitative and Quantitative Analysis: Analyze the evaluation indicators based on the literature review and the research on public policy evaluation theory to determine the evaluation index system for the effectiveness of industrial assistance;

[0009] S2. Grading and Layering: Establish a hierarchical structure model to divide the evaluation index system for assistance effectiveness into layers;

[0010] S3. Indicator Evaluation: Evaluate the importance of the indicators at each level of the model based on the scale scoring method;

[0011] S4. Hierarchical analysis: Construct a judgment matrix, calculate the maximum eigenvalue and eigenvector, conduct a single-layer consistency test after normalizing the eigenvector, and then conduct a total-layer consistency test after sorting the total hierarchy;

[0012] S5. Index weight association: Based on the group decision-making method, collect the index scoring data of multiple experts, conduct hierarchical analysis respectively to obtain the sorting vector, perform weighted arithmetic average processing and associate each evaluation index with the corresponding index weight value to construct an evaluation system for the effectiveness of industrial assistance.

[0013] Preferably, study the evaluation dimensions of the evaluation indexes, including the economic benefit dimension, the industrial development dimension, and the social benefit dimension. Select secondary indexes according to the economic benefit dimension, including: 1) The growth situation of the village economy; 2) The growth situation of the assistance income; 3) The employment driving effect;

[0014] Select secondary indexes according to the industrial development dimension, including: 1) The extension situation of the industrial chain; 2) The evaluation of the competitiveness of agricultural products; 3) The evaluation of the innovation ability;

[0015] Select secondary indexes according to the social benefit dimension, including: 1) The assistance effect; 2) The public service ability; 3) The infrastructure construction situation; 4) The participation effect; 5) The human settlement environment.

[0016] Preferably, select tertiary indexes according to the economic benefit dimension, including: 1) The growth situation of the village economy: The change rate of the total agricultural output value, the change rate of the total industrial output value, and the change rate of the total service output value; 2) The growth situation of the assistance income: The growth rate of the per capita disposable income of rural residents and the growth rate of the village collective economic income; 3) The employment driving effect: The growth rate of the number of newly added employment positions, the employment growth rate of the permanent labor force, and the growth rate of the number of people receiving vocational skills training as evaluation indexes;

[0017] Select tertiary indexes according to the industrial development dimension, including: 1) The extension situation of the industrial chain: The conversion rate of agricultural product processing, the growth rate of rural online retail sales, the change rate of the number of new agricultural business entities, the change rate of the number of rural tourism reception facilities, and the change rate of the number of rural tourism reception person-times; 2) The evaluation of the competitiveness of agricultural products: The change rate of the number of agricultural brands, the change rate of the number of agricultural products included in the "Guangdong Brand" agricultural brands; 3) The change rate of the number of cooperation projects with universities, research institutions, etc., and the number of innovation achievements of the assisted industries;

[0018] Select the third-level indicators according to the social benefit dimension, including: 1) Assistance effect: the change rate of the number of monitored households and the growth rate of the per capita income of monitored households; 2) Public service capacity: the change rate of the number of cultural facilities, the change rate of the number of sports and fitness facilities, the coverage rate of elderly care service facilities, and the number of electrification renovations of teaching equipment in rural schools; 3) Infrastructure construction situation: the proportion of hardened roads connecting households in the village, the 5G network coverage rate, the change rate of the number of upgraded power infrastructure, the coverage rate of lighting on the main roads in the village, and the popularization rate of rural tap water; 4) Participation effect: the number of times farmers' representatives participate in meetings and discuss matters, and the satisfaction of farmers; 5) Human settlement environment: the area of rural renovation, the renovation rate of rural housing walls, the construction area of small village parks, and the green coverage rate of the village.

[0019] Preferably, according to the secondary indicators and tertiary indicators corresponding to the evaluation dimension, determine the evaluation index system for the effectiveness of industrial assistance. The steps for establishing the hierarchical structure model include: 1) Set the evaluation of the effectiveness of industrial assistance as the model target layer A; 2) Set the evaluation dimension as the main criterion layer; 3) Set the secondary indicators corresponding to the evaluation dimension as the sub-criterion layer B; 4) Set the tertiary indicators corresponding to the secondary indicators as the index layer C to form a hierarchical structure and construct a hierarchical structure model.

