Power distribution network project consistency comprehensive evaluation method based on quantitative evaluation

Abstract

The invention provides a power distribution network project consistency comprehensive evaluation method based on quantitative evaluation. The method comprises the following steps: step 1, obtaining basic data of a power distribution network project; based on the basic data, constructing a power distribution network project consistency evaluation index system; 2, performing quantitative processing on each subdivision evaluation index; 3, according to a weighted calculation result, calculating a consistency comprehensive evaluation score of each power distribution network project; and verifying the sorting result and the actual operation condition of the power distribution network project, and if the sorting result is inconsistent with the actual operation condition, adjusting a weighted calculation mode or a fuzzy evaluation method in the evaluation index system so as to enable the evaluation result to better fit the actual operation condition. According to the technical scheme, the quantitative index and the qualitative index are quantified by adopting the normalization processing and the fuzzy evaluation method respectively, so that the influence of subjective factors is reduced, and the objectivity and credibility of the evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network projects, and in particular to a comprehensive evaluation method for distribution network project consistency based on quantitative evaluation. Background Art

[0002] With the rapid development of the power industry, the number and complexity of distribution network projects are increasing. As a key link in the power system connecting users and transmission networks, the operation of the distribution network directly affects the overall stability and power supply quality of the power system. The implementation of distribution network projects usually involves comprehensive considerations in multiple dimensions, including economic, technical, operational reliability and environmental adaptability.

[0003] The existing distribution network project evaluation methods have the following shortcomings: first, the evaluation index system is not comprehensive enough and fails to fully consider the specific characteristics of different projects, resulting in one-sided evaluation results; second, the qualitative indicators in the evaluation process are difficult to quantify, and the subjective judgment of experts often leads to greater uncertainty in the evaluation results; finally, there is a lack of a scientific weighted calculation mechanism, and the importance of each evaluation dimension cannot be reasonably reflected, which affects the final ranking and decision-making. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a comprehensive evaluation method for distribution network project consistency based on quantitative evaluation, which adopts normalization processing and fuzzy evaluation method to quantify quantitative indicators and qualitative indicators respectively, reduces the influence of subjective factors, and improves the objectivity and credibility of the evaluation results.

[0005] To achieve the above object, the present invention adopts the following technical solution: a comprehensive evaluation method for distribution network project consistency based on quantitative evaluation, comprising the following steps: Step 1: Obtain basic data of the distribution network project, the basic data including the geographic information of the project, the structural information of the distribution network, historical operation data, equipment parameters, load distribution and scheduling plan data; based on the basic data, construct a distribution network project consistency evaluation index system, the evaluation index system includes multiple evaluation dimensions, the evaluation dimensions at least include the economic evaluation dimension, technical evaluation dimension, operation reliability evaluation dimension and environmental adaptability evaluation dimension of the project, each evaluation dimension contains multiple subdivision evaluation indicators, the subdivision evaluation indicators include quantitative indicators and qualitative indicators; the quantitative indicators are calculated based on the historical operation data and equipment parameters of the distribution network project, and the qualitative indicators are obtained by expert evaluation of the scheduling plan data and load distribution data of the distribution network project; Step 2: quantify each subdivided evaluation index, wherein the quantitative index is normalized, and the normalization includes calculating the relative value of each subdivided evaluation index according to the maximum and minimum values; the qualitative index is quantified by a fuzzy evaluation method, and the fuzzy evaluation method includes assigning fuzzy membership to the qualitative index by expert scoring, and converting the obtained membership value into a corresponding quantitative value; based on the normalized quantitative index and the qualitative index processed by the fuzzy evaluation method, weighted calculation is performed on the multiple evaluation dimensions, and the weighted calculation includes assigning different weight values ​​according to the importance of each evaluation dimension to the consistency of the distribution network project, and the weight value is determined by expert scoring or empirical analysis of historical data; Step 3: Based on the result of the weighted calculation, calculate the consistency comprehensive evaluation score of each distribution network project, the consistency comprehensive evaluation score is formed by the weighted scores of each evaluation dimension; perform a multi-project comparative analysis on the consistency comprehensive evaluation score, the comparative analysis includes sorting multiple distribution network projects based on a set evaluation threshold, and the sorting result is used to determine the optimal distribution network project; verify the sorting result with the actual operation of the distribution network project, the verification includes comparing the sorting result with the economic benefits, technical performance, operation stability and environmental impact of the actual operation of the project. If the sorting result is inconsistent with the actual operation, adjust the weighted calculation method or fuzzy evaluation method in the evaluation index system to make the evaluation result more in line with the actual operation.

