Power transmission and transformation project cost accounting method and system based on multi-factor fuzzy optimization

Abstract

The invention belongs to the technical field of power transmission and transformation projects, and particularly relates to a power transmission and transformation project cost accounting method and system based on multi-factor fuzzy optimization, and the method comprises the steps: firstly constructing a comprehensive evaluation index system for power transmission and transformation project settlement assessment, and then combining an improved analytic hierarchy process, an entropy evaluation method and a game theory; obtaining static combination weight values of each evaluation index at different moments, then obtaining a time weight vector of each evaluation index, and then weighting the time weight vector of each evaluation index and the static combination weight values at different moments to obtain a dynamic combination weight value of each evaluation index; and finally, performing fuzzy comprehensive evaluation based on the actual evaluation data of each evaluation index and the dynamic combination weight value, and finally obtaining a power transmission and transformation project settlement assessment evaluation result. According to the method, assessment key points in different periods can be reflected, dynamic comprehensive assessment is realized, and more accurate and comprehensive power transmission and transformation project settlement assessment can be realized by combining the advantages of the improved analytic hierarchy process and the entropy evaluation method.

Description

Technical Field

[0001] The present invention belongs to the technical field of power transmission and transformation engineering, and specifically relates to a power transmission and transformation engineering cost accounting method and system based on multi-factor fuzzy optimization. Background Art

[0002] In the field of power transmission and transformation engineering, project evaluation is an important link to ensure engineering quality, economic benefits and social benefits. With the rapid development of power systems, higher requirements are put forward for the settlement evaluation system of power transmission and transformation engineering. Traditional evaluation methods, such as single analytic hierarchy process or entropy method, can provide evaluation support to a certain extent, but there are some inherent limitations. The analytic hierarchy process (AHP) relies on the subjective judgment of experts to construct the judgment matrix and calculate the weights. When dealing with complex systems, this method may lead to deviations in the evaluation results due to the dominant role of subjective factors. As an objective weighting method, the entropy method determines the weight by analyzing the discrete degree of data, which can reduce the interference of subjective factors. However, the entropy method is too dependent on the distribution characteristics of data. When the data difference is not large, it may lead to unreasonable weight distribution and ignore the importance of expert experience. In addition, during the assessment stage, the same power transmission and transformation project may undergo multiple engineering assessments and evaluations. At different times, the assessment focus will change with the assessment stage. The traditional analytic hierarchy process or entropy method cannot reflect this feature. Therefore, there is an urgent need for a cost accounting method for power transmission and transformation projects that can combine the advantages of improved analytic hierarchy process and entropy method and can reflect the assessment focus in different periods. Summary of the invention

[0003] The purpose of the present invention is to provide a cost accounting method for power transmission and transformation projects based on multi-factor fuzzy optimization, which combines the advantages of improved hierarchical analysis method and entropy method, can reflect the assessment focus of different periods, and realize dynamic comprehensive evaluation, in order to solve the above problems existing in the prior art.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows:

[0005] In a first aspect, the present invention provides a method for calculating the cost of a power transmission and transformation project based on multi-factor fuzzy optimization, the method comprising:

[0006] S1. Construct a comprehensive evaluation index system for power transmission and transformation project settlement assessment according to the full life cycle of the power transmission and transformation project;

[0007] S2. Obtaining the static combined weight values ​​of each evaluation index in the comprehensive evaluation index system at different times; the method for obtaining the combined weight values ​​comprises: firstly, subjectively weighting each evaluation index in the comprehensive evaluation index system by using the improved analytic hierarchy process and expert opinions to obtain a first weight, and objectively weighting each evaluation index by using the entropy method to obtain a second weight; then, applying game theory to combine and weight the first weight and the second weight to obtain the static combined weight value of each evaluation index;

[0008] S3, obtaining the time weight vector of each evaluation indicator in the comprehensive evaluation indicator system; weighting the time weight vector of each evaluation indicator with the static combination weight value at different times to obtain the dynamic combination weight value of each evaluation indicator;

[0009] S4. Perform fuzzy comprehensive evaluation based on the actual evaluation data of each evaluation index and the dynamic combination weight value to obtain the evaluation result of the final settlement assessment of the transmission and transformation project.

[0010] The S3 includes:

[0011] S31. Obtain the time weight vector w of the evaluation index by solving the following nonlinear programming problem t =(w1, w2, ..., w T ):

[0012]

[0013] In the above formula, w t It represents the dynamic time weight of the evaluation index at time t; η represents the pre-set time importance; T represents the number of time periods;

[0014] S32, weighting the time weight vector of each evaluation index with the static combination weight value at different times according to the following formula to obtain the dynamic combination weight value of the evaluation index:

[0015]

[0016] In the above formula, w i represents the dynamic combined weight value of evaluation index i; w * i (t) represents the static combined weight value of evaluation index i at time t.