[0020] Preferably, evaluate the importance of the indicators at each level of the model, including: randomly select two indicator factors for comparison in each level, select 1 to 9 as the scale values for importance judgment, and score the importance of the indicator factors through the scale scoring method;

[0021] Among them, the scale values 1, 3, 5, 7, and 9 respectively represent the increasing effect of the importance degree when one indicator factor is compared with another indicator factor, while the scale values 2, 4, 6, and 8 respectively represent the intermediate values for adjacent judgments between the scale values 1, 3, 5, 7, and 9;

[0022] The steps for constructing the judgment matrix include:

[0023] 1) Set A to represent the matrix target, and C i , C j (i, j = 1, 2, 3,..., n) represent the matrix factors, and C ij (i, j = 1, 2, 3,..., n) represent the indicator factors C at each level i The numerical value of the relative importance of the indicator factor C j , then the judgment matrix is expressed as:

[0024]

[0025] 2) According to P ω = λ max W, calculate the maximum eigenvalue λ of the judgment matrix maxThe corresponding eigenvector W. Based on the calculated eigenvector W, through obtain the weight distribution of each hierarchical index, substitute the known data, and thus calculate the maximum eigenvalue λ max value;

[0026] Among them (PW) i represents the i-th element;

[0027] 3) Normalize the eigenvector W according to the Z-score normalization method, including: where μ represents the mean of the eigenvector W, σ represents the standard deviation of the eigenvector W, and W' represents the weight vector obtained after normalization.

[0028] Preferably, perform a single-layer consistency test on the judgment matrix. The consistency test formula includes: CR = CI / RI;

[0029] where CR represents the random consistency ratio of the judgment matrix, the standard value of RI is a constant, representing the average random consistency index of the judgment matrix, and CI represents the general consistency index of the judgment matrix, which is calculated by the following formula: CI = (λ max -n) / (n - 1);

[0030] If the CR of the judgment matrix < 0.1 or when λ max = n, making CI = 0, then it is determined that the single-layer hierarchy conforms to the consistency principle and the corresponding judgment matrix has satisfactory consistency, and the weight vector W' corresponding to the judgment matrix can represent the corresponding index weights;

[0031] Conversely, if CR ≥ 0.1 and CI ≠ 0, then it is determined that the single-layer hierarchy does not conform to the consistency principle and the judgment matrix does not have satisfactory consistency, and adjust the judgment matrix, including:

[0032] 1) Identify inconsistent elements: By re-analyzing the eigenvector W and the maximum eigenvalue λ max , identify the margin m of each element that causes inconsistency ij : where ω i and ω j are both components of the eigenvector, and a ij is the recognition judgment value;

[0033] 2) Adjust the judgment: Adjust the corresponding judgment value a ij according to the identified inconsistent elements. The adjustment principle is to ensure that the judgment matrix is closer to consistency;

[0034] 3) Recalculate the consistency ratio: After each adjustment, recalculate the maximum eigenvalue λmax and the random consistency ratio CR until CR < 0.1.

[0035] Preferably, calculate the relative importance weights of all index factors in each level for the overall goal layer, perform the overall layer sorting and consistency test, including:

[0036] 1) Calculate the value of the general consistency index CI after the overall layer sorting:

[0037] 2) Calculate the value of the average random consistency index RI after the overall layer sorting:

[0038] 3) Calculate the value of the random consistency ratio CR after the overall layer sorting: If CR < 0.1, it is determined that each level after the overall layer sorting conforms to the consistency principle and the corresponding judgment matrix has satisfactory consistency, and the weight vector W' corresponding to the judgment matrix can represent the corresponding index weights;

[0039] Conversely, if CR ≥ 0.1, it is determined that each level after the overall layer sorting does not conform to the consistency principle and the corresponding judgment matrix does not have satisfactory consistency, and the judgment matrix needs to be adjusted again.

[0040] Preferably, based on the group decision-making method, collect the index scoring data of multiple experts. First, calculate the maximum eigenvalue λ max and the eigenvector W according to the judgment matrix of each expert, then conduct a single-layer consistency test and an overall layer consistency test for each judgment matrix respectively. After normalizing each eigenvector W, obtain the weight vector W', then sort the weight vectors in descending order to obtain the sorting vector, and conduct a weighted arithmetic average process on the sorting vector to obtain the weight value of the corresponding index. Associate each evaluation index and the weight value of the corresponding index to construct an evaluation system for the effectiveness of industrial assistance. The specific steps of the weighted arithmetic average include:

[0041] 1) Determine the weight vector: Determine that the weight vector satisfies the non-negativity and normalization conditions, that is, the sum of all weights is 1.