[0006] In a preferred embodiment, the basic data is collected in real time or regularly from different data sources through a multi-source data collection interface, and the multi-source data collection interface includes a geographic information system interface for collecting geographic information, a power grid topology data interface for collecting distribution network structure information, an operation monitoring system interface for collecting historical operation data, an equipment management system interface for collecting equipment parameters, a load monitoring system interface for collecting load distribution data, and a dispatch management system interface for collecting dispatch plan data; The acquired basic data is cleaned and preprocessed, and the data cleaning and preprocessing include removing invalid data, correcting abnormal data and filling missing data. The data cleaning and preprocessing are achieved by an abnormal data detection algorithm based on machine learning, and the abnormal data detection algorithm identifies and corrects abnormal data based on historical data patterns and equipment parameter models.

[0007] In a preferred embodiment, the quantitative indicators of the economic evaluation dimension in step 1 include: return on investment, cost-effectiveness ratio, and asset utilization rate; the qualitative indicators of the economic evaluation dimension include: project feasibility, financing difficulty, and economic development driving force; The quantitative indicators of the technical evaluation dimension include: power supply reliability, voltage qualification rate, and line loss rate; the qualitative indicators of the technical evaluation dimension include: technological advancement, equipment compatibility, and system scalability; The quantitative indicators of the operational reliability evaluation dimension include: failure rate, mean repair time, and N-1 verification pass rate; the qualitative indicators of the operational reliability evaluation dimension include: emergency response capability, maintenance convenience, and anti-interference capability; The quantitative indicators of the environmental adaptability evaluation dimension include: carbon emissions, noise levels, and electromagnetic radiation intensity; the qualitative indicators of the environmental adaptability evaluation dimension include: environmental friendliness, landscape coordination, and climate adaptability.

[0008] In a preferred embodiment, in step 2, the quantitative index normalization process specifically includes the following steps: Determine the maximum and minimum values ​​of indicators: Based on historical data, determine the maximum and minimum values ​​of each quantitative indicator; Calculate the normalized score of the current project indicator: Use the maximum and minimum normalization method to convert the indicator value of the current project into a score between 0 and 1. Specifically, subtract the minimum value from the current value, and then divide it by the difference between the maximum and minimum values; Repeat the above steps for all quantitative indicators: perform the same normalization process on all quantitative indicators in the consistency evaluation indicator system.

[0009] In a preferred embodiment, in step 2, the quantification of the qualitative indicators using the fuzzy evaluation method specifically includes the following steps: Formulate multi-level scoring standards: Based on the comprehensive analysis of the actual operation of the distribution network project and historical data, formulate multi-level scoring standards suitable for each qualitative indicator; According to the multi-level scoring criteria, each qualitative indicator is assigned a membership degree at different evaluation levels; Calculate the average membership of each qualitative indicator at different evaluation levels; Use the fuzzy comprehensive evaluation model to convert the fuzzy membership degree into the corresponding quantitative score; Repeat the above steps for all qualitative indicators: Perform the same fuzzy evaluation process on all qualitative indicators in the consistency evaluation indicator system.

[0010] In a preferred embodiment, the weighted calculation of the multiple evaluation dimensions in step 2 specifically includes: determining the initial weight: preliminarily determining the weight of each dimension based on historical data analysis; weight correction: using the Delphi method to obtain adjusted weights through multiple rounds of anonymous questionnaires; weight application: applying the corrected weights to the score calculation of each dimension.