[0017] In S2, the subjective weighting using the improved analytic hierarchy process and expert opinions includes the following steps:

[0018] A1. Construct the relative importance matrix A through the scaling method:

[0019]

[0020]

[0021] In the above formula, A ij Indicates the relative importance between evaluation index i and evaluation index j; n is the total number of evaluation indicators;

[0022] A2. Calculate the judgment matrix B based on the relative importance matrix. The element b in the judgment matrix B ij The calculation formula is:

[0023]

[0024] r max =max(r i );

[0025] r min =min(r i );

[0026] k m =r max / r min ;

[0027] In the above formula, r i 、r j are the sum of the elements in the i-th row and the j-th row in the relative importance matrix A; r max 、r min All are intermediate calculations; k m is the importance ranking index;

[0028] A3, calculate the quasi-optimal consistency matrix B' of the judgment matrix B, the element b' of the quasi-optimal consistency matrix B' ij The calculation formula is:

[0029]

[0030] c ik =lgb ik ;c jk =lgb jk ;

[0031] In the above formula, c ik 、c jk All are transfer matrix elements;

[0032] A4. Calculate the first weight of each evaluation index:

[0033]

[0034] In the above formula, It represents the weight of evaluation index i obtained by subjective weighting using improved analytic hierarchy process; represents the elements in the normalized quasi-optimal consistency matrix B';

[0035] A5. Calculate the weight of the evaluation index obtained from expert opinions according to the following formula:

[0036]

[0037] In the above formula, w i expert represents the weight of evaluation index i obtained from expert opinions; w ij represents the weighted score of expert j on indicator i obtained by the Delphi method; E represents the total number of experts;

[0038] A6. According to the following formula With w i expert Fusion, get the first weight:

[0039]

[0040] In the above formula, Represents the first weight of evaluation index i.

[0041] The objective weighting using the entropy method comprises the following steps:

[0042] B1. After standardizing the original data of each evaluation index, the weight matrix R is calculated. The element r in the weight matrix R is ij The calculation formula is:

[0043]

[0044] In the above formula, x ij It represents the data obtained after the evaluation index i in the jth project is standardized; m is the number of projects;

[0045] B2. Calculate the information entropy value and information utility value of each evaluation index:

[0046]

[0047] d i =1-e i ;

[0048] In the above formula, e i represents the information entropy value of evaluation index i; d i It represents the information utility value of evaluation index i;

[0049] B3. Calculate the second weight of each evaluation index:

[0050]

[0051] In the above formula, Represents the second weight of the evaluation index i.

[0052] S2 also includes using sensitivity analysis to fine-tune the first weight and the second weight before combining and weighting the first weight obtained by subjective weighting and the second weight obtained by objective weighting using game theory; the sensitivity analysis includes:

[0053] Construct a linear regression model to evaluate the impact of each evaluation indicator on project success:

[0054]

[0055] In the above formula, Y is the degree of project success, represents the weight value of evaluation index i obtained by the lth method before fine-tuning using sensitivity analysis; X i is the actual data of evaluation index i, ∈ is the error term;

[0056] Calculated according to the following formula:

[0057]

[0058] δ=β×(S i -S threshold );

[0059]

[0060] In the above formula, represents the weight value of evaluation index i obtained by the lth method after fine-tuning using sensitivity analysis; S i represents the sensitivity coefficient of the evaluation index i; β represents the proportional constant; S threshold represents the preset sensitivity threshold;

[0061] The application of game theory to combine and weight the weight values ​​obtained by subjective weighting and the weight values ​​obtained by objective weighting includes:

[0062] C1. Solve the following decision model to obtain the method weight vector (a1, a2, ...a L ):

[0063]

[0064] In the above formula, a l represents the weight coefficient of the lth method;

[0065] C2. Calculate the static combination weight according to the following formula:

[0066]

[0067]

[0068] In the above formula, represents the normalized weight coefficient of the lth method; w * Represents the static combination weight of the evaluation indicators.

[0069] In a second aspect, the present invention provides a system for calculating the cost of power transmission and transformation projects based on multi-factor fuzzy optimization, the system comprising:

[0070] Evaluation index system construction module, used to construct a comprehensive evaluation index system for power transmission and transformation project settlement assessment according to the full life cycle of the power transmission and transformation project;

[0071] The static combination weight calculation module is used to obtain the static combination weight value of each evaluation index in the comprehensive evaluation index system at different times; the method for obtaining the combination weight value includes: firstly, subjectively weighting each evaluation index in the comprehensive evaluation index system by using the improved hierarchical analysis method and expert opinions to obtain a first weight, and objectively weighting each evaluation index by using the entropy method to obtain a second weight; then, applying game theory to combine and weight the first weight and the second weight to obtain the static combination weight value of each evaluation index;

[0072] The dynamic combination weight calculation module is used to obtain the time weight vector of each evaluation indicator in the comprehensive evaluation indicator system; the time weight vector of each evaluation indicator is weighted with the static combination weight value at different times to obtain the dynamic combination weight value of each evaluation indicator;

[0073] The fuzzy comprehensive evaluation module is used to perform fuzzy comprehensive evaluation based on the actual evaluation data of each evaluation index and the dynamic combination weight value to obtain the evaluation result of the final settlement assessment of the transmission and transformation project.

[0074] The dynamic combination weight calculation module obtains the time weight vector w of the evaluation index by solving the following nonlinear programming problem t =(w1, w2, ..., w T ):

[0075]

[0076] In the above formula, w t It represents the dynamic time weight of the evaluation index at time t; η represents the pre-set time importance; T represents the number of time periods;

[0077] The dynamic combination weight calculation module weights the time weight vector of each evaluation index with the static combination weight value at different times according to the following formula to obtain the dynamic combination weight value of the evaluation index:

[0078]

[0079] In the above formula, w i represents the dynamic combined weight value of evaluation index i; w * i (t) represents the static combined weight value of evaluation index i at time t.