[0042] 2) Sort the sequence numbers: Sort the original sequence numbers in descending order;

[0043] 3) Calculate the weighted average: According to the sorted sequence numbers and the weight vector corresponding to the sequence numbers, perform a weighted average calculation according to the calculation formula of the weighted arithmetic average OWA algorithm:

[0044] where a represents the original sequence number, b j represents the j-th element after sorting, and W j represents the corresponding weight vector.

[0045] (III) Beneficial effects

[0046] Compared with the prior art, the present invention provides a method for constructing an evaluation system for the effectiveness of targeted industrial assistance in the power grid, which has the following beneficial effects:

[0047] 1. The method for constructing the evaluation system for the effectiveness of targeted industrial assistance in the power grid provides specific evaluation methods and tools for the evaluation of the effectiveness of industrial assistance based on the theory of public policy benefit evaluation. It can assist in determining evaluation indicators and quantitative analysis of benefits. By integrating policy theory with the goal as the orientation and adopting a top-down and bottom-up collection system, it comprehensively measures the performance of industrial assistance projects in multiple dimensions such as economic benefits, industrial development, and social benefits, so as to discover the urban-rural gap and the effectiveness and problems of rural construction, and provide a scientific basis for policy adjustment and optimization.

[0048] 2. The method for constructing the evaluation system for the effectiveness of targeted industrial assistance in the power grid selects evaluation dimensions to break down the goals of complex decision-making in industrial assistance work into several levels of multiple indicators, then makes pairwise importance judgments on the elements of each level, and determines and correlates the weights of each indicator element to form a comprehensive evaluation combining qualitative and quantitative methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flowchart of the method for constructing the evaluation system for the effectiveness of industrial assistance of the present invention;

[0050] Figure 2 is a flowchart of setting indicators for the evaluation system by using the analytic hierarchy process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments and drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] Embodiment 1:

[0053] S1. Qualitative and quantitative analysis: Analyze the evaluation indicators based on the literature review and the research on the theory of public policy evaluation to determine the evaluation index system for the effectiveness of industrial assistance.

[0054] Further, study the evaluation dimensions of the evaluation indicators, including the economic benefit dimension, the industrial development dimension, and the social benefit dimension. According to the economic benefit dimension, select secondary indicators, including: 1) The growth situation of the village economy; 2) The growth situation of the assistance income; 3) The employment driving effect;

[0055] Select secondary indicators according to the dimension of industrial development, including: 1) The extension of the industrial chain; 2) The evaluation of the competitiveness of agricultural products; 3) The evaluation of innovation ability;

[0056] Select secondary indicators according to the dimension of social benefits, including: 1) The assistance effect; 2) The public service ability; 3) The situation of infrastructure construction; 4) The participation effect; 5) The human settlement environment;

[0057] Select tertiary indicators according to the dimension of economic benefits, including: 1) The growth of the village economy: the change rate of the total agricultural output value, the change rate of the total industrial output value, and the change rate of the total service output value; 2) The growth of the income from assistance: the growth rate of the per capita disposable income of rural residents and the growth rate of the income of the village collective economy; 3) The employment driving effect: the growth rate of the number of newly added employment positions, the growth rate of the employment of the permanent labor force, and the growth rate of the number of people receiving vocational skills training are used as evaluation indicators;

[0058] Select tertiary indicators according to the dimension of industrial development, including: 1) The extension of the industrial chain: the conversion rate of agricultural product processing, the growth rate of rural online retail sales, the change rate of the number of new agricultural business entities, the change rate of the number of rural tourism reception facilities, the change rate of the number of rural tourism reception person-times; 2) The evaluation of the competitiveness of agricultural products: the change rate of the number of agricultural brands, the change rate of the number of agricultural brand products included in the "Guangdong Brand" agricultural products; 3) The change rate of the number of cooperation projects with universities, scientific research institutions, etc., and the number of innovative achievements of the assisted industries;

[0059] Select tertiary indicators according to the dimension of social benefits, including: 1) The assistance effect: the change rate of the number of monitored households, the growth rate of the per capita income of monitored households; 2) The public service ability: the change rate of the number of cultural facilities, the change rate of the number of sports and fitness facilities, the coverage rate of elderly care service facilities, the number of electrification renovations of teaching equipment in rural schools; 3) The situation of infrastructure construction: the proportion of hardened roads connecting households in the village, the 5G network coverage rate, the change rate of the number of upgraded power infrastructure, the coverage rate of lighting on the main roads in the village, and the penetration rate of rural tap water; 4) The participation effect: the number of times of farmers' representatives participating in meetings and discussing matters, the satisfaction of farmers; 5) The human settlement environment: the area of rural renovation, the renovation rate of the walls of rural houses, the construction area of small village parks, the greening coverage rate of the village.