[0011] In a preferred embodiment, the calculation of the comprehensive consistency evaluation score of each distribution network project in step 3 specifically includes: determining the indicator weight: determining the weight of each detailed evaluation indicator through historical data analysis or expert scoring; weighted calculation: multiplying the quantitative score of each indicator by its corresponding weight, and then summing them to obtain the final comprehensive consistency evaluation score.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. By acquiring multi-dimensional basic data and constructing an indicator system that includes multiple evaluation dimensions such as economy, technology, operational reliability and environmental adaptability, a comprehensive evaluation of the distribution network project is achieved, avoiding the one-sidedness of single indicator evaluation. Normalization processing and fuzzy evaluation methods are used to quantify quantitative indicators and qualitative indicators respectively, reducing the influence of subjective factors and improving the objectivity and credibility of the evaluation results.

[0013] 2. Determine the weight values ​​of different evaluation dimensions through expert scoring or historical data analysis, so that the evaluation system can be flexibly adjusted according to actual conditions and adapt to the characteristics of distribution network projects of different types or regions. Use a weighted calculation method to combine the scores of each evaluation dimension to obtain a consistent comprehensive evaluation score, making the evaluation results more accurate and comprehensive. By setting evaluation thresholds to rank multiple projects, a horizontal comparison between different distribution network projects is achieved, making it easier to determine the optimal project.

[0014] 3. By verifying the ranking results with the actual operation conditions and adjusting the evaluation system according to the verification results, the evaluation method has the ability to self-optimize and adapt, ensuring that the evaluation results are consistent with the actual situation. It provides a scientific decision-making basis for the planning, construction and operation of distribution network projects, and helps to improve resource allocation efficiency and project management level. Through the quantitative and automated evaluation process, the efficiency of distribution network project evaluation is greatly improved, saving manpower and time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flow chart of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0017] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0018] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0019] Comprehensive evaluation method for distribution network project consistency based on quantitative evaluation, refer to Figure 1 , including the following steps: Step S1. Obtain basic data of the distribution network project, the basic data including but not limited to the geographic information of the project, the structural information of the distribution network, historical operation data, equipment parameters, load distribution and scheduling plan data; based on the basic data, construct a distribution network project consistency evaluation index system, the evaluation index system includes multiple evaluation dimensions, the evaluation dimensions at least include the economic evaluation dimension, technical evaluation dimension, operation reliability evaluation dimension and environmental adaptability evaluation dimension of the project, each evaluation dimension includes multiple subdivision evaluation indicators, the subdivision evaluation indicators include quantitative indicators and qualitative indicators; Step S2. quantify each subdivided evaluation index, wherein the quantitative index is normalized, and the normalization includes calculating the relative value of each subdivided evaluation index according to the maximum and minimum values; the qualitative index is quantified by a fuzzy evaluation method, and the fuzzy evaluation method includes assigning fuzzy membership to the qualitative index by expert scoring, and converting the obtained membership value into a corresponding quantitative value; based on the normalized quantitative index and the qualitative index processed by the fuzzy evaluation method, weighted calculation is performed on the multiple evaluation dimensions, and the weighted calculation includes assigning different weight values ​​according to the importance of each evaluation dimension to the consistency of the distribution network project, and the weight value is determined by expert scoring or empirical analysis of historical data; Step S3. Calculate the comprehensive evaluation score of consistency of each distribution network project according to the result of the weighted calculation, wherein the comprehensive evaluation score of consistency is formed by the weighted scores of each evaluation dimension; perform a multi-project comparative analysis on the comprehensive evaluation score of consistency, wherein the comparative analysis includes sorting multiple distribution network projects based on a set evaluation threshold, and the sorting result is used to determine the optimal distribution network project; verify the sorting result with the actual operation of the distribution network project, wherein the verification includes comparing the sorting result with the economic benefits, technical performance, operation stability and environmental impact of the actual operation of the project; if the sorting result is inconsistent with the actual operation, adjust the weighted calculation method or fuzzy evaluation method in the evaluation index system to make the evaluation result more in line with the actual operation.

