A method and terminal for evaluating the whole life cycle of power grid infrastructure projects

Abstract

The present invention discloses a method and terminal for evaluating the entire life cycle of a power grid infrastructure project. The method establishes an evaluation system for the entire life cycle of a project including an operation and maintenance stage and an operation feedback stage, as well as a benchmark value of each indicator in the evaluation system, and obtains an index value of each indicator; obtains an AHP subjective weight of each indicator according to the evaluation system; obtains an objective weight of each indicator by using a pre-trained random forest algorithm; calculates a combined weight of each indicator according to the AHP subjective weight and the objective weight of each indicator; calculates an index comprehensive score of each indicator and a project comprehensive score for the entire life cycle of the project according to the index value, benchmark value and combined weight of each indicator; takes the operation and maintenance and operation feedback stages into consideration, is more comprehensive, overcomes the shortcomings of a single weighting method, effectively reduces the influence of a method that relies solely on subjective weights or a previous method for obtaining objective weights on a high degree of data dependence, and improves the reliability and accuracy of the evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid infrastructure, and in particular to a method and terminal for evaluating the entire life cycle of a power grid infrastructure project. Background Art

[0002] Scholars both domestically and internationally have conducted extensive and in-depth research on the theory of life cycle cost (LCC), proposing numerous models for estimating LCC. The concept of LCC was first coined in 1904 by the Swedish railway system, recognizing that railway construction required considering not only immediate construction costs but also future maintenance costs. By 1980, advanced countries had fully grasped the LCC theory, and various industries had enacted industry-specific regulations and practices incorporating LCC thinking. Extensive research and application of LCC thinking has been conducted in shipbuilding, aerospace, infrastructure, and equipment, resulting in a significant literature review. Construction companies have subsequently focused their attention on this advanced theory. Construction is a capital-intensive industry with low profit margins, so cost reduction remains a topic of ongoing exploration and research. The emergence of LCC has provided new insights for construction companies. Construction companies have conducted optimization studies on the LCC of their designs, tools, and related facilities, achieving significant success in project selection. However, relevant research in my country is still relatively lagging, particularly in terms of standards and data accumulation. As for the definition of life cycle cost (LCC), there is currently no generally accepted definition in academia in the strict sense. However, it can be concluded from domestic and foreign literature that the life cycle includes the feasibility study, preliminary design, commissioning and construction, operation and maintenance, overhaul and modification, and finally decommissioning of projects, facilities, and information systems. Therefore, LCC includes the direct and indirect costs incurred in all these stages.

[0003] By studying LCC, we can clarify the impact of costs at different stages, explore the quantitative and logical relationships between different expense items, and identify the cost transmission between expense entities. In short, from these patterns, relationships, and transmission, we can seek cost control measures to optimize and control costs throughout the entire life cycle. Professor Blanchard of the United States believes that the different stages of the life cycle form an interconnected system, and the costs of different stages are transmitted within the system. To control and optimize LCC, all links must be coordinated and unified, with clear goals. The optimization constraints must cover the entire life cycle. Otherwise, the constraints may be flawed, and the optimized LCC will lose its reference value.

[0004] In the field of power grid infrastructure projects, life cycle cost control theory takes a longer-term view, defining the research cycle as the entire lifespan. It disregards the cost of a particular phase and focuses on minimizing costs throughout the entire life cycle. Life cycle cost refers to the total cost of a project from design, construction, operation and maintenance to decommissioning. Due to the long construction period and substantial costs associated with power grid infrastructure projects, cost control during the construction period attracts significant attention from all parties. However, the subsequent costs of operation, maintenance, and overhaul often exceed the construction period costs by several times. However, this long-term expenditure on operation, maintenance, and overhaul is often overlooked by managers.

[0005] At the same time, many scholars use methods such as entropy weight method and coefficient of variation method to evaluate projects and determine objective weights based on the variability of indicators. However, this method cannot take into account the horizontal influence between indicators; it is highly dependent on samples, and as the modeling samples change, the weights will also change. Therefore, the use of methods such as entropy weight method often leads to weight distortion. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method and terminal for evaluating the entire life cycle of a power grid infrastructure project, taking into account the costs of subsequent operation and maintenance and making a more accurate assessment.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A method for evaluating the entire life cycle of a power grid infrastructure project comprises the following steps:

[0009] S1. Establish an evaluation system for the entire project life cycle, including the operation and maintenance phase and the operational feedback phase, as well as the benchmark values ​​of each indicator in the evaluation system, and obtain the indicator values ​​of each indicator;

[0010] S2. Obtain the AHP subjective weight of each indicator according to the evaluation system;

[0011] S3, obtain the objective weight of each indicator through the pre-trained random forest algorithm;

[0012] S4. Calculate the combined weight of each indicator based on the AHP subjective weight and the objective weight of each indicator;

[0013] S5. Calculate the comprehensive indicator score of each indicator and the comprehensive project score for the entire life cycle of the project based on the indicator value, benchmark value and combined weight of each indicator.

[0014] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0015] A terminal for the full life cycle evaluation of a power grid infrastructure project comprises a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for the full life cycle evaluation of a power grid infrastructure project are implemented.

[0016] The beneficial effects of the present invention are: a method and terminal for evaluating the entire life cycle of a power grid infrastructure project of the present invention establishes an evaluation system for the entire life cycle of the project taking into account the operation and maintenance stage and the operation feedback stage, which is more comprehensive, uses a random forest algorithm to obtain objective weights, uses an improved hierarchical analysis method to obtain subjective weights, and uses the subjective weights and objective weights to comprehensively determine the combined weights of each indicator. The combined weight determination strategy overcomes the shortcomings of a single weighting method, effectively reduces the impact of relying solely on subjective weights or previous methods of obtaining objective weights on data dependence, and improves the reliability and accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a method for evaluating the entire life cycle of a power grid infrastructure project according to an embodiment of the present invention;

[0018] Figure 2 This is a structural diagram of a terminal for evaluating the entire life cycle of a power grid infrastructure project according to an embodiment of the present invention;

[0019] Figure 3 This is an explanatory diagram of a random forest algorithm for a method for evaluating the entire life cycle of a power grid infrastructure project according to an embodiment of the present invention;

[0020] Description of labels:

[0021] 1. A terminal for evaluating the entire life cycle of power grid infrastructure projects; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0022] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0023] Please refer to Figure 1 A method for evaluating the entire life cycle of a power grid infrastructure project comprises the following steps:

[0024] S1. Establish an evaluation system for the entire project life cycle, including the operation and maintenance phase and the operational feedback phase, as well as the benchmark values ​​of each indicator in the evaluation system, and obtain the indicator values ​​of each indicator;

[0025] S2. Obtain the AHP subjective weight of each indicator according to the evaluation system;

[0026] S3, obtain the objective weight of each indicator through the pre-trained random forest algorithm;

[0027] S4. Calculate the combined weight of each indicator based on the AHP subjective weight and the objective weight of each indicator;

[0028] S5. Calculate the comprehensive indicator score of each indicator and the comprehensive project score for the entire life cycle of the project based on the indicator value, benchmark value and combined weight of each indicator.