[0060] Specifically, the economic benefit indicators include three aspects: the growth of the village economy, the growth of the income from assistance, and the employment driving effect: 1) Agriculture, industry, and services are the main components of the village economy, and the changes in their total output values can directly present the effectiveness of industrial assistance in different industrial fields;

[0061] 2) The growth rate of the per capita disposable income of rural residents is a key indicator to measure the living standards of rural residents, reflecting the achievements of assistance in broadening farmers' employment and income channels. The growth rate of the village collective economic income focuses on reflecting the effective utilization degree of the village collective assets and resources by the assistance. If the village collective economic income increases significantly, it indicates that the assistance is effective in promoting the development of collective industries and revitalizing collective assets;

[0062] 3) The employment-driven effect plays a positive role in absorbing local labor force, which can improve the quality of workers, enhance employment adaptability and competitiveness;

[0063] The industrial development indicators include three aspects: the extension of the industrial chain, the competitiveness of agricultural products, and the innovation ability: 1) The processing and transformation of agricultural products can significantly increase the added value of agricultural products. The processing conversion rate of agricultural products is selected to measure the effectiveness of the assistance in promoting the development of the agricultural product processing industry;

[0064] The growth rate of rural online retail sales refers to the growth rate of the retail sales of goods realized through the Internet platform in rural areas, reflecting the transformation of the assistance-driven sales method of agricultural products towards modernization and increasing the sales channels of agricultural products;

[0065] The change rate of the number of new agricultural business entities reflects the trend of industrial development towards scale and intensification;

[0066] The change rate of the number of rural tourism reception facilities and the number of person-times focuses on showing the vitality of the new rural tourism business forms spawned by the integration of the primary, secondary, and tertiary industries and stimulating new consumption hotspots, reflecting the potential of industrial integration and the effectiveness of market expansion;

[0067] 2) The evaluation of the competitiveness of agricultural products starts from the dimension of brand building, which can focus on the overall brand promotion trend of regional agricultural products and reflect the process of the high-quality brand system absorbing local products, highlighting the effectiveness of the increase in the added value of agricultural products and the shaping of market recognition;

[0068] 3) The innovation ability includes two indicators: the number of innovation achievements of the assisted industries. The change rate of the number of cooperation projects between industrial assistance and universities, scientific research institutions, etc. can reflect the degree of docking of external scientific research resources, and the number of innovation achievements of the assisted industries can evaluate the actual output of endogenous innovation achievements under the assistance, so as to comprehensively evaluate the innovation vitality and technology transformation ability of the industry.

[0069] The social benefit indicators include five aspects: assistance effect, public service ability, infrastructure, participation effect, and human settlement environment: 1) By monitoring the measurement indicators of the assistance effect, accurately evaluate the effect of industrial assistance to ensure the stability of the assistance achievements;

[0070] 2) By quantifying the increase and decrease trend of the public service ability indicators, analyze the key supporting role played by the assisted industries in enriching rural public service resources;

[0071] 3) The infrastructure construction situation can comprehensively reflect the driving force and coverage of the assistance in promoting the iterative upgrade of rural hardware facilities;

[0072] 4) Participating in the effect evaluation can judge the degree of fit and implementation accuracy of the assistance projects with the actual needs of the countryside, and ensure the accurate implementation of the assistance measures;

[0073] 5) The area of improvement can directly measure the scale of the work on improving the environment in key rural areas; the renovation rate of rural housing walls reflects the effectiveness of the assistance in beautifying the rural living environment; the construction area of village small-scale parks and the green coverage rate of villages can both reflect the effectiveness of the assistance in improving the rural ecological environment quality and enhancing the greening level.

[0074] Example Two:

[0075] S2. Hierarchical classification: Establish a hierarchical structure model to divide the evaluation index system of assistance effectiveness into layers.