[0020] In step 1, basic data of the distribution network project is obtained, and based on the basic data, a distribution network project consistency evaluation index system is constructed, including: Obtain basic data of the distribution network project, the basic data including geographic information of the project, structural information of the distribution network, historical operation data, equipment parameters, load distribution and scheduling plan data, the basic data is collected in real time or regularly from different data sources through a multi-source data collection interface, the multi-source data collection interface includes a geographic information system interface for collecting geographic information, a power grid topology data interface for collecting distribution network structural information, an operation monitoring system interface for collecting historical operation data, an equipment management system interface for collecting equipment parameters, a load monitoring system interface for collecting load distribution data and a scheduling management system interface for collecting scheduling plan data; Performing data cleaning and preprocessing on the acquired basic data, wherein the data cleaning and preprocessing include removing invalid data, correcting abnormal data, and filling missing data, wherein the data cleaning and preprocessing are implemented by an abnormal data detection algorithm based on machine learning, wherein the abnormal data detection algorithm identifies and corrects abnormal data based on historical data patterns and equipment parameter models; Based on the basic data after data cleaning and preprocessing, a distribution network project consistency evaluation index system is constructed, and the consistency evaluation index system includes multiple dimensions, and the multiple dimensions include at least an economic dimension, a technical dimension, an operation reliability dimension, and an environmental adaptability dimension. Each dimension includes multiple quantitative indicators and qualitative indicators. The quantitative indicators are calculated based on the historical operation data and equipment parameters of the distribution network project, and the qualitative indicators are obtained by expert evaluation of the dispatch plan data and load distribution data of the distribution network project. The quantitative indicators and qualitative indicators in the consistency evaluation index system are quantified, and the quantification processing includes normalizing the quantitative indicators and allocating fuzzy membership to the qualitative indicators. The normalization processing calculates the relative value of each quantitative indicator based on the maximum and minimum values, and the fuzzy membership allocation quantifies the fuzzy evaluation results of the qualitative indicators based on the expert scoring system.

[0021] Obtain basic data of distribution network projects First, the basic data of the distribution network project is obtained through the multi-source data acquisition interface. These data include the project's geographic information, distribution network structure information, historical operation data, equipment parameters, load distribution and scheduling plan data.

[0022] The specific implementation is as follows: Geographic Information System Interface: Use GIS (Geographic Information System) technology to obtain spatial information of the distribution network project. For example, the longitude and latitude coordinates of the substation (such as 30.5432°N, 114.3456°E), the direction of the distribution line (such as the straight-line distance from point A (30.5432°N, 114.3456°E) to point B (30.5678°N, 114.3789°E) is 2.5 kilometers).

[0023] Grid topology data interface: Using graph database technology, store and obtain the structural information of the distribution network. For example, a 10kV distribution line contains 1 substation outgoing line switch, 5 section switches, 10 junction boxes, and 50 distribution transformers.

[0024] Operation monitoring system interface: Through the SCADA (Supervisory Control and Data Acquisition) system, the operation parameters of the distribution network are collected in real time. For example, the voltage of a 10kV distribution line is 10.2kV, the current is 100A, the active power is 1.77MW, and the reactive power is 0.6Mvar.

[0025] Equipment management system interface: Use the asset management system to obtain detailed parameters of distribution network equipment. For example, the rated capacity of a distribution transformer is 400kVA, the rated voltage is 10 / 0.4kV, the short-circuit impedance is 4%, the no-load loss is 0.65kW, and the load loss is 4.6kW.

[0026] Load monitoring system interface: Obtain load distribution data through smart meters and power consumption information collection systems. For example, the maximum daily load of a distribution substation is 350kW, which occurs at 14:30, and the minimum load is 50kW, which occurs at 3:00 in the morning.

[0027] Dispatching management system interface: Obtain dispatching plan data from the dispatching automation system. For example, it is planned to overhaul a 10kV line from 9:00 to 11:00 on July 15, requiring a 200kW load transfer.