[0029] From the above description, it can be seen that the beneficial effects of the present invention are: a full life cycle evaluation method and terminal for a power grid infrastructure project of the present invention considers the operation and maintenance stage and the operation feedback stage to establish an evaluation system for the full life cycle of the project, which is more comprehensive, uses a random forest algorithm to obtain objective weights, uses an improved hierarchical analysis method to obtain subjective weights, and uses the subjective weights and objective weights to comprehensively determine the combined weights of each indicator. The combined weight determination strategy overcomes the shortcomings of a single weighting method, effectively reduces the impact of relying solely on subjective weights or previous methods of obtaining objective weights on data dependence, and improves the reliability and accuracy of the evaluation.

[0030] Furthermore, the step S2 includes the steps of:

[0031] S21, establishing a judgment matrix scale;

[0032] S22. Obtaining the expert importance comparison scores according to the matrix scale and generating a judgment matrix;

[0033] S23, normalizing the judgment matrix to obtain a standard judgment matrix;

[0034] S24. Sum the rows of the standard judgment matrix and normalize the row sums to obtain the AHP subjective weight of each indicator.

[0035] From the above description, it can be seen that the AHP subjective weight is calculated through the above method.

[0036] Furthermore, the judgment matrix is:

[0037] A=[a ij ] n×n ;

[0038] Normalizing the judgment matrix by column is specifically as follows:

[0039]

[0040] Sum the obtained standard judgment matrix row by row to get the sum of each row of the standard judgment matrix:

[0041]

[0042] Normalize the sum of each row of the standard matrix to obtain the AHP subjective weight W of each indicatori :

[0043]

[0044] Among them, n represents the number of indicators, Represents the elements of the normalized matrix.

[0045] From the above description, it can be seen that the AHP subjective weight is calculated using the above formula.

[0046] Furthermore, the step S2 further includes the steps of:

[0047] S25. Calculate the consistency index CI:

[0048]

[0049] Where n represents the number of indicators, λmax represents the maximum eigenvalue of the judgment matrix, and the calculation formula of λmax is as follows:

[0050]

[0051] Among them, W i Indicates the AHP subjective weight of each indicator, a ij Represents the value of the i-th row and j-th column in the judgment matrix, i <n,j<n;

[0052] S26, obtaining a random consistency index RI by looking up a table according to the order of the judgment matrix;

[0053] S27. Calculate the consistency ratio:

[0054]

[0055] If the CR value is less than the preset threshold, the judgment matrix passes the consistency test.

[0056] As can be seen from the above description, the present invention also performs a consistency test on the judgment matrix to ensure the validity of the AHP subjective weights.

[0057] Furthermore, the training of the random forest algorithm in step S3 includes the following steps:

[0058] S31. Perform Bootstrap sampling using random forest to generate K independent decision trees by extracting K sample data sets, where each sample data set represents a full life cycle indicator system for a power grid infrastructure project.

[0059] S32, let k = 1, train decision tree T k , the training input is the kth data set, calculate the kth data set, and calculate the accuracy L of the kth out-of-bag data set k ;

[0060] S33, rearrange the features f in the out-of-bag dataset and calculate the accuracy

[0061] S34, for all sample data sets k=2, 3...K, execute steps S32 and 33;

[0062] S35. Calculate the classification accuracy error after rearrangement:

[0063]

[0064] S36. For each feature f, calculate the influence of feature f on the accuracy of out-of-bag data:

[0065]

[0066] And the variance of the impact degree:

[0067]

[0068] Then calculate the importance of feature f c :

[0069] f c =e f / S;

[0070] Get the importance of all features f c .

[0071] From the above description, it can be seen that the random forest algorithm is trained in the above manner to calculate the objective weights.

[0072] Furthermore, the calculation of the combination weight is specifically as follows:

[0073]

[0074] Among them, W j represents the combined weight of indicator j, W j 1 and W j 2 They represent the subjective weight and objective weight of indicator j respectively.

[0075] From the above description, it can be seen that the combined weight is calculated by the Lagrange multiplier method based on the subjective weight and the objective weight. Based on the obtained combined weight, the influencing factors of each indicator in a single project can be comprehensively analyzed, and greater attention can be paid to indicators with large weights to save project costs.

[0076] Furthermore, the step S5 is specifically as follows:

[0077] and dividing the index value of each index by the reference value, performing normalization processing on the index value of each index to unify the index dimension, and obtaining a second index value of each index;

[0078] For each indicator, multiplying the second indicator value by the combination weight to obtain the indicator comprehensive score of each indicator;

[0079] Add up the comprehensive scores of all the indicators to obtain the comprehensive score of the project.

[0080] From the above description, it can be seen that the present invention draws on the idea of ​​per-unit value to normalize each indicator to unify the indicator dimension, and on this basis, performs weighted calculation on the indicators, calculates the comprehensive indicator score of each indicator, and the comprehensive project score, and evaluates the project and each indicator within the project.

[0081] Furthermore, the evaluation system for the entire life cycle of the project includes indicators such as planning capability, feasibility study, survey fee, basic design fee, other design fee, bidding work, contract content, execution evaluation, equipment data, construction and installation engineering fee, equipment purchase fee, other fees, dynamic fee, labor fee, energy consumption fee, environmental fee, other fees, maintenance fee, repair fee, labor fee, insurance fee, power outage time, repair cost, electricity value coefficient, average failure rate, scrapping disposal cost, equipment residual value, line loss rate, comprehensive voltage qualification rate, power supply reliability rate, incremental investment economic internal rate of return, incremental capital return rate, incremental investment financial net present value, incremental investment return rate, management mechanism evaluation, incentive mechanism evaluation, N-1 pass rate, overload line ratio and continuous safe operation days;

[0082] Among them, other design fees are fees charged according to the relevant needs of engineering design or the relevant regulations of the client;

[0083] Other expenses refer to other related expenses necessary to complete the construction of the project but not including construction costs, installation costs and equipment purchase costs;

[0084] Other expenses refer to the expenses required for the annual project operation during the operation and maintenance phase, in addition to labor costs, energy costs and environmental fees.