[0076] Furthermore, according to the secondary indicators and tertiary indicators corresponding to the evaluation dimensions, determine the evaluation index system of industrial assistance effectiveness. The steps for establishing the hierarchical structure model include: 1) Set the evaluation of industrial assistance effectiveness as the target layer A of the model; 2) Set the evaluation dimensions as the main criterion layer; 3) Set the secondary indicators corresponding to the evaluation dimensions as the sub-criterion layer B; 4) Set the tertiary indicators corresponding to the secondary indicators as the index layer C to form a hierarchical structure and construct the hierarchical structure model.

[0077] Specifically, the hierarchical classification is shown in the following table:

[0078]

[0079]

[0080]

[0081] Example Three:

[0082] S3. Index evaluation: Based on the scale scoring method, evaluate the importance of the indicators at each level of the model.

[0083] Furthermore, evaluating the importance of the indicators at each level of the model includes: randomly selecting two indicator factors for comparison in each level, selecting 1 to 9 as the scale values for importance judgment, and scoring the importance of the indicator factors through the scale scoring method;

[0084] Among them, the scale values 1, 3, 5, 7, and 9 respectively represent the increasing effect of the importance degree when one indicator factor is compared with another indicator factor, while the scale values 2, 4, 6, and 8 respectively represent the intermediate values for adjacent judgments between the scale values 1, 3, 5, 7, and 9.

[0085] Specifically, the meanings of the index scoring scales are shown in the following table:

[0086]

[0087] Example 4:

[0088] S4. Analytic Hierarchy Process: Construct a judgment matrix, calculate the maximum eigenvalue and eigenvector, conduct a single-layer consistency test after normalizing the eigenvector, and then conduct a total-layer consistency test after sorting the total hierarchy.

[0089] Furthermore, the steps for constructing the judgment matrix include:

[0090] 1) Set A to represent the matrix objective, and C i , C j (i, j = 1, 2, 3,..., n) to represent the matrix factors, and C ij (i, j = 1, 2, 3,..., n) to represent the index factors C in each hierarchy i For the index factor C j The numerical value of the relative importance, then the judgment matrix is expressed as:

[0091]

[0092] 2) According to P ω = λ max W, calculate the eigenvector W corresponding to the maximum eigenvalue λ max of the judgment matrix. According to the calculated eigenvector W, through obtain the weight distribution of each level of indicators, substitute the known data, and thus calculate the value of the maximum eigenvalue λ max ;

[0093] where (PW) i represents the i-th element;

[0094] 3) Normalize the eigenvector W according to the Z-score standardization method, including: where μ represents the mean of the eigenvector W, σ represents the standard deviation of the eigenvector W, and W' represents the weight vector obtained after normalization;

[0095] Conduct a single-layer consistency test on the judgment matrix. The consistency test formula includes: CR = CI / RI;

[0096] where CR represents the random consistency ratio of the judgment matrix, the standard value of RI is a constant, representing the average random consistency index of the judgment matrix, and CI represents the general consistency index of the judgment matrix, which is calculated by the following formula: CI = (λ max-(n) / (n - 1);

[0097] If the CR of the judgment matrix is < 0.1 or when λ max = n, such that CI = 0, then it is determined that the single-layer hierarchy conforms to the consistency principle and the corresponding judgment matrix has satisfactory consistency, and the weight vector W' corresponding to the judgment matrix can represent the weights of the corresponding indicators;

[0098] Conversely, if CR ≥ 0.1 and CI ≠ 0, then it is determined that the single-layer hierarchy does not conform to the consistency principle and the judgment matrix does not have satisfactory consistency, and the judgment matrix needs to be adjusted, including:

[0099] 1) Identify inconsistent elements: By re-analyzing the eigenvector W and the maximum eigenvalue λ max , identify the margin m of each element that causes inconsistency ij : where ω i and ω j are both components of the eigenvector, and a ij is the recognition judgment value;

[0100] 2) Adjust the judgment: Adjust the corresponding judgment value a ij according to the identified inconsistent elements, and the adjustment principle is to ensure that the judgment matrix is closer to consistency;

[0101] 3) Re-calculate the consistency ratio: After each adjustment, re-calculate the maximum eigenvalue λ max and the random consistency ratio CR until CR < 0.1;

[0102] Calculate the relative importance weights of all index factors in each hierarchy for the overall goal layer, perform the overall hierarchy sorting and conduct the consistency test, including:

[0103] 1) Calculate the value of the general consistency index CI after the overall hierarchy sorting:

[0104] 2) Calculate the value of the average random consistency index RI after the overall hierarchy sorting:

[0105] 3) Calculate the value of the random consistency ratio CR after the overall hierarchy sorting: If CR < 0.1, then it is determined that each hierarchy after the overall hierarchy sorting conforms to the consistency principle and the corresponding judgment matrix has satisfactory consistency, and the weight vector W' corresponding to the judgment matrix can represent the weights of the corresponding indicators;

[0106] Conversely, if CR ≥ 0.1, then it is determined that each hierarchy after the overall hierarchy sorting does not conform to the consistency principle and the corresponding judgment matrix does not have satisfactory consistency, and the judgment matrix needs to be re-adjusted.

[0107] Specifically, for the original judgment matrix constructed based on the scores given by each expert, calculate the maximum eigenvalue λ of each original judgment matrix max and the eigenvector W. After normalizing this eigenvector W, the weight vector is obtained, which can reflect the sorting weights of the factors at the same level relative to the factors at the upper level. Then, perform single-level sorting and consistency test and overall-level sorting consistency test respectively

[0108] Example 5:

[0109] S5. Index weight association: Based on the group decision-making method, collect the index scoring data of multiple experts, perform hierarchical analysis respectively to obtain the sorting vectors, perform weighted arithmetic average processing and associate each evaluation index with the corresponding index weight value to construct an evaluation system for the effectiveness of industrial assistance

[0110] Further, based on the group decision-making method, collect the index scoring data of multiple experts. First, calculate the maximum eigenvalue λ according to the judgment matrix of each expert max and the eigenvector W. Then, perform single-level consistency test and overall-level consistency test on each judgment matrix respectively. After normalizing each eigenvector W, the weight vector W' is obtained. Then, sort the weight vectors in descending order to obtain the sorting vector, and perform weighted arithmetic average processing on the sorting vector to obtain the weight value of the corresponding index. Associate each evaluation index with the corresponding index weight value to construct an evaluation system for the effectiveness of industrial assistance. The specific steps of the weighted arithmetic average include:

[0111] 1) Determine the weight vector: Determine that the weight vector satisfies the non-negativity and normalization conditions, that is, the sum of all weights is 1

[0112] 2) Sort the sequence numbers: Sort the original sequence numbers in descending order

[0113] 3) Calculate the weighted average: According to the sorted sequence numbers and the corresponding weight vectors, perform weighted average calculation according to the calculation formula of the weighted arithmetic average OWA algorithm:

[0114] where a represents the original sequence number, b j represents the jth element after sorting, and W j represents the corresponding weight vector

[0115] Specifically, the group decision-making method is to aggregate the decision-making data of multiple experts, comprehensively consider the decision-making information given by multiple experts, first calculate the judgment matrix of each expert, and then perform weighted arithmetic average on the sorting vectors of each expert to obtain the final index weights as shown in the following table:

[0116]

[0117]

[0118] The beneficial effects of the present invention are as follows: The construction method of the evaluation system for the effectiveness of the power grid's targeted industrial assistance provides specific evaluation methods and tools for the evaluation of the effectiveness of industrial assistance based on the theory of public policy benefit evaluation. It can assist in determining evaluation indicators and quantitative benefit analysis. By integrating policy theory with a goal orientation and adopting a top-down and bottom-up collection system, it comprehensively measures the performance of industrial assistance projects in multiple dimensions such as economic benefits, industrial development, and social benefits, thereby discovering the achievements and problems of the urban-rural gap and rural construction, providing a scientific basis for policy adjustment and optimization, and by selecting evaluation dimensions, the goals of complex decision-making in industrial assistance work are refined and decomposed into several levels of multiple indicators, and then pairwise importance judgments are made on the elements of each level, and the weights of each indicator element are determined and correlated to form a comprehensive evaluation combining qualitative and quantitative methods.