[0028] Data cleaning and preprocessing The acquired basic data is cleaned and preprocessed to ensure the quality and availability of the data.

[0029] The specific implementation steps are as follows: Data integrity check: Check the integrity of various data and mark missing data. For example, it is found that the load data of a distribution transformer is missing from June 1 to June 3.

[0030] Abnormal data detection: Use machine learning-based abnormal data detection algorithms, such as the Isolation Forest algorithm, to identify abnormal data. For example, it is detected that the voltage of a 10kV line suddenly drops to 8.5kV at a certain moment, which is significantly lower than the normal operating voltage range (9.7kV-10.3kV).

[0031] Data correction: Correct the detected abnormal data. For example, for the abnormal voltage data above, the average value of the previous and next time points can be used to replace it, and the corrected voltage value is 10.1kV.

[0032] Data filling: fill in missing data. For example, for missing distribution transformer load data, the average value of historical data in the same week and time period can be used to fill in the missing data.

[0033] Data standardization: Convert data of different dimensions to a unified scale. For example, convert voltage data (kV) and current data (A) to standardized values ​​between 0 and 1.

[0034] Constructing a consistency evaluation index system for distribution network projects Based on the cleaned and preprocessed basic data, a distribution network project consistency evaluation index system is constructed. The system includes four dimensions: economy, technology, operation reliability and environmental adaptability, and each dimension contains multiple quantitative and qualitative indicators.

[0035] The specific indicator system is as follows: Economic dimension: Quantitative indicators: return on investment, cost-effectiveness ratio, asset utilization Qualitative indicators: project feasibility, financing difficulty, economic development driving force Technical dimensions: Quantitative indicators: power supply reliability, voltage qualification rate, line loss rate Qualitative indicators: technological advancement, equipment compatibility, system scalability Operation reliability dimensions: Quantitative indicators: failure rate, mean repair time, N-1 verification pass rate Qualitative indicators: emergency response capability, maintenance convenience, anti-interference capability Environmental adaptability dimensions: Quantitative indicators: carbon emissions, noise levels, electromagnetic radiation intensity Qualitative indicators: environmental friendliness, landscape coordination, climate adaptability Quantitative processing of indicators Each indicator in the constructed indicator system is quantified so that it can be mathematically calculated and compared.

[0036] The specific implementation steps are as follows: Normalization of quantitative indicators: Use the range normalization method to convert quantitative indicators into values ​​between 0 and 1. Subtract the minimum value from the original value, and then divide it by the difference between the maximum and minimum values ​​to get the normalized result.

[0037] For example, the return on investment of a project is 12%. It is known that the lowest return on investment of all projects is 5% and the highest is 20%. Then the normalized value of the return on investment of this project is 0.4667.

[0038] Fuzzy evaluation processing of qualitative indicators: Qualitative indicators are evaluated by expert scoring and converted into fuzzy membership values.

[0039] For example, when evaluating the "technological advancement" indicator, the expert scoring standard is: Very advanced: 0.8-1.0 Moderately advanced: 0.6-0.8 Average: 0.4-0.6 Moderately backward: 0.2-0.4 Very backward: 0-0.2 Suppose five experts score the technological advancement of a project, and the scores are 0.85, 0.78, 0.82, 0.76, and 0.80 respectively. The final score of the project's technological advancement index is the average of these five scores, which is 0.802.

[0040] Weighted calculation of evaluation dimensions The four evaluation dimensions are weighted to reflect the importance of each dimension in the overall evaluation.

[0041] The specific implementation steps are as follows: Determine the initial weight: Based on historical data analysis, preliminarily determine the weight of each dimension. For example: Economic dimension: 30% Technical dimension: 25% Operational reliability dimension: 25% Environmental adaptability dimension: 20% Expert revision: Organize experts to revise the initial weights. Use the Delphi method and conduct multiple rounds of anonymous questionnaires to reach a consensus.

[0042] Assume that after the experts' revision, the weights of each dimension are adjusted as follows: Economic dimension: 28% Technical dimension: 27% Operational reliability dimension: 26% Environmental adaptability dimension: 19% Weight application: Apply the revised weights to the score calculation of each dimension.