[0085] From the above description, it can be seen that the present invention scientifically divides and defines projects based on the theory of the entire life cycle, conducts multi-angle inspections and evaluations on them, realizes the evaluation and control of the entire process of power grid infrastructure projects, clarifies the cost structure of each stage of project design, construction, operation and maintenance, inspection, risk and scrapping, and takes it into more comprehensive considerations.

[0086] Furthermore, the acquisition of the device data is specifically as follows:

[0087] Step 1: Establish alternative cost calculation model S:

[0088]

[0089]

[0090]

[0091] Among them, minS is the minimum cost investment in the selection stage, operation and maintenance stage, and scrapping stage of the specific equipment of the power grid infrastructure project, x j , j=1,2,…n; is the number of n types of equipment required for the infrastructure project, c j , j=1,2,…n; is the unit cost of the corresponding equipment, ω j , j = 1, 2, ... n; is the average annual operation and maintenance cost of a single device, N is the life of the equipment, and i is the discount rate;

[0092] Step 2: Use the linear programming method to find an integer feasible solution for S and obtain the objective function value s * Indicates the optimal cost of equipment solution selection S, then there is and iterate;

[0093] Step 3: Select any variable x from the optimal solution of S that does not meet the integer condition. j , whose value is b j , with [b j ] means less than b j The maximum integer that will be constrained by the condition x j <[b j ] is added to S as a constraint for the subsequent planning problem S1, and the conditional constraint x j ≥[b j ]+1 is added to S as the solution constraint condition of the subsequent planning problem S2, and the subsequent planning problems S1 and S2 are solved respectively using the linear programming method;

[0094] Step 4: If the optimal objective function of each branch is greater than j, then cut off this branch. If it is less than j and does not meet the integer condition, repeat step 2 until Get the optimal solution

[0095] As can be seen from the above description, the device data is calculated and obtained in the above manner.

[0096] Please refer to Figure 2 A terminal for the full life cycle evaluation of a power grid infrastructure project includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method for the full life cycle evaluation of a power grid infrastructure project are implemented.

[0097] The present invention provides a method and terminal for evaluating the entire life cycle of a power grid infrastructure project, which are applicable to the entire life cycle evaluation of a power grid infrastructure project.

[0098] Please refer to Figure 1 and Figure 3 , embodiment 1 of the present invention is:

[0099] A method for evaluating the entire life cycle of a power grid infrastructure project, characterized by comprising the steps of:

[0100] S1. Establish an evaluation system for the entire project life cycle, including the operation and maintenance phase and the operational feedback phase, as well as the benchmark values ​​of each indicator in the evaluation system, and obtain the indicator values ​​of each indicator;

[0101] The evaluation system for the entire life cycle of the project includes indicators such as planning capability, feasibility study, survey fee, basic design fee, other design fee, bidding work, contract content, execution evaluation, equipment data, construction and installation engineering fee, equipment purchase fee, other fees, dynamic fee, labor fee, energy consumption fee, environmental fee, other fees, maintenance fee, repair fee, labor fee, insurance fee, power outage time, repair cost, electricity value coefficient, average failure rate, scrap disposal cost, equipment residual value, line loss rate, comprehensive voltage qualification rate, power supply reliability rate, incremental investment economic internal rate of return, incremental capital return rate, incremental investment financial net present value, incremental investment return rate, management mechanism evaluation, incentive mechanism evaluation, N-1 pass rate, overload line ratio, and continuous safe operation days;

[0102] Among them, other design fees are fees charged according to the relevant needs of engineering design or the relevant regulations of the client;

[0103] Other expenses refer to other related expenses necessary to complete the construction of the project but not including construction costs, installation costs and equipment purchase costs;

[0104] Other expenses refer to the expenses required for the annual project operation during the operation and maintenance phase, in addition to labor costs, energy costs and environmental fees.

[0105] In this example, the model's target layer covers the entire lifecycle of power grid infrastructure projects. The criteria layer is divided into four stages: planning, implementation, operation and maintenance, and production and operation feedback. Examples of indicators at each level are shown in Table 1. In this example, the scoring calculation primarily focuses on the third-level indicators.

[0106] Planning stage:

[0107] The planning phase involves drafting a contract, developing a design plan, and reviewing the drawings for reasonable compliance based on the overall data such as surveys and design diagrams. If the design does not meet the contract requirements, the design plan needs to be adjusted until it meets the design requirements. At the same time, the cost and investment expenses of each link are compared with the design plan, and the calculation results are repeatedly verified to avoid miscalculations. The indicators established at each level are as follows:

[0108] 1.1.1 Preliminary Work Plan

[0109] (1) Planning ability

[0110] Project planning capability is the activity of project execution agencies selecting and formulating project goals, engineering standards, project budgets, implementation procedures and implementation plans based on future project decisions.

[0111] The specific scoring criteria for the standardized execution of the schedule plan are as follows:

[0112] ①The progress plan is complete and covers all aspects;

[0113] ② Progress control complies with the power company's construction period and progress management regulations;

[0114] ③Schedule deviations are handled promptly and schedule plans are adjusted dynamically in a timely manner;

[0115] ④ The overall control measures for construction progress are good, and the progress risks and response measures at each stage are scientific, reasonable and effective;

[0116] Each item "compliant" is worth 100 points, each item "basically compliant" is worth 80 points, and each item "partially compliant" is worth 60 points.

[0117] Baseline value: 60 points

[0118] (2) Feasibility study

[0119] Feasibility study refers to a method of comprehensively analyzing and demonstrating a project's technological advancement and economic rationality in order to achieve optimal economic results. It is generally divided into three stages: opportunity study, preliminary feasibility study, and technical and economic feasibility study.

[0120] The specific scoring criteria for feasibility study are as follows:

[0121] ① Meet market expectations through market research

[0122] ②Preliminary feasibility study

[0123] ③Technical and economic feasibility study

[0124] ④Construction accidents and risk assessment

[0125] Each item "compliant" is worth 100 points, each item "basically compliant" is worth 80 points, and each item "partially compliant" is worth 60 points.

[0126] Baseline value: 60 points

[0127] 1.1.2 Survey and design work

[0128] The design fee for survey and design work consists of survey fees, basic design fees and other design fees.

[0129] (1) Survey fees

[0130] Survey fees refer to the fees paid by the project legal person to a qualified survey organization for engineering survey work, preparation of relevant survey documents and geotechnical engineering design documents, etc., in accordance with survey and design specifications. They are calculated based on the engineering survey fee standards issued by the national administrative department.