[0080] The static combined weight calculation module performs subjective weighting according to the following steps:

[0081] A1. Construct the relative importance matrix A through the scaling method:

[0082]

[0083] In the above formula, A ij Indicates the relative importance between evaluation index i and evaluation index j; n is the total number of evaluation indicators;

[0084] A2. Calculate the judgment matrix B based on the relative importance matrix. The element b in the judgment matrix B ij The calculation formula is:

[0085]

[0086] r max =max(r i );

[0087] r min =min(r i );

[0088] k m =r max / r min ;

[0089] In the above formula, r i 、r j are the sum of the elements in the ith row and the jth row in the relative importance matrix A; rmax and rmin are both intermediate calculations; k m is the importance ranking index;

[0090] A3, calculate the quasi-optimal consistency matrix B' of the judgment matrix B, the element b' of the quasi-optimal consistency matrix B' ij The calculation formula is:

[0091]

[0092] c ik =lgb ik ;c jk =lgb jk ;

[0093] In the above formula, c ik 、c jk All are transfer matrix elements;

[0094] A4. Calculate the first weight of each evaluation index:

[0095]

[0096] In the above formula, It represents the weight of evaluation index i obtained by subjective weighting using improved analytic hierarchy process; represents the elements in the normalized quasi-optimal consistency matrix B';

[0097] A5. Calculate the weight of the evaluation index obtained from expert opinions according to the following formula:

[0098]

[0099] In the above formula, w i expert represents the weight of evaluation index i obtained from expert opinions; w ij represents the weighted score of expert j on indicator i obtained by the Delphi method; E represents the total number of experts;

[0100] A6. According to the following formula With w i expert Fusion, get the first weight:

[0101]

[0102] In the above formula, Represents the first weight of evaluation index i.

[0103] The static combination weight calculation module performs objective weighting according to the following steps:

[0104] B1. After standardizing the original data of each evaluation index, the weight matrix R is calculated. The element r in the weight matrix R is ij The calculation formula is:

[0105]

[0106] In the above formula, x ij It represents the data obtained after the evaluation index i in the jth project is standardized; m is the number of projects;

[0107] B2. Calculate the information entropy value and information utility value of each evaluation index:

[0108]

[0109] d i =1-e i ;

[0110] In the above formula, e i represents the information entropy value of evaluation index i; d i It represents the information utility value of evaluation index i;

[0111] B3. Calculate the second weight of each evaluation index:

[0112]

[0113] In the above formula, Represents the second weight of the evaluation index i.

[0114] The static combined weight calculation module is used to use sensitivity analysis to fine-tune the first weight and the second weight before combining and weighting the first weight obtained by subjective weighting and the second weight obtained by objective weighting using game theory; the sensitivity analysis includes:

[0115] Construct a linear regression model to evaluate the impact of each evaluation indicator on project success:

[0116]

[0117] In the above formula, Y is the degree of project success, represents the weight value of evaluation index i obtained by the lth method before fine-tuning using sensitivity analysis; X i is the actual data of evaluation index i, ∈ is the error term;

[0118] Calculated according to the following formula:

[0119]

[0120] δ=β×(S i -S threshold );

[0121]

[0122] In the above formula, represents the weight value of evaluation index i obtained by the lth method after fine-tuning using sensitivity analysis; S i represents the sensitivity coefficient of the evaluation index i; β represents the proportional constant; S threshold represents the preset sensitivity threshold;

[0123] The static combination weight calculation module is used to perform combination weighting according to the following steps:

[0124] C1. Solve the following decision model to obtain the method weight vector (a1, a2, ...a L ):

[0125]

[0126] In the above formula, a l represents the weight coefficient of the lth method;

[0127] C2. Calculate the static combination weight according to the following formula:

[0128]

[0129] In the above formula, represents the normalized weight coefficient of the lth method; w * Represents the static combination weight of the evaluation indicators.

[0130] Compared with the prior art, the present invention has the following beneficial effects:

[0131] The cost accounting method for power transmission and transformation project based on multi-factor fuzzy optimization of the present invention first constructs a comprehensive evaluation index system for power transmission and transformation project settlement assessment according to the full life cycle of the power transmission and transformation project, then obtains the static combination weight value of each evaluation index in the comprehensive evaluation index system at different times, and then obtains the time weight vector of each evaluation index in the comprehensive evaluation index system, and then weights the time weight vector of each evaluation index with the static combination weight value at different times to obtain the dynamic combination weight value of each evaluation index, and finally performs fuzzy comprehensive evaluation based on the actual evaluation data of each evaluation index and the dynamic combination weight value to obtain the evaluation result of the final power transmission and transformation project settlement assessment; the above design takes into account the evaluation of each evaluation index at different times. The change of the target weight can reflect the assessment focus in different periods and realize dynamic comprehensive evaluation; the method for obtaining the combined weight value includes: firstly, subjectively weighting each evaluation index in the comprehensive evaluation index system using the improved analytic hierarchy process and expert opinions to obtain the first weight, and objectively weighting using the entropy method to obtain the second weight; then applying game theory to combine the first weight and the second weight to obtain the static combined weight value of each evaluation index; the above design combines the improved analytic hierarchy process, the entropy method and game theory, and realizes the balance of subjective and objective weights through the idea of ​​game theory, overcomes the shortcomings of a single weighting method, and thus is more in line with the diversity and complexity of actual projects, and obtains a more comprehensive, scientific and accurate evaluation result. Therefore, the present invention can not only reflect the assessment focus in different periods and realize dynamic comprehensive evaluation, but also can combine the advantages of the improved analytic hierarchy process and the entropy method to realize a more accurate and comprehensive settlement evaluation of power transmission and transformation projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] Figure 1The present invention provides a flow chart of the method for calculating the cost of power transmission and transformation projects based on multi-factor fuzzy optimization.