[0043] Calculation of project consistency comprehensive evaluation score Based on the scores and weights of each dimension, the comprehensive evaluation score of the distribution network project's consistency is calculated.

[0044] The calculation steps are as follows: Calculate the score of each dimension: Take the weighted average of the quantitative scores of each indicator in each dimension to obtain the total score of the dimension.

[0045] For example, the economic dimension includes 6 indicators. Assuming that the weight of each indicator is equal, the scores of a project on these 6 indicators are 0.85, 0.78, 0.92, 0.80, 0.88, and 0.83 respectively. Then the score of the project in the economic dimension is 0.8433.

[0046] Calculate the comprehensive evaluation score: Multiply the score of each dimension by its corresponding weight, and then sum them up to get the comprehensive evaluation score of the project's consistency.

[0047] Assume that the scores of a project in four dimensions are: Economic dimension: 0.8433 Technical dimension: 0.7950 Operational reliability dimension: 0.8620 Environmental adaptability dimension: 0.7780 The comprehensive evaluation score of the consistency of this project is 0.8219.

[0048] Project ranking and comparative analysis Based on the calculated comprehensive consistency evaluation scores, multiple distribution network projects are ranked and compared and analyzed.

[0049] The specific implementation steps are as follows: Set an evaluation threshold: Based on historical project data and expert experience, set an evaluation threshold to determine whether the project meets the qualification standards. For example, set the evaluation threshold to 0.75.

[0050] Project screening: Compare the comprehensive consistency evaluation score of each project with the evaluation threshold to screen out qualified projects.

[0051] For example, there are five distribution network projects A, B, C, D, and E, whose comprehensive consistency evaluation scores are 0.8219, 0.7890, 0.8120, 0.7450, and 0.8350 respectively. According to the set threshold of 0.75, projects A, B, C, and E are qualified projects, and project D is unqualified.

[0052] Project ranking: Qualified projects will be arranged in descending order according to their comprehensive consistency evaluation scores.

[0053] According to the above example, the ranking of qualified projects is: E>A>C>B Determine the best project: The project with the highest score in the sorting results is the best project, in this case, it is project E.

[0054] Result feedback: Feedback the ranking results to the distribution network project management system to support decision-making. For example, the implementation of project E can be prioritized and more resources can be allocated to it; for unqualified project D, in-depth analysis and improvement are required.

[0055] Verification of evaluation results and optimization of indicator system Compare the evaluation results with the actual operation of the project to verify the effectiveness of the evaluation method and optimize the indicator system based on the comparison results.

[0056] The specific implementation steps are as follows: Tracking project implementation results: Conduct follow-up surveys on implemented projects and collect actual operation data. For example, record key indicator data such as actual return on investment, power supply reliability, number of failures, and carbon emissions of Project E within one year after commissioning.

[0057] Comparative analysis: Compare and analyze the actual operation data of the project with the evaluation results. For example, if the actual return on investment of Project E is 15%, which is higher than the expected 12% during the evaluation, it means that the evaluation of the economic dimension may be conservative.

[0058] Identify the reasons for the discrepancy: Analyze the reasons why the evaluation results differ from the actual situation. Possible reasons include improper indicator selection, unreasonable weight setting, data quality issues, etc.

[0059] Optimize the indicator system: Optimize the indicator system based on the analysis results. Possible optimization measures include: Adjust indicator weights: If it is found that the actual impact of the economic dimension is greater than expected, its weight can be appropriately increased. Increase or decrease indicators: If it is found that some important factors are not taken into account, the corresponding indicators can be added; for indicators with strong correlation, they can be merged or deleted. Improve the quantitative method: If the quantitative results of some qualitative indicators deviate greatly from the actual situation, their quantitative methods can be improved.

[0060] Continuous Improvement: Review and improve evaluation methods regularly (e.g. annually) to ensure their continued effectiveness.