[0131] Benchmark value: industry average spending

[0132] (2) Basic design fee

[0133] The basic design fee refers to the cost of preparing preliminary design documents and construction drawings. This fee includes services such as providing technical briefings, resolving technical difficulties encountered during construction according to the design, and final completion acceptance services. The calculation formula is as follows:

[0134] J=Y×t1×t2×t3 (I-1)

[0135] Among them: J is the basic design fee; Y is the engineering design base; t1 is the professional adjustment coefficient; t2 is the engineering complexity cost adjustment coefficient; t3 is the additional adjustment coefficient.

[0136] The engineering design fee is the sum of equipment fees, construction and installation fees, tools and equipment fees, and trial operation fees in the preliminary design budget approved for the project.

[0137] Benchmark value: industry average spending

[0138] (3) Other design fees

[0139] Other design fees are fees charged based on the relevant needs of engineering design or the relevant regulations of the client, including overall design fees, coordination fees, standard design fees, reuse design fees and other related cost items. The calculation formula is as follows:

[0140] Q=J×λ(1-2)

[0141] Where: Q is other design fees; λ is the ratio of other design fees (to the base design fee).

[0142] Benchmark value: industry average spending

[0143] 1.1.3 Bidding

[0144] The design of power grid infrastructure projects is typically conducted through open bidding. Power design institutes draft bid documents based on the tender documents, which include technical proposals, price quotes, and company qualifications. The bidding authority reviews the bid documents and ultimately determines the winning bidder. Once a power design institute is awarded the bid, it proceeds to design the project based on the project proposal or investment estimate.

[0145] 1.1.4 Contract Signing Evaluation

[0146] (1)Contract content

[0147] Evaluate the richness of the results involved in the contract terms, the responsiveness of the research content, and the perfection of the contract terms.

[0148] The specific scoring criteria for the compliance of contract signing are as follows:

[0149] ① The contract signing process is standardized and reasonable. 40 points if it complies, 30 points if it basically complies, and 20 points if it partially complies;

[0150] ② The contract clearly stipulates the rights and obligations of both parties, the solution to contractual disputes, the distribution of benefits and risks, and detailed provisions for matters needing attention, etc., and is fair. 30 points for compliance, 20 points for compliance, and 15 points for almost compliance;

[0151] ③ The contract terms are complete and meet the requirements. If they meet the requirements, they will be awarded 30 points; if they basically meet the requirements, they will be awarded 20 points; if they partially meet the requirements, they will be awarded 15 points.

[0152] Baseline value: 60 points

[0153] (2) Execution evaluation

[0154] Evaluate the monthly and annual funding execution of the project.

[0155] Scoring Rules:

[0156] ① Monthly fund execution status;

[0157] ②Annual fund execution status.

[0158] Each item "compliant" is worth 100 points, each item "basically compliant" is worth 80 points, and each item "partially compliant" is worth 60 points.

[0159] Baseline value: 60 points

[0160] Project implementation phase:

[0161] 1.2.1 Equipment selection analysis

[0162] During the progress of power grid infrastructure projects, equipment selection is one of the important links affecting capital investment, and is determined by the nature of the power grid infrastructure project. Equipment costs account for a large proportion of the total investment, and the factors of equipment selection affect the cost expenditure in the subsequent operation and maintenance and inspection stages.

[0163] When selecting equipment for power grid infrastructure projects, there are many indicators based on actual project needs, which we understand as constraints. These constraints vary across different project environments, and equipment selection impacts costs during the operation and maintenance phase. The decision-maker's goal is to achieve optimal costs while satisfying these constraints. Furthermore, the final result of the equipment selection decision must be an integer. Therefore, this problem is an integer programming problem.

[0164] Step 1: Establish alternative cost calculation model S:

[0165]

[0166]

[0167]

[0168] Among them, minS is the minimum cost investment in the selection stage, operation and maintenance stage, and scrapping stage of the specific equipment of the power grid infrastructure project, x j , j=1,2,…n; is the number of n types of equipment required for the infrastructure project, c j , j=1,2,…n; is the unit cost of the corresponding equipment, ω j , j = 1, 2, ... n; is the average annual operation and maintenance cost of a single device, N is the life of the equipment, and i is the discount rate;

[0169] Step 2: Use the linear programming method to find an integer feasible solution for S and obtain the objective function value s * Indicates the optimal cost of equipment solution selection S, then there is and iterate;

[0170] Step 3: Select any variable x from the optimal solution of S that does not meet the integer condition. j , whose value is b j , with [b j ] means less than b j The maximum integer that will be constrained by the condition x j <[b j ] is added to S as a constraint for the subsequent planning problem S1, and the conditional constraint x j ≥[b j ]+1 is added to S as the solution constraint condition of the subsequent planning problem S2, and the subsequent planning problems S1 and S2 are solved respectively using the linear programming method;

[0171] Step 4: If the optimal objective function of each branch is greater than j, then cut off this branch. If it is less than j and does not meet the integer condition, repeat step 2 until Get the optimal solution

[0172] Benchmark value: Overall project investment

[0173] 1.2.2 Cost analysis during the construction phase

[0174] According to the latest "Regulations on the Preparation and Calculation of Power Grid Project Construction Budgets" (2013 edition) issued by the National Energy Administration, the construction phase costs of power grid infrastructure projects are composed of construction and installation costs, equipment purchase costs, other expenses, and dynamic costs. The sum of construction and installation costs, equipment purchase costs, and other expenses is called static investment. The following is a detailed breakdown of these cost components.

[0175] (1) Construction and installation costs

[0176] Construction and installation costs include both construction and installation costs. Construction costs refer to the costs incurred for constructing the various buildings, structures, and other facilities that comprise the project, ensuring they meet design requirements and function. Installation costs refer to the costs incurred for assembling, assembling, and commissioning the various equipment, piping, cables, and auxiliary equipment that comprise the project's production process system, ensuring they meet the design requirements and function.

[0177] Construction and installation project costs consist of five parts: direct costs, indirect costs, profits, price differences in the base period and taxes.

[0178] Benchmark value: industry average spending

[0179] (2) Equipment purchase costs

[0180] Equipment purchase expenses refer to the expenses incurred in purchasing or manufacturing various equipment for project construction and transporting the equipment to the designated location on the construction site. They include equipment fees and equipment transportation expenses.

[0181] Benchmark value: industry average spending

[0182] (3) Other expenses

[0183] Other expenses refer to expenses necessary to complete the project construction that are not included in construction costs, installation costs, or equipment purchase costs. They include construction site acquisition and clearance fees, project construction management fees, project construction technical service fees, production preparation fees, and large-scale transportation measures fees.