[0133] Figure 2 This is a structural block diagram of the power transmission and transformation project cost accounting system based on multi-factor fuzzy optimization according to the present invention. DETAILED DESCRIPTION

[0134] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0135] Embodiment 1:

[0136] See also Figure 1 , a cost accounting method for power transmission and transformation projects based on multi-factor fuzzy optimization, specifically follows the following steps:

[0137] S1. Based on the concept of the full life cycle of the project, the construction process of the power transmission and transformation project is divided into three main periods: the initial preparation stage, the construction stage and the later operation stage. Based on this, a four-level comprehensive evaluation index system is constructed for the settlement and assessment of the power transmission and transformation project;

[0138] The comprehensive evaluation index system includes the following first-level indicators: initial preparation, construction, and later operation; the initial preparation includes the following second-level indicators: decision-making stage and preparation stage; construction includes the following second-level indicators: risk management, data management, construction management, and supervision level; later operation includes the following second-level indicators: process evaluation, technical evaluation, and benefit evaluation; the decision-making stage includes the following third-level indicators: initial decision-making level and risk analysis; the preparation stage includes the following third-level indicators: bidding and preparation for construction; the risk management includes the following third-level indicators: natural and external risks, economic risks, and technical risks; the data management includes the following third-level indicators: archive management and contract management; Construction management includes the following three-level indicators: quality management, safety management; the supervision level includes the following three-level indicators: supervision documents, supervision quality; the process evaluation includes the following three-level indicators: engineering quality evaluation, safety evaluation; the technical evaluation includes the following three-level indicators: technical evaluation, production and operation indicators; the benefit evaluation includes the following three-level indicators: economic benefits, environmental benefits, and social benefits; the initial decision-making level includes the following four-level indicators: scientific decision-making, applicability of decision-making, and rationality of decision-making; the risk analysis includes the following four-level indicators: risk identification and assessment, and risk response strategies; the bidding includes the following four-level indicators: contract standardization, scientific effectiveness of bidding, and completeness of information; The preparation for construction includes the following four indicators: funds, staff, materials, and logistics; the natural and external risks include the following four indicators: climate impact, geographical impact; the economic risks include the following four indicators: price adjustment, interest rate change, and cost level control; the technical risks include the following four indicators: construction design level, design unit level; the file management includes the following four indicators: file rationality, file integrity; the contract management includes the following four indicators: contract filing status, contract management system; the quality management includes the following four indicators: quality supervision measures, quality control; the safety management includes the following four indicators: safety management organization, safety management measures; The supervision documents include the following four-level indicators: technical document review, supervision quality report; the supervision quality includes the following four-level indicators: supervision approval, supervision strength; the project quality evaluation includes the following four-level indicators: project quality record; the safety evaluation includes the following four-level indicators: safe construction, safe production, and safe management; the technical evaluation includes the following four-level indicators: substation, conductor, ground wire, insulation technology, and pole tower; the production and operation indicators include the following four-level indicators: production timing, power supply capacity, and voltage quality; the economic benefits include the following four-level indicators: economic benefits; the environmental benefits include the following four-level indicators: environmental benefits; the social benefits include the following four-level indicators: social benefits;

[0139] S2. Obtaining the static combined weight values ​​of each evaluation index in the comprehensive evaluation index system at different times; the method for obtaining the combined weight values ​​comprises: firstly, subjectively weighting each evaluation index in the comprehensive evaluation index system by using the improved analytic hierarchy process and expert opinions to obtain a first weight, and objectively weighting each evaluation index by using the entropy method to obtain a second weight; then, applying game theory to combine and weight the first weight and the second weight to obtain the static combined weight value of each evaluation index;

[0140] The subjective weighting using improved analytic hierarchy process and expert opinions includes the following steps:

[0141] A1. Construct the relative importance matrix A through the scaling method:

[0142]

[0143] In the above formula, A ij Indicates the relative importance between evaluation index i and evaluation index j; n is the total number of evaluation indicators;

[0144] A2. Calculate the judgment matrix B based on the relative importance matrix. The element b in the judgment matrix B ij The calculation formula is:

[0145]

[0146] r max =max(r i );

[0147] r min =min(r i );

[0148] k m =r max / r min ;

[0149] In the above formula, r i 、r j are the sum of the elements in the ith row and the jth row in the relative importance matrix A; rmax and rmin are both intermediate calculations; k m is the importance ranking index;

[0150] A3, calculate the quasi-optimal consistency matrix B' of the judgment matrix B, the element b' of the quasi-optimal consistency matrix B' ij The calculation formula is:

[0151]

[0152] c ik =lgb ik ;cjk =lgb jk ;

[0153] In the above formula, c ik 、c jk All are transfer matrix elements;

[0154] A4. Calculate the first weight of each evaluation index:

[0155]

[0156] In the above formula, It represents the weight of evaluation index i obtained by subjective weighting using improved analytic hierarchy process; represents the elements in the normalized quasi-optimal consistency matrix B';

[0157] A5. Calculate the weight of the evaluation index obtained from expert opinions according to the following formula:

[0158]

[0159] In the above formula, w i expert represents the weight of evaluation index i obtained from expert opinions; w ij represents the weighted score of expert j on indicator i obtained by the Delphi method; E represents the total number of experts;

[0160] A6. According to the following formula With w i expert Fusion, get the first weight:

[0161]

[0162] In the above formula, represents the first weight of evaluation index i;

[0163] A7. Perform consistency check on the first weight after fusion to ensure the rationality and scientificity of weight allocation;

[0164] The objective weighting using the entropy method comprises the following steps:

[0165] B1. After standardizing the original data of each evaluation index, the weight matrix R is calculated. The element r in the weight matrix R is ij The calculation formula is:

[0166]

[0167] In the above formula, x ij It represents the data obtained after the evaluation index i in the jth project is standardized; m is the number of projects;

[0168] B2. Calculate the information entropy value and information utility value of each evaluation index:

[0169]

[0170] d i =1-e i ;

[0171] In the above formula, e i represents the information entropy value of evaluation index i; d i It represents the information utility value of evaluation index i;

[0172] B3. Calculate the second weight of each evaluation index:

[0173]

[0174] In the above formula, represents the second weight of evaluation index i;

[0175] B4. Introduce a verification process to enhance the accuracy of objective weighting. Specifically, use statistical test methods, such as chi-square test or Pearson correlation coefficient, to directly compare the second weight obtained by the entropy method with the results of other objective weighting methods. When the statistical test shows a significant difference, adjust the weight and fine-tune the weight based on the difference analysis results to ensure the objectivity and accuracy of the evaluation system.

[0176] In order to further optimize the weight allocation, before applying game theory to combine the first weight obtained by subjective weighting and the second weight obtained by objective weighting, sensitivity analysis is used to fine-tune the first weight and the second weight to ensure that the key indicators have more appropriate weights;

[0177] The sensitivity analysis included:

[0178] Construct a linear regression model to evaluate the impact of each evaluation indicator on project success:

[0179]

[0180] In the above formula, Y is the degree of project success, represents the weight value of evaluation index i obtained by the lth method before fine-tuning using sensitivity analysis, l = 1, 2; when l = 1 Indicates the first weight value before fine-tuning, when l = 2 represents the second weight value before fine-tuning; X i is the actual data of evaluation index i, ∈ is the error term;

[0181] Calculated according to the following formula:

[0182]

[0183] δ=β×(S i -S threshold );

[0184]

[0185] In the above formula, represents the weight value of evaluation index i obtained by the lth method after fine-tuning using sensitivity analysis; S i represents the sensitivity coefficient of the evaluation index i; β represents the proportional constant; S threshold represents the preset sensitivity threshold;

[0186] The method of applying game theory to combine and weight the weight values ​​obtained by subjective weighting and the weight values ​​obtained by objective weighting comprises the following steps:

[0187] C1. Solve the following decision model to obtain the method weight vector (a1, a2, ...a L ):

[0188]

[0189] In the above formula, a l represents the weight coefficient of the lth method;

[0190] C2. Calculate the static combination weight according to the following formula:

[0191]

[0192] In the above formula, represents the normalized weight coefficient of the lth method; w * Represents the static combination weight of the evaluation indicators;

[0193] S3, obtaining the time weight vector of each evaluation indicator in the comprehensive evaluation indicator system; weighting the time weight vector of each evaluation indicator with the static combination weight value at different times to obtain the dynamic combination weight value of each evaluation indicator; the dynamic combination weight value can better reflect the changes of the evaluation indicators in different time periods with the change of time periods;

[0194] S31. Obtain the time weight vector w of the evaluation index by solving the following nonlinear programming problem t =(w1, w2, ..., w T ):

[0195]

[0196]

[0197] In the above formula, w t It indicates the dynamic time weight of the evaluation index at time t; η indicates the pre-set time importance; T indicates the number of time periods; when η=0.1, it means that recent data is highly valued; when η=0.3, it means that recent data is more valued; when η=0.5, it means that data of all periods are equally important; when η=0.7, it means that long-term data is more important; when η=0.9, it means that long-term data is extremely valued; when η=0.2, 0.4, 0.6, 0.8, it means that it is in the middle of two adjacent judgments;

[0198] S32, weighting the time weight vector of each evaluation index with the static combination weight value at different times according to the following formula to obtain the dynamic combination weight value of the evaluation index:

[0199]

[0200] In the above formula, w i represents the dynamic combined weight value of evaluation index i; w * i (t) represents the static combined weight value of evaluation index i at time t;

[0201] S4. Perform fuzzy comprehensive evaluation based on the actual evaluation data of each evaluation index and the dynamic combination weight value to obtain the evaluation result of the final power transmission and transformation project settlement assessment;

[0202] The fuzzy comprehensive evaluation comprises the following steps:

[0203] S41, define a comment set, which is a form of expression of the evaluation results, and converts the qualitative description into a processable form. In this embodiment, the comment set includes evaluation levels of "excellent", "good", "medium", "poor", and "very poor" to cover all possible results of the settlement assessment and evaluation of the power transmission and transformation project; then, collect the actual evaluation data of each evaluation indicator, where the actual evaluation data can come from actual measurement, historical records or other relevant data sources;

[0204] S42, converting the actual data into a membership value corresponding to the comment set through a membership function, and then constructing a fuzzy evaluation matrix based on the membership value of each evaluation index and a dynamic combination weight;

[0205] S43 determines the operator of fuzzy comprehensive evaluation, and commonly used fuzzy operators include fuzzy weighted average operator, fuzzy weighted geometric average operator, etc.; uses fuzzy operator and fuzzy evaluation matrix to calculate and obtain the comprehensive membership value of the project;

[0206] S44. Use the maximum membership principle to defuzzify the comprehensive membership value and obtain the final evaluation result of the power transmission and transformation project settlement assessment.