[0119] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a power grid fixed-point industry support effectiveness evaluation system, characterized in that: The following steps are involved: S1. Qualitative and quantitative analysis: Analyze the evaluation indicators based on literature review and public policy evaluation theory research, and determine the evaluation indicator system for industrial assistance effectiveness; S2. Grading and stratification: Establish a hierarchical structure model to divide the evaluation index system of assistance effectiveness into layers; S3. Indicator evaluation: The importance of indicators at each level of the model is evaluated based on the scale scoring method; S4. Hierarchical analysis: construct a judgment matrix, calculate the maximum eigenvalue and eigenvector, perform a single-layer consistency test after normalizing the eigenvector, and then perform a total-layer consistency test after sorting the total layers; S5. Indicator weight association: Based on the group decision-making method, the indicator scoring data of multiple experts are collected, and hierarchical analysis is performed to obtain the ranking vectors. The weighted arithmetic average is performed and the weight value of each evaluation indicator and the corresponding indicator is associated to build an industrial assistance effectiveness evaluation system.

2. The method for constructing a power grid fixed-point industry support effectiveness evaluation system according to claim 1 is characterized in that: The evaluation indicators are evaluated in terms of dimensions, including economic benefits, industrial development and social benefits. Secondary indicators are selected based on the economic benefits, including: 1) village economic growth; 2) assistance income growth; 3) employment driving effect; Secondary indicators are selected based on the industrial development dimensions, including: 1) industrial chain extension; 2) agricultural product competitiveness assessment; 3) innovation capability assessment; Secondary indicators are selected based on the social benefit dimensions, including: 1) assistance effect; 2) public service capacity; 3) infrastructure construction; 4) participation effect; 5) living environment.

3. The method for constructing a power grid fixed-point industry support effectiveness evaluation system according to claim 2 is characterized in that: According to the economic benefit dimension, three-level indicators are selected, including: 1) Village economic growth: the change rate of total agricultural output value, the change rate of total industrial output value, and the change rate of total service industry output value; 2) Assistance income growth: the growth rate of per capita disposable income of rural residents and the growth rate of village collective economic income; 3) Employment driving effect: the growth rate of the number of new jobs, the growth rate of permanent labor employment, and the growth rate of the number of people receiving vocational skills training are used as evaluation indicators; According to the industrial development dimension, three-level indicators are selected, including: 1) Industrial chain extension: agricultural product processing conversion rate, rural online retail sales growth rate, number of new agricultural business entities, number of rural tourism reception facilities, and number of rural tourism reception; 2) Agricultural product competitiveness assessment: number of agricultural brands, number of agricultural brand products included in the "Guangdong brand"; 3) Number of cooperation projects with universities, scientific research institutions, etc., and the number of innovation achievements in supporting industries; According to the social benefit dimension, three-level indicators are selected, including: 1) Assistance effect: change rate of the number of monitored households, growth rate of per capita income of monitored households; 2) Public service capacity: change rate of the number of cultural facilities, change rate of the number of sports and fitness facilities, coverage rate of elderly care service facilities, and number of electrification transformation of teaching equipment in rural schools; 3) Infrastructure construction status: proportion of hardened roads leading to households in the village, 5G network coverage rate, change rate of the number of power infrastructure upgrades, lighting coverage rate of main roads in the village, and rural tap water penetration rate; 4) Participation effect: number of times farmer representatives attended meetings and farmers' satisfaction; 5) Living environment: rural improvement area, farmhouse wall transformation rate, construction area of ​​small and micro parks in villages, and village greening coverage rate.

4. The method for constructing a power grid fixed-point industry support effectiveness evaluation system according to claim 3 is characterized in that: According to the secondary indicators and tertiary indicators corresponding to the evaluation dimensions, the industrial assistance effectiveness evaluation index system is determined. The steps of establishing the hierarchical model include: 1) setting the industrial assistance effectiveness evaluation as the model target layer A; 2) setting the evaluation dimensions as the main criterion layer; 3) setting the secondary indicators corresponding to the evaluation dimensions as the sub-criterion layer B; 4) setting the tertiary indicators corresponding to the secondary indicators as the indicator layer C, so as to form a hierarchical structure and construct a hierarchical model.

5. The method for constructing a power grid fixed-point industry support effectiveness evaluation system according to claim 1 is characterized in that: The importance of the indicators at each level of the model is evaluated, including: randomly selecting two indicator factors at each level for comparison, selecting 1 to 9 as the scale value for importance judgment, and scoring the importance of the indicator factors by the scale scoring method; The scale values ​​1, 3, 5, 7 and 9 represent the increasing importance of one indicator factor compared with another indicator factor, and the scale values ​​2, 4, 6 and 8 represent the intermediate values ​​between the adjacent judgments of the scale values ​​1, 3, 5, 7 and 9; The judgment matrix construction step includes: 1) Let A represent the matrix target, C i , C j (i, j = 1, 2, 3, ..., n) represents the matrix factors, C ij (i, j = 1, 2, 3, ..., n) represents the index factor C in each level i For index factor C j The value of relative importance, the judgment matrix is ​​expressed as: 2) According to P ω =λ max W, calculate and judge the maximum characteristic root λ of the matrix max The corresponding eigenvector W is obtained by Get the weight distribution of indicators at each level, bring in the known data, and calculate the maximum characteristic root λ max The value of in (PW) i Represents the i-th element; 3) The feature vector W is normalized according to the Z-score normalization method, including: Where μ represents the mean of the feature vector W, σ represents the standard deviation of the feature vector W, and W' represents the weight vector obtained after normalization.