[0184] Benchmark value: industry average spending

[0185] (4) Dynamic Fees

[0186] Dynamic costs refer to the costs incurred for the various elements that constitute the project cost during the period from construction budget preparation year to completion acceptance due to price increases and capital cost increases caused by changes in time and market prices, mainly including price difference reserves and construction period loan interest.

[0187] Dynamic costs include price difference reserve and construction period interest.

[0188] The formula for calculating the price difference reserve is:

[0189]

[0190] C is the price difference reserve, e is the annual increase index, n1 is the time interval from the construction budget preparation year to the construction start year; n2 is the construction period; i is the i-th year from the starting year; Fi is the project funds invested in the i-th year.

[0191] The interest on construction loans is calculated based on the bank's interest rate for the same period.

[0192] Benchmark value: industry average spending

[0193] Operation and maintenance stage:

[0194] 1.3.1 Cost Analysis during Operation and Maintenance

[0195] The cost of the operation and maintenance phase of a power grid infrastructure project refers to the sum of all costs incurred during the operation period after the completion of the power grid infrastructure project, including labor costs, energy consumption costs, environmental costs and other costs.

[0196] (1) Labor costs: refers to the total of project personnel’s wages, benefits, allowances, personnel safety and security expenses, and training expenses.

[0197] (2) Energy consumption: refers to the energy costs consumed in the daily operation of the project after completion, such as electricity consumption, oil consumption, and coal consumption.

[0198] (3) Environmental costs: The operation of power grid infrastructure projects may cause certain types of pollution, such as noise pollution, water pollution, and electromagnetic radiation. As my country pays more and more attention to environmental protection, the expenditure on environmental protection in daily operation and maintenance is increasing. Therefore, environmental expenditures must also be included in the full life cycle cost accounting. Specifically, it refers to the total amount of expenses spent on protecting the environment around the project or beautifying the surrounding area of ​​the project. This part of the content is also continuously spent as the project operates.

[0199] (4) Other expenses

[0200] Indicates other annual expenses for project operation.

[0201] 1.3.2 Routine maintenance cost analysis

[0202] Routine maintenance refers to planned periodic equipment maintenance work. This type of maintenance will not affect the use of electricity and will not cause cost losses due to power outages. The calculation method is as follows:

[0203]

[0204] in:

[0205] r0 is the rate of return on investment

[0206] t i is the maintenance period of the i-th equipment;

[0207] N is the number of project equipment

[0208] p is the maintenance rate.

[0209] Maintenance rate = equipment operating cost / investment amount

[0210] Equipment investment amount = equipment investment amount * maintenance fee rate

[0211] 1.3.3 Non-routine maintenance cost analysis

[0212] Unconventional maintenance is maintenance performed due to planned power outages and sudden failures that cause power outages on the power user side. The calculation formula is as follows:

[0213] FC i =Σ a ×W j ×T J +λ j ×RC j ×MRRT j (1-6)

[0214] λ j is the annual average failure rate of the jth device;

[0215] T J is the annual failure interruption rate of the jth device;

[0216] RC j is the average repair cost of the jth device;

[0217] MRRT j is the average repair time of the jth device;

[0218] a is the electricity consumption value coefficient of the relevant electricity-consuming side user;

[0219] Operational feedback stage:

[0220] 1.4.1 Cost of scrapping

[0221] The primary costs of power grid infrastructure projects at the end-of-life stage include equipment dismantling, demolition of supporting buildings, waste recycling, and environmental protection costs, minus the cost of recovered equipment and materials. These environmental protection costs are due to the fact that many power grid infrastructure projects are built in suburban areas, mountainous areas, or on cultivated land. The costs associated with protecting or restoring the surrounding environment after decommissioning are called environmental protection costs. Recognizing environmental protection costs helps power grid companies clarify their social responsibilities and raise awareness of environmental protection costs. The end-of-life costs are calculated as the disposal costs minus the equipment's residual value.

[0222] 1.4.2 Social and economic benefits

[0223] (1) Line loss rate (comprehensive line loss rate of 10kV and below)

[0224] This indicator refers to the percentage of loss load of 10kV and below distribution network lines to the power supply load. It is an important part of the power department's assessment and a comprehensive technical and economic indicator reflecting the level of power grid operation and management. The establishment of this indicator aims to guide various units to pay attention to the scientific nature of power grid development while paying attention to improving the power supply capacity of the power grid, enhance energy-saving and consumption-reducing awareness, optimize the power grid structure, and improve power supply efficiency.

[0225] Base value: power supply load

[0226] (2) Comprehensive voltage qualification rate

[0227] This indicator is used to measure the quality of power supply provided by the company to users. It is specifically defined as the percentage of the cumulative operating time when the actual operating voltage deviation is within the limit to the corresponding total operating statistical time.

[0228] Benchmark value: total running statistical time

[0229] (3) Power supply reliability

[0230] This indicator, based on the "Regulations for the Evaluation of Power Supply Reliability for Power System Users" (DL / T836-2012), is used to quantitatively measure the degree to which the power supply network provides reliable power to users. Taking into account power outages due to faults and scheduled outages, and ignoring power curtailment due to insufficient system power, the average number of power outage hours per user during the statistical period is calculated. This indicator is labeled AIHC-3 (hours per user). The calculation formula is as follows:

[0231] Power supply reliability rate = 1-(average power consumption time of users / statistical time)*100%

[0232] (4) Economic internal rate of return of incremental investment (△EIRR)

[0233] The incremental investment economic internal rate of return is divided into incremental total investment and incremental domestic investment economic internal rate of return. The incremental total investment economic internal rate of return is a relative indicator that reflects the benefits a project creates for the national economy from the perspective of the national economy as a whole. It represents the dynamic benefits that can be achieved from the funds employed by the project. The incremental domestic investment economic internal rate of return reflects the dynamic benefits that can be achieved from the domestic funds employed by the project. △EIRR is calculated by reversing the formula 1-7:

[0234]

[0235] Where: △EIRR is the economic internal rate of return:

[0236] △B is the incremental inflow of national economic benefits:

[0237] △C is the incremental outflow of national economic benefits:

[0238] (ΔB-ΔC) t is the incremental net benefit flow in year t.

[0239] n is the number of years in the calculation period

[0240] 1.4.3 Project Sustainability Evaluation (External Environment)

[0241] (1) Incremental capital return rate

[0242] The incremental capital profit rate refers to the ratio of the average annual incremental profit to the incremental capital during the production and operation period of the project, which reflects the profitability of the incremental capital.