[0207] Embodiment 2:

[0208] See also Figure 2 , a power transmission and transformation project cost accounting system based on multi-factor fuzzy optimization, including an evaluation index system construction module, a static combination weight calculation module, and a dynamic combination weight calculation module; the evaluation index system construction module is used to construct a comprehensive evaluation index system for power transmission and transformation project settlement assessment according to the full cycle life of the power transmission and transformation project; the static combination weight calculation module is used to obtain the static combination weight value of each evaluation index in the comprehensive evaluation index system at different times; the method for obtaining the combination weight value includes: firstly, for each evaluation index in the comprehensive evaluation index system, subjectively weighting is performed using the improved hierarchical analysis method and expert opinions to obtain a first weight, and objectively weighting is performed using the entropy method to obtain a second weight; then, the first weight and the second weight are fine-tuned using sensitivity analysis; and then, the first weight and the second weight are combined and weighted using game theory to obtain the static combination weight value of each evaluation index;

[0209] Specifically, the static combination weight calculation module performs subjective weighting according to the following steps:

[0210] A1. Construct the relative importance matrix A through the scaling method:

[0211]

[0212] In the above formula, A ij Indicates the relative importance between evaluation index i and evaluation index j; n is the total number of evaluation indicators;

[0213] A2. Calculate the judgment matrix B based on the relative importance matrix. The element b in the judgment matrix B ij The calculation formula is:

[0214]

[0215] r max =max(r i );

[0216] r mim =min(r i );

[0217] k m =r max / r min ;

[0218] In the above formula, r i 、rj are the sum of the elements in the i-th row and the j-th row in the relative importance matrix A; r max 、r min All are intermediate calculations; k m is the importance ranking index;

[0219] A3, calculate the quasi-optimal consistency matrix B' of the judgment matrix B, the element b' of the quasi-optimal consistency matrix B' ij The calculation formula is:

[0220]

[0221] c ik =lgb ik ;c jk =lgb jk ;

[0222] In the above formula, c ik 、c jk All are transfer matrix elements;

[0223] A4. Calculate the first weight of each evaluation index:

[0224]

[0225] In the above formula, It represents the weight of evaluation index i obtained by subjective weighting using improved analytic hierarchy process; represents the elements in the normalized quasi-optimal consistency matrix B';

[0226] A5. Calculate the weight of the evaluation index obtained from expert opinions according to the following formula:

[0227]

[0228] In the above formula, w i expert represents the weight of evaluation index i obtained from expert opinions; w ij represents the weighted score of expert j on indicator i obtained by the Delphi method; E represents the total number of experts;

[0229] A6. According to the following formula With w i expert Fusion, get the first weight:

[0230]

[0231] In the above formula, represents the first weight of evaluation index i;

[0232] Specifically, the static combination weight calculation module performs objective weighting according to the following steps:

[0233] B1. After standardizing the original data of each evaluation index, the weight matrix R is calculated. The element r in the weight matrix R is ij The calculation formula is:

[0234]

[0235] In the above formula, x ij It represents the data obtained after the evaluation index i in the jth project is standardized; m is the number of projects;

[0236] B2. Calculate the information entropy value and information utility value of each evaluation index:

[0237]

[0238] d i =1-e i ;

[0239] In the above formula, e i represents the information entropy value of evaluation index i; d i It represents the information utility value of evaluation index i;

[0240] B3. Calculate the second weight of each evaluation index:

[0241]

[0242] In the above formula, represents the second weight of evaluation index i;

[0243] The sensitivity analysis includes the following steps:

[0244] Construct a linear regression model to evaluate the impact of each evaluation indicator on project success:

[0245]

[0246] In the above formula, Y is the degree of project success, represents the weight value of evaluation index i obtained by the lth method before fine-tuning using sensitivity analysis, l = 1, 2, respectively represents the first weight value and the second weight value; X i is the actual data of evaluation index i, ∈ is the error term;

[0247] Calculated according to the following formula:

[0248]

[0249] δ=β×(S i-S threshold );

[0250]

[0251] In the above formula, represents the weight value of evaluation index i obtained by the lth method after fine-tuning using sensitivity analysis; S i represents the sensitivity coefficient of the evaluation index i; β represents the proportional constant; S threshold represents the preset sensitivity threshold;

[0252] The static combination weight calculation module is used to perform combination weighting according to the following steps:

[0253] C1. Solve the following decision model to obtain the method weight vector (a1, a2, ...a L ):

[0254]

[0255] In the above formula, a l represents the weight coefficient of the lth method;

[0256] C2. Calculate the static combination weight according to the following formula:

[0257]

[0258] In the above formula, represents the normalized weight coefficient of the lth method; w * Represents the static combination weight of the evaluation indicators;

[0259] The dynamic combination weight calculation module is used to obtain the time weight vector of each evaluation indicator in the comprehensive evaluation indicator system; the time weight vector of each evaluation indicator is weighted with the static combination weight value at different times to obtain the dynamic combination weight value of each evaluation indicator;

[0260] Specifically, the dynamic combination weight calculation module obtains the time weight vector w of the evaluation index by solving the following nonlinear programming problem: t =(w1, w2, ..., w T ):

[0261]

[0262] In the above formula, w t It represents the dynamic time weight of the evaluation index at time t; η represents the pre-set time importance; T represents the number of time periods;

[0263] Specifically, the dynamic combination weight calculation module weights the time weight vector of each evaluation index with the static combination weight value at different times according to the following formula to obtain the dynamic combination weight value of the evaluation index:

[0264]

[0265] In the above formula, w i represents the dynamic combined weight value of evaluation index i; w * i (t) represents the static combined weight value of evaluation index i at time t;

[0266] The fuzzy comprehensive evaluation module is used to perform fuzzy comprehensive evaluation based on the actual evaluation data of each evaluation index and the dynamic combination weight value to obtain the evaluation result of the final settlement assessment of the power transmission and transformation project.