[0061] Through the above detailed steps, this method realizes a comprehensive and objective consistency evaluation of distribution network projects, providing strong support for project decision-making and management. At the same time, through continuous verification and optimization, the accuracy and applicability of the evaluation method are guaranteed.

[0062] In an optional embodiment, Each subdivided evaluation index is quantified, wherein the quantitative index is normalized, and the normalization includes calculating the relative value according to the maximum and minimum values ​​of each subdivided evaluation index; the qualitative index is quantified by the fuzzy evaluation method, and the fuzzy evaluation method includes assigning fuzzy membership to the qualitative index by expert scoring, and converting the obtained membership value into the corresponding quantitative value, including: Quantify each subdivided evaluation indicator in the consistency evaluation indicator system of the distribution network project, and the subdivided evaluation indicator includes quantitative indicators and qualitative indicators; normalize the quantitative indicators, and the normalize processing includes calculating the relative value of each quantitative indicator according to the maximum and minimum values ​​of each quantitative indicator, specifically: for each quantitative indicator, based on its maximum and minimum values ​​in historical data, calculate the normalized score of the quantitative indicator in the current distribution network project; The qualitative indicators are quantified by a fuzzy evaluation method, and the fuzzy evaluation method includes the following steps: assigning a fuzzy membership to each qualitative indicator by expert scoring, wherein the expert scoring is performed by a multi-level scoring standard, and the multi-level scoring standard is obtained based on a comprehensive analysis of the actual operation of the distribution network project and historical data; determining the fuzzy membership of each qualitative indicator according to the expert scoring result, wherein the fuzzy membership represents the membership of the qualitative indicator at different evaluation levels; converting the fuzzy membership into a corresponding quantitative value, wherein the quantitative value is calculated by a fuzzy comprehensive evaluation model, wherein the fuzzy comprehensive evaluation model includes sequentially performing weighted average processing and normalization processing on the fuzzy membership, and finally generating a quantitative score for the qualitative indicator; The normalized quantitative index scores and the qualitative index scores processed by the fuzzy evaluation method are input into the consistency comprehensive evaluation model for weighted calculation. The weighted calculation is based on the weight of each subdivided evaluation index, and the weight is determined by historical data analysis or expert scoring.

[0063] First, it is necessary to prepare all the subdivided evaluation index data in the consistency evaluation index system of the distribution network project. These indicators include quantitative indicators and qualitative indicators.

[0064] Examples of quantitative indicators: 1. Return on investment (%) 2. Power supply reliability rate (%) 3. Line loss rate (%) 4. Equipment utilization rate (%) 5. Failure recovery time (hours) Examples of qualitative indicators: 1. Technological advancement 2. Environmental friendliness 3. System Scalability 4. Security 5. Social Benefits For each indicator, you need to collect historical data and relevant data of the current project. For example, for the quantitative indicator "return on investment", you may need to collect the return on investment data of all similar projects in the past five years, as well as the expected return on investment of the current evaluation project.

[0065] Normalization of quantitative indicators The quantitative indicators are normalized so that indicators of different dimensions can be compared and calculated comprehensively.

[0066] The specific implementation steps are as follows: Determine the maximum and minimum values ​​of the indicator: Based on historical data, determine the maximum and minimum values ​​of each quantitative indicator. For example, for the "Return on Investment" indicator, assume that the maximum value in the historical data is 25% and the minimum value is 5%.

[0067] Calculate the normalized score of the current project indicator: Use the maximum and minimum normalization method to convert the indicator value of the current project into a score between 0 and 1. The calculation formula is: Normalized score = (current value - minimum value) / (maximum value - minimum value).

[0068] Taking "return on investment" as an example, assuming that the expected return on investment of the current project is 15%, its normalized score is: (15% - 5%) / (25% - 5%) = 0.5.