[0243] Return on capital = total annual incremental profit / incremental capital × 100%

[0244] (2) Financial net present value of incremental investment (ΔFNPV)

[0245] The incremental investment financial net present value refers to the benchmark rate of return (i c ) or a set discount rate, discounting the incremental net cash flows (incremental total investment or incremental equity capital) for each year during the project's calculation period back to the present value at the beginning of the construction period. This dynamic indicator reflects the profitability of incremental investment over the project's lifecycle. Actual incremental net cash flows are used for years prior to the post-evaluation point. For years after the post-evaluation point, incremental net cash flows are re-forecasted based on circumstances. Each year's incremental cash flows are discounted back to the beginning of the construction period using the newly selected discount rate, and the sum is then calculated for all years.

[0246] ΔFNPV=∑(ΔRCI-ΔRCO) t (1+i k ) -t t=1……n (1-8)

[0247] ΔRCI – the actual or re-forecasted annual incremental cash inflow of the project;

[0248] ΔRCO – the actual or re-forecasted annual incremental cash outflow of the project;

[0249] i k - A discount rate reselected based on actual circumstances;

[0250] n——calculation period (same as previous evaluation);

[0251] t——a specific year in the assessment period.

[0252] A project with an incremental investment financial net present value greater than or equal to zero is considered successful.

[0253] (3) Incremental investment profit rate

[0254] The incremental investment profit rate refers to the ratio of the average annual incremental profit of the project during the production and operation period to the total incremental funds during the construction period (the sum of the incremental fixed asset investment and the incremental total working capital). It is a static indicator for examining the profitability of the project's unit incremental investment.

[0255] Incremental investment profit rate = total incremental profit / total incremental investment × 100%

[0256] The total incremental profit is the difference between the total profits of "with projects" and "without projects", and the total incremental funds are the difference between the total funds of "with projects" and the total funds of "without projects".

[0257] 1.4.4 Project Sustainability Evaluation (Internal Mechanism)

[0258] (1) Management mechanism evaluation

[0259] Expert subjective scoring

[0260] Baseline value: 60 points

[0261] (2) Evaluation of incentive mechanisms

[0262] Expert subjective scoring

[0263] Baseline value: 60 points

[0264] (3) N-1 pass rate

[0265] This indicator reflects the grid's ability to maintain normal and continuous power supply to loads under a set of anticipated accidents and is used to verify the strength of the distribution network structure and the rationality of its operation. Medium-voltage line N-1 refers to the power transfer capacity of the same-level grid when a segment of a medium-voltage line (including a segment of an overhead line, a ring network unit of a cable line, or a section of a cable feeder) fails or is scheduled to be shut down. The medium-voltage line N-1 pass rate reflects the proportion of medium-voltage lines that meet N-1 requirements. When calculating this indicator, the power transfer capacity of the grid at the same level and that of the lower-level grid must be properly considered.

[0266] N-1 pass rate = number of medium-voltage public distribution lines that meet N-1 in the line / total number of medium-voltage public distribution lines * 100%

[0267] Multiply the N-1 pass rate by 100 to get the score of this indicator.

[0268] Baseline value: 60 points

[0269] (4) Overload line ratio

[0270] Base value: 1

[0271] (5) Continuous safe operation days

[0272] Base value: 365

[0273] Table 1

[0274]

[0275]

[0276] The step S1 further comprises the steps of:

[0277] S11. Normalize the indicator values ​​according to the reference values ​​to unify the indicator dimensions.

[0278] In this embodiment, the above indicators are subsequently normalized to unify the indicator dimensions, and the value of each indicator is divided by the benchmark value to obtain a per-unit value, on which basis the indicators are weighted.

[0279] In power systems, grid voltage levels vary. To simplify calculations, per-unit values ​​are used to unify the dimensions. In the random forest algorithm, due to the large disparity and uncorrelation between the data for each metric, it's normally impossible to directly analyze and rank their importance. Drawing on the concept of per-unit values, we select a baseline value for each metric and divide the raw data by it to achieve a unified dimension.

[0280] S2. Obtain the AHP subjective weight of each indicator according to the evaluation system;

[0281] The step S2 comprises the steps of:

[0282] S21, establishing a judgment matrix scale;

[0283] S22. Obtaining the expert importance comparison scores according to the matrix scale and generating a judgment matrix;

[0284] The judgment matrix is:

[0285] A=[a ij ] n×n ;

[0286] S23, normalizing the judgment matrix to obtain a standard judgment matrix;

[0287] Normalizing the judgment matrix by column is specifically as follows:

[0288]

[0289] S24, summing the rows of the standard judgment matrix and normalizing the row sums to obtain the AHP subjective weight of each indicator;

[0290] Sum the obtained standard judgment matrix row by row to get the sum of each row of the standard judgment matrix:

[0291]

[0292] Normalize the sum of each row of the standard matrix to obtain the AHP subjective weight W of each indicator i :

[0293]

[0294] Among them, n represents the number of indicators, Represents the elements of the normalized matrix.

[0295] In this embodiment, in the hierarchical analysis method, numbers 1-9 and their reciprocals are used as judgment matrix scales. The meaning of the judgment matrix scales is shown in Table 2 below:

[0296] Table 2

[0297] scale meaning 1 The two indicators have the same importance 3 Compared with the two indicators, the former is slightly more important than the latter 5 Compared with the two indicators, the former is more important than the latter 7 Compared with the two indicators, the former is more important than the latter 9 Compared with the two indicators, the former is extremely important than the latter 2、4、6、8 The median value of the above adjacent judgments

[0298] Hierarchical single ranking includes calculating the relative weights of indicators at each level in the evaluation index system and performing consistency checks. Expert scoring forms a judgment matrix of the criterion level indicators relative to the target level. An example of the judgment matrix is ​​shown in Table 3:

[0299] Table 3

[0300] A B1 B2 B3 B4 B1 1 1 / 2 1 / 3 1 / 4 B2 2 1 1 1 B3 3 1 1 1 B4 4 1 1 1

[0301] For ease of understanding, the above table shows the importance of indicators from high to low as follows: B4, B3, B2, B1. The above table is only an example to assist understanding and is not an actual judgment matrix.