[0267] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0268] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0269] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0270] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0271] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A cost accounting method for power transmission and transformation projects based on multi-factor fuzzy optimization, characterized by: The method comprises: S1. Construct a comprehensive evaluation index system for power transmission and transformation project settlement assessment according to the full life cycle of the power transmission and transformation project; S2. Obtaining the static combined weight values ​​of each evaluation index in the comprehensive evaluation index system at different times; the method for obtaining the combined weight values ​​comprises: firstly, subjectively weighting each evaluation index in the comprehensive evaluation index system by using the improved analytic hierarchy process and expert opinions to obtain a first weight, and objectively weighting each evaluation index by using the entropy method to obtain a second weight; then, applying game theory to combine and weight the first weight and the second weight to obtain the static combined weight value of each evaluation index; S3, obtaining the time weight vector of each evaluation indicator in the comprehensive evaluation indicator system; weighting the time weight vector of each evaluation indicator with the static combination weight value at different times to obtain the dynamic combination weight value of each evaluation indicator; S4. Perform fuzzy comprehensive evaluation based on the actual evaluation data of each evaluation index and the dynamic combination weight value to obtain the evaluation result of the final settlement assessment of the transmission and transformation project.

2. The cost accounting method for power transmission and transformation engineering based on multi-factor fuzzy optimization according to claim 1 is characterized by: The S3 includes: S31. Obtain the time weight vector w of the evaluation index by solving the following nonlinear programming problem t =(w1, w2, ..., w T ): In the above formula, w t It represents the dynamic time weight of the evaluation index at time t; η represents the pre-set time importance; T represents the number of time periods; S32, weighting the time weight vector of each evaluation index with the static combination weight value at different times according to the following formula to obtain the dynamic combination weight value of the evaluation index: In the above formula, w i represents the dynamic combined weight value of evaluation index i; w * i (t) represents the static combined weight value of evaluation index i at time t.

3. The method for calculating the cost of power transmission and transformation projects based on multi-factor fuzzy optimization according to claim 1 or 2, characterized in that: In S2, the subjective weighting using the improved analytic hierarchy process and expert opinions includes the following steps: A1. Construct the relative importance matrix A through the scaling method: In the above formula, A ij Indicates the relative importance between evaluation index i and evaluation index j; n is the total number of evaluation indicators; A2. Based on the relative importance matrix, a judgment matrix B is obtained. The element b in the judgment matrix B ij The calculation formula is: r max =max(r i ); r min =min(r i ); k m ＝r max / r min ； In the above formula, r i 、r j are the sum of the elements in the i-th row and the j-th row in the relative importance matrix A; r max 、r min All are intermediate calculations; k m is the importance ranking index; A3, calculate the quasi-optimal consistency matrix B' of the judgment matrix B, the element b of the quasi-optimal consistency matrix B' ij The calculation formula is: c ik =lgb ik ;c jk =lgb jk ; In the above formula, c ik 、c jk All are transfer matrix elements; A4. Calculate the first weight of each evaluation index: In the above formula, It represents the weight of evaluation index i obtained by subjective weighting using improved analytic hierarchy process; represents the elements in the normalized quasi-optimal consistency matrix B′; A5. Calculate the weight of the evaluation index obtained from expert opinions according to the following formula: In the above formula, w i expert represents the weight of evaluation index i obtained from expert opinions; w jj represents the weighted score of expert j on indicator i obtained by the Delphi method; E represents the total number of experts; A6. According to the following formula With w i expert Fusion, get the first weight: In the above formula, Represents the first weight of evaluation index i.

4. The method for calculating the cost of power transmission and transformation projects based on multi-factor fuzzy optimization according to claim 1 or 2, characterized in that: The objective weighting using the entropy method comprises the following steps: B1. After standardizing the original data of each evaluation index, the weight matrix R is calculated. The element r in the weight matrix R is ij The calculation formula is: In the above formula, x ij It represents the data obtained after the evaluation index i in the jth project is standardized; m is the number of projects; B2. Calculate the information entropy value and information utility value of each evaluation index: d j =1-e i ; In the above formula, e i represents the information entropy value of evaluation index i; d i It represents the information utility value of evaluation index i; B3. Calculate the second weight of each evaluation index: In the above formula, Represents the second weight of the evaluation index i.

5. The method for calculating the cost of power transmission and transformation projects based on multi-factor fuzzy optimization according to claim 1 or 2, characterized in that: S2 also includes using sensitivity analysis to fine-tune the first weight and the second weight before combining and weighting the first weight obtained by subjective weighting and the second weight obtained by objective weighting using game theory; the sensitivity analysis includes: Construct a linear regression model to evaluate the impact of each evaluation indicator on project success: In the above formula, Y is the degree of project success, represents the weight value of evaluation index i obtained by the ,th method before fine-tuning using sensitivity analysis; X i is the actual data of evaluation index i, ∈ is the error term; Calculated according to the following formula: δ=β×(S i -S threshold ); In the above formula, represents the weight value of evaluation index i obtained by the lth method after fine-tuning using sensitivity analysis; S i represents the sensitivity coefficient of the evaluation index i; β represents the proportional constant; S threshold represents the preset sensitivity threshold; The application of game theory to combine and weight the weight values ​​obtained by subjective weighting and the weight values ​​obtained by objective weighting includes: C1. Solve the following decision model to obtain the method weight vector (a1, a2, ...a L ): In the above formula, a l represents the weight coefficient of the lth method; C2. Calculate the static combination weight according to the following formula: In the above formula, represents the normalized weight coefficient of the th method; w * Represents the static combination weight of the evaluation indicators.