6. The method for constructing a power grid fixed-point industry support effectiveness evaluation system according to claim 5 is characterized in that: A single-layer consistency test is performed on the judgment matrix, and the consistency test formula includes: CR = CI / RI; Where CR represents the random consistency ratio of the judgment matrix, the standard value of RI is a constant, which represents the average random consistency index of the judgment matrix, and CI represents the general consistency index of the judgment matrix, which is calculated by the following formula: CI = (λ max -n) / (n-1); If the CR of the judgment matrix is ​​less than 0.1 or when λ max =n, so that CI = 0, it is determined that the single-layer hierarchy meets the consistency principle and the corresponding judgment matrix has satisfactory consistency, and the weight vector W' corresponding to the judgment matrix can represent the corresponding indicator weight; On the contrary, if CR ≥ 0.1 and CI ≠ 0, it is determined that the single layer does not meet the consistency principle and the judgment matrix does not have satisfactory consistency, and the judgment matrix is ​​adjusted, including: 1) Identify inconsistent elements: by reanalyzing the eigenvector W and the largest eigenroot λ max , identify the margin m of each element that causes the inconsistency ij : where ω i and ω j are all components of the eigenvector, a ij It represents the recognition judgment value; 2) Adjust judgment: adjust the corresponding judgment value a according to the identified inconsistent elements ij ,The adjustment principle is to ensure that the judgment matrix is ​​closer to consistency; 3) Repeated calculation of consistency ratio: The maximum characteristic root λ needs to be recalculated after each adjustment max and random consistency ratio CR, until CR < 0.

1.

7. The method for constructing a power grid fixed-point industry support effectiveness evaluation system according to claim 1 is characterized in that: Calculate the relative importance weights of all indicator factors in each level to the overall target level, perform a total ranking of the levels and conduct a consistency test, including: 1) Calculate the value of the general consistency index CI after the total layer sorting: 2) Calculate the value of the average random consistency index RI after the total layer is sorted: 3) Calculate the value of the random consistency ratio CR after the total layer is sorted: If CR < 0.1, it is determined that each level after the total level is sorted complies with the consistency principle and the corresponding judgment matrix has satisfactory consistency. The weight vector W' corresponding to the judgment matrix can represent the corresponding indicator weight; On the contrary, if CR ≥ 0.1, it is determined that each level after the total level is sorted does not meet the consistency principle and the corresponding judgment matrix does not have satisfactory consistency, and the judgment matrix needs to be readjusted.

8. The method for constructing a power grid fixed-point industry support effectiveness evaluation system according to claim 1 is characterized in that: Based on the group decision-making method, the index scoring data of multiple experts are collected, and the maximum characteristic root λ is calculated according to the judgment matrix of each expert. max and eigenvector W, and then perform single-layer consistency test and total-layer consistency test on each judgment matrix respectively, and obtain weight vector W' after normalizing each eigenvector W, and then sort the weight vectors in descending order to obtain the sorting vector, and perform weighted arithmetic average on the sorting vector to obtain the weight value of the corresponding indicator, and associate each evaluation indicator with the weight value of the corresponding indicator to construct an industrial assistance effectiveness evaluation system, where the specific steps of weighted arithmetic average include: 1) Determine the weight vector: Determine that the weight vector satisfies the non-negative and normalized conditions, that is, the sum of all weights is 1. 2) Sorting sequence numbers: sort the original sequence numbers from large to small; 3) Calculate the weighted average: According to the sorted sequence number and the weight vector corresponding to the sequence number, the weighted average is calculated according to the calculation formula of the weighted arithmetic average (OWA) algorithm: Where a represents the original sequence number, b j represents the jth element after sorting, W j Represents the corresponding weight vector.

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