[0302] The step S2 further comprises the steps of:

[0303] S25. Calculate the consistency index CI:

[0304]

[0305] Where n represents the number of indicators, λmax represents the maximum eigenvalue of the judgment matrix, and the calculation formula of λmax is as follows:

[0306]

[0307] Among them, W i Indicates the AHP subjective weight of each indicator, a ij Represents the value of the i-th row and j-th column in the judgment matrix, i <n,j<n;

[0308] S26, obtaining a random consistency index RI by looking up a table according to the order of the judgment matrix;

[0309] S27. Calculate the consistency ratio:

[0310]

[0311] If the CR value is less than the preset threshold, the judgment matrix passes the consistency test.

[0312] S3, obtain the objective weight of each indicator through the pre-trained random forest algorithm;

[0313] The training of the random forest algorithm in step S3 includes the following steps:

[0314] S31. Perform Bootstrap sampling using random forest to generate K independent decision trees by extracting K sample data sets, where each sample data set represents a full life cycle indicator system for a power grid infrastructure project.

[0315] S32, let k = 1, train decision tree T k , the training input is the kth data set, calculate the kth data set, and calculate the accuracy L of the kth out-of-bag data set k ;

[0316] S33, rearrange the features f in the out-of-bag dataset and calculate the accuracy

[0317] S34, for all sample data sets k=2, 3...K, execute steps S32 and 33;

[0318] S35. Calculate the classification accuracy error after rearrangement:

[0319]

[0320] S36. For each feature f, calculate the influence of feature f on the accuracy of out-of-bag data:

[0321]

[0322] And the variance of the impact degree:

[0323]

[0324] Then calculate the importance of feature f c :

[0325] f c =e f / S;

[0326] Get the importance of all features f c .

[0327] like Figure 3 As shown in the figure, random forest is an algorithm that integrates multiple trees using the concept of ensemble learning. Its basic unit is the decision tree, and its essence belongs to the ensemble learning method, a major branch of machine learning. Random forest has a good ability to prevent overfitting. The specific process of constructing each classifier requires randomly extracting a portion of samples from the original dataset as the sample subspace, then randomly selecting a new feature subspace from the sample subspace, building a decision tree as the classifier in this new space, and finally reaching the final decision through voting. Random forest has two important randomization methods:

[0328] 1. Use bagging to create a training set for each tree. The idea behind bagging is to randomly select a subset of samples from the overall population for training, then perform voting to determine the classification results through multiple samplings. The final result is the average of the above models. Because samples are repeatedly sampled and then repeatedly replaced, this increases the perturbation capacity of the samples.

[0329] 2. Random Subspace of Features: When splitting each node in a decision tree, a subset of all feature samples is randomly selected, and the optimal split is chosen from this subset to construct the tree. A decision tree is a sequence of independent and identically distributed random variables. Because each decision tree is trained independently, random forest training can be performed in parallel, effectively ensuring the efficiency and scalability of the random forest algorithm.

[0330] S4. Calculate the combined weight of each indicator based on the AHP subjective weight and the objective weight of each indicator;

[0331] The calculation of the combination weight is specifically as follows:

[0332]

[0333] Among them, W j represents the combined weight of indicator j, W j 1 and W j 2 They represent the subjective weight and objective weight of indicator j respectively.

[0334] The insulation condition of the transformers reflected by various characteristic indicators varies. The subjective weights derived from the AHP method reflect the evaluator's preferences. The random forest algorithm, using decision trees and ensemble learning, objectively weights the indicators. To avoid the drawbacks of excessive subjectivity and potential errors in the algorithmic data, this paper effectively combines these two weighting methods, employing a combined weighting method to reflect the influence of different indicators. This method fully accounts for subjective scores based on expert experience while also adjusting the weights based on the characteristics of the data itself, making the resulting weights more scientific and reasonable.

[0335] Based on the obtained combined weights, we can comprehensively analyze the influencing factors of each indicator in a single project, and pay more attention to indicators with large weights to save project costs.

[0336] S5. Calculate the comprehensive score of each indicator and the comprehensive project score for the entire life cycle of the project based on the indicator value, benchmark value and combined weight of each indicator;

[0337] The step S5 is specifically as follows:

[0338] and dividing the index value of each index by the reference value, performing normalization processing on the index value of each index to unify the index dimension, and obtaining a second index value of each index;

[0339] For each indicator, multiplying the second indicator value by the combination weight to obtain the indicator comprehensive score of each indicator;

[0340] Add up the comprehensive scores of all the indicators to obtain the comprehensive score of the project.

[0341] Please refer to Figure 2 , the second embodiment of the present invention is:

[0342] A power grid infrastructure project full life cycle evaluation terminal 1, comprising a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2, wherein the processor 2 executes the computer program to implement the steps of the power grid infrastructure project full life cycle evaluation method of the first embodiment above.

[0343] In summary, the present invention provides a method and terminal for evaluating the entire life cycle of a power grid infrastructure project. The method and terminal for evaluating the entire life cycle of a power grid infrastructure project of the present invention establish an evaluation system for the entire life cycle of the project by considering the operation and maintenance stage and the operation feedback stage. The system is more comprehensive, uses a random forest algorithm to obtain objective weights, uses an improved hierarchical analysis method to obtain subjective weights, and uses the subjective weights and objective weights to comprehensively determine the combined weights of each indicator. The combined weight determination strategy overcomes the shortcomings of a single weighting method, effectively reduces the impact of relying solely on subjective weights or previous methods of obtaining objective weights on data dependence, and improves the reliability and accuracy of the evaluation.

[0344] The present invention starts from the whole process of power grid infrastructure projects, scientifically divides and defines the projects based on the whole life cycle theory, and conducts multi-angle inspection and evaluation on them, so as to realize the evaluation and control of the whole process of power grid infrastructure projects, take into account the development speed and development quality, make rational and efficient use of the internal resources of the enterprise, and improve the enterprise's operating ability, asset profitability and quality service capabilities.

[0345] The present invention analyzes and sets evaluation indicators for power grid infrastructure projects within each life cycle; quantifies the influencing factors of each stage of the infrastructure project cycle, constructs an evaluation indicator system for power grid infrastructure projects from the dimensions of determining the target layer, criterion layer, and indicator layer, establishes a scientific and objective power grid construction project evaluation system, and conducts post-evaluation of power grid infrastructure projects.

[0346] This method normalizes the matrix composed of each characteristic quantity to the same dimension using the concept of per-unit values. It then uses a random forest algorithm to determine the objective weights of the indicators and an improved analytic hierarchy process to determine the subjective weights of the indicators. Finally, a combined weighting method is used to sort the subjective and objective weights according to indicator importance to obtain the combined weight of each indicator. This weighting strategy overcomes the shortcomings of a single weighting method and has certain practical significance.