6. The cost accounting system for power transmission and transformation projects based on multi-factor fuzzy optimization is characterized by: The system comprises: Evaluation index system construction module, used to construct a comprehensive evaluation index system for power transmission and transformation project settlement assessment according to the full life cycle of the power transmission and transformation project; The static combination weight calculation module is used to obtain the static combination weight value of each evaluation index in the comprehensive evaluation index system at different times; the method for obtaining the combination weight value includes: firstly, subjectively weighting each evaluation index in the comprehensive evaluation index system by using the improved hierarchical analysis method and expert opinions to obtain a first weight, and objectively weighting each evaluation index by using the entropy method to obtain a second weight; then, applying game theory to combine and weight the first weight and the second weight to obtain the static combination weight value of each evaluation index; The dynamic combination weight calculation module is used to obtain the time weight vector of each evaluation indicator in the comprehensive evaluation indicator system; the time weight vector of each evaluation indicator is weighted with the static combination weight value at different times to obtain the dynamic combination weight value of each evaluation indicator; The fuzzy comprehensive evaluation module is used to perform fuzzy comprehensive evaluation based on the actual evaluation data of each evaluation index and the dynamic combination weight value to obtain the evaluation result of the final settlement assessment of the transmission and transformation project.

7. The power transmission and transformation project cost accounting system based on multi-factor fuzzy optimization according to claim 6 is characterized by: The dynamic combination weight calculation module obtains the time weight vector w of the evaluation index by solving the following nonlinear programming problem t =(w1, w2, ..., w T ): In the above formula, w t It represents the dynamic time weight of the evaluation index at time t; η represents the pre-set time importance; T represents the number of time periods; The dynamic combination weight calculation module weights the time weight vector of each evaluation index with the static combination weight value at different times according to the following formula to obtain the dynamic combination weight value of the evaluation index: In the above formula, w i represents the dynamic combined weight value of evaluation index i; w * i (t) represents the static combined weight value of evaluation index i at time t.

8. The power transmission and transformation project cost accounting system based on multi-factor fuzzy optimization according to claim 6 or 7, characterized in that: The static combined weight calculation module performs subjective weighting according to the following steps: A1. Construct the relative importance matrix A through the scaling method: In the above formula, A ij Indicates the relative importance between evaluation index i and evaluation index j; n is the total number of evaluation indicators; A2. Based on the relative importance matrix, a judgment matrix B is obtained. The element b in the judgment matrix B ij The calculation formula is: r max =max(r i ); r min =min(r i ); k m ＝r max / r min ； In the above formula, r i 、r j are the sum of the elements in the i-th row and the j-th row in the relative importance matrix A; r max 、r min All are intermediate calculations; k m is the importance ranking index; A3, calculate the quasi-optimal consistency matrix B' of the judgment matrix B, the element b of the quasi-optimal consistency matrix B' ij The calculation formula is: c ik =lgb ik ;c jk =lgb jk ; In the above formula, c ik 、c jk All are transfer matrix elements; A4. Calculate the first weight of each evaluation index: In the above formula, It represents the weight of evaluation index i obtained by subjective weighting using improved analytic hierarchy process; represents the elements in the normalized quasi-optimal consistency matrix B'; A5. Calculate the weight of the evaluation index obtained from expert opinions according to the following formula: In the above formula, w i expert represents the weight of evaluation index i obtained from expert opinions; w ij represents the weighted score of expert j on indicator i obtained by the Delphi method; E represents the total number of experts; A6. According to the following formula With w i expert Fusion, get the first weight: In the above formula, Represents the first weight of evaluation index i.

9. The power transmission and transformation project cost accounting system based on multi-factor fuzzy optimization according to claim 6 or 7, characterized in that: The static combination weight calculation module performs objective weighting according to the following steps: B1. After standardizing the original data of each evaluation index, the weight matrix R is calculated. The element r in the weight matrix R is ij The calculation formula is: In the above formula, x ij It represents the data obtained after the evaluation index i in the jth project is standardized; m is the number of projects; B2. Calculate the information entropy value and information utility value of each evaluation index: d i =1-e i ; In the above formula, e i represents the information entropy value of evaluation index i; d i It represents the information utility value of evaluation index i; B3. Calculate the second weight of each evaluation index: In the above formula, Represents the second weight of the evaluation index i.

10. The power transmission and transformation project cost accounting system based on multi-factor fuzzy optimization according to claim 6 or 7, characterized in that: The static combined weight calculation module is used to use sensitivity analysis to fine-tune the first weight and the second weight before combining and weighting the first weight obtained by subjective weighting and the second weight obtained by objective weighting using game theory; the sensitivity analysis includes: Construct a linear regression model to evaluate the impact of each evaluation indicator on project success: In the above formula, Y is the degree of project success, represents the weight value of evaluation index i obtained by the ,th method; X i is the actual data of evaluation index i, ∈ is the error term; Calculated according to the following formula: δ=β×(S i -S threshold ); In the above formula, represents the weight value of evaluation index i obtained by the lth method after fine-tuning using sensitivity analysis; S i represents the sensitivity coefficient of the evaluation index i; β represents the proportional constant; S threshold represents the preset sensitivity threshold; The static combination weight calculation module is used to perform combination weighting according to the following steps: C1. Solve the following decision model to obtain the method weight vector (a1, a2, ...a L ): In the above formula, a l represents the weight coefficient of the lth method; C2. Calculate the static combination weight according to the following formula: In the above formula, represents the normalized weight coefficient of the th method; w * Represents the static combination weight of the evaluation indicators.

Images