[0347] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for evaluating the entire life cycle of a power grid infrastructure project, characterized in that: Including steps: S1. Establish an evaluation system for the entire project life cycle, including the operation and maintenance phase and the operational feedback phase, as well as the benchmark values ​​of each indicator in the evaluation system, and obtain the indicator values ​​of each indicator; The step S1 further comprises the steps of: S11, normalizing the indicator values ​​according to the reference values ​​to unify the indicator dimensions; The evaluation system for the entire life cycle of the project includes indicators: equipment data; The acquisition of the device data is specifically as follows: Step 1: Establish alternative cost calculation model S: ； ； ； Among them, minS is the minimum cost investment in the selection stage, operation and maintenance stage, and scrapping stage of specific equipment in the power grid infrastructure project; , j=1,2,…n, is the number of n types of equipment required for the infrastructure project, , j=1,2,…n, is the unit cost of the corresponding equipment; , j=1,2,…n, is the average annual operation and maintenance cost of a single device; , j=1,2,…n, is the equipment residual cost rate; N is the equipment life span, i is the discount rate; Step 2: Use the linear programming method to find an integer feasible solution for S and obtain the objective function value ,by Indicates the optimal cost of equipment solution selection S, then there is , and iterate; Step 3: Select any variable that does not meet the integer condition in the optimal solution of S , whose value is ,by[ ] means less than The maximum integer, constrained by the condition Add to S as a constraint for the subsequent planning problem S1, and set the conditional constraint Add to S as the solution constraint condition of the subsequent planning problem S2, and use the linear programming method to solve the subsequent planning problems S1 and S2 respectively; Step 4: If the optimal objective function of each branch is greater than j, then cut off this branch. If it is less than j and does not meet the integer condition, repeat step 2 until , and get the optimal solution ; S2. Obtain the AHP subjective weight of each indicator according to the evaluation system; S3, obtain the objective weight of each indicator through the pre-trained random forest algorithm; The training of the random forest algorithm in step S3 includes the following steps: S31. Perform Bootstrap sampling using random forest to generate K independent decision trees by extracting K sample data sets, where each sample data set represents a full life cycle indicator system for a power grid infrastructure project. S32, let k = 1, train decision tree , the training input is the kth data set, calculate the kth data set, and calculate the accuracy of the kth out-of-bag data set ; S33, rearrange the features f in the out-of-bag dataset and calculate the accuracy ; S34. For all sample data sets k = 2, 3 ... K, execute steps S32 and S33; S35. Calculate the classification accuracy error after rearrangement: ； S36. For each feature f, calculate the influence of feature f on the accuracy of out-of-bag data: ； And the variance of the impact degree: ； Then calculate the importance of feature f : ； Get the importance of all features ; S4. Calculate the combined weight of each indicator based on the AHP subjective weight and the objective weight of each indicator; S5. Calculate the comprehensive indicator score of each indicator and the comprehensive project score for the entire life cycle of the project based on the indicator value, benchmark value and combined weight of each indicator.

2. A method for evaluating the entire life cycle of a power grid infrastructure project according to claim 1, characterized in that: The step S2 comprises the steps of: S21, establish judgment matrix scale; S22. Obtaining the expert importance comparison scores according to the matrix scale and generating a judgment matrix; S23, normalizing the judgment matrix to obtain a standard judgment matrix; S24. Sum the rows of the standard judgment matrix and normalize the row sums to obtain the AHP subjective weight of each indicator.

3. A method for evaluating the entire life cycle of a power grid infrastructure project according to claim 2, characterized in that: The judgment matrix is: A=[ a ij ] n×n ； Normalizing the judgment matrix by column is specifically as follows: ； Sum the obtained standard judgment matrix row by row to get the sum of each row of the standard judgment matrix: Normalize the sum of each row of the standard matrix to obtain the AHP subjective weight of each indicator : ； Among them, n represents the number of indicators, Represents the elements of the normalized matrix.

4. A method for evaluating the entire life cycle of a power grid infrastructure project according to claim 2, characterized in that: The step S2 further comprises the steps of: S25. Calculate the consistency index CI: ； Where n represents the number of indicators, represents the maximum eigenvalue of the judgment matrix, The calculation formula is as follows: ； Among them, W i represents the AHP subjective weight of each indicator, Represents the value of the i-th row and j-th column in the judgment matrix, i <n,j<n; S26, obtaining a random consistency index RI by looking up a table according to the order of the judgment matrix; S27. Calculate the consistency ratio: ； If the CR value is less than the preset threshold, the judgment matrix passes the consistency test.

5. A method for evaluating the entire life cycle of a power grid infrastructure project according to claim 1, characterized in that: The calculation of the combination weight is specifically as follows: ； Among them, n represents the number of indicators, W j represents the combined weight of indicator j, W j 1 and W j 2 They represent the subjective weight and objective weight of indicator j respectively.

6. A method for evaluating the entire life cycle of a power grid infrastructure project according to claim 1, characterized in that: The step S5 is specifically as follows: and dividing the index value of each index by the reference value, performing normalization processing on the index value of each index to unify the index dimension, and obtaining a second index value of each index; For each indicator, multiplying the second indicator value by the combination weight to obtain the indicator comprehensive score of each indicator; Add up the comprehensive scores of all the indicators to obtain the comprehensive score of the project.

7. A method for evaluating the entire life cycle of a power grid infrastructure project according to claim 1, characterized in that: The evaluation system for the entire project life cycle includes indicators such as planning capability, feasibility study, survey fees, basic design fees, other design fees, bidding work, contract content, execution evaluation, equipment data, construction and installation engineering costs, equipment purchase costs, other costs, dynamic costs, energy consumption costs, environmental costs, other costs, maintenance costs, repair costs, labor costs, insurance premiums, power outage time, repair costs, electricity value coefficient, average failure rate, scrap disposal cost, equipment residual value, line loss rate, comprehensive voltage qualification rate, power supply reliability rate, incremental investment economic internal rate of return, incremental capital return rate, incremental investment financial net present value, incremental investment return rate, management mechanism evaluation, incentive mechanism evaluation, N-1 pass rate, overload line ratio, and continuous safe operation days; Among them, other design fees are fees charged according to the relevant needs of engineering design or the relevant regulations of the client; Other expenses refer to other related expenses necessary to complete the construction of the project but not including construction costs, installation costs and equipment purchase costs; Other expenses refer to the expenses required for the annual project operation during the operation and maintenance phase, in addition to labor costs, energy costs and environmental fees.

8. A terminal for evaluating the entire life cycle of a power grid infrastructure project, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for evaluating the entire life cycle of a power grid infrastructure project described in any one of claims 1 to 7 are implemented.

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