[0069] Repeat the above steps for all quantitative indicators: Perform the same normalization process on all quantitative indicators in the consistency evaluation indicator system. For example: Power supply reliability: current value 99.9%, maximum value 99.99%, minimum value 99%, normalized score 0.9091 Line loss rate: current value 6%, maximum value 10%, minimum value 4%, normalized score 0.6667 Equipment utilization: current value 75%, maximum value 90%, minimum value 50%, normalized score 0.625 Fault recovery time: current value 2 hours, maximum value 8 hours, minimum value 0.5 hours, normalized score 0.8 Fuzzy evaluation processing of qualitative indicators The fuzzy evaluation method is used to quantify the qualitative indicators, converting the subjective evaluation of experts into objective quantitative scores.

[0070] The specific implementation steps are as follows: Formulate multi-level scoring standards: Based on a comprehensive analysis of the actual operation of the distribution network project and historical data, formulate a multi-level scoring standard suitable for each qualitative indicator. For example, for the "technical advancement" indicator, the following five-level scoring standard can be formulated: Very advanced: 0.8-1.0 Moderately advanced: 0.6-0.8 Average: 0.4-0.6 Moderately backward: 0.2-0.4 Very backward: 0-0.2 Expert scoring: Invite multiple field experts to score each qualitative indicator. Each expert assigns a degree of membership to each qualitative indicator at different evaluation levels based on a multi-level scoring standard.

[0071] For example, for the "technological advancement" indicator, suppose there are three experts scoring, and their scoring results are as follows: Expert 1: Very advanced (0.7), relatively advanced (0.3), average (0), relatively backward (0), very backward (0) Expert 2: Very advanced (0.6), relatively advanced (0.4), average (0), relatively backward (0), very backward (0) Expert 3: Very advanced (0.8), relatively advanced (0.2), average (0), relatively backward (0), very backward (0) Determine the fuzzy membership: Based on the expert scoring results, calculate the average membership of each qualitative indicator at different evaluation levels. Continuing with "technical advancement" as an example: Very advanced: (0.7 + 0.6 + 0.8) / 3 = 0.7 Moderately advanced: (0.3 + 0.4 + 0.2) / 3 = 0.3 Average: (0 + 0 + 0) / 3 = 0 Moderately backward: (0 + 0 + 0) / 3 = 0Very backward: (0 + 0 + 0) / 3 = 0 Calculate the quantitative score: Use the fuzzy comprehensive evaluation model to convert the fuzzy membership degree into the corresponding quantitative score. The specific steps are as follows: The fuzzy membership is weighted averaged, and the weights can be set according to the importance of the evaluation level. For example, the weights can be set to: [1.0, 0.8, 0.6, 0.4, 0.2].

[0072] Calculate the weighted average: 0.7 * 1.0 + 0.3 * 0.8 + 0 * 0.6 + 0 * 0.4 + 0 * 0.2 = 0.94 The weighted average is normalized to obtain the final quantitative score. In this case, since the weighted average is already in the range of 0-1, no normalization is required. Therefore, the final quantitative score of the "technological advancement" indicator is 0.94.

[0073] Repeat the above steps for all qualitative indicators: Perform the same fuzzy evaluation process on all qualitative indicators in the consistency evaluation indicator system. For example: Environmental friendliness: Quantitative score 0.85 System scalability: Quantitative score 0.78 Security: Quantitative score 0.92 Social benefits: Quantitative score 0.88 Comprehensive evaluation calculation The normalized quantitative index scores and the qualitative index scores processed by the fuzzy evaluation method are input into the consistency comprehensive evaluation model for weighted calculation.

[0074] The specific implementation steps are as follows: Determine the weight of indicators: Determine the weight of each sub-evaluation indicator through historical data analysis or expert scoring. For example: Return on investment: weight 0.15 Power supply reliability: weight 0.20 Line loss rate: weight 0.10 Equipment utilization: weight 0.10 Fault recovery time: weight 0.10 Technological advancement: weight 0.10 Environmental friendliness: weight 0.05 System scalability: weight 0.05 Security: weight 0.10 Social benefits: weight 0.05 Weighted calculation: Multiply the quantitative score of each indicator by its corresponding weight, and then sum them up to obtain the final consistency comprehensive evaluation score.

[0075] The calculation process is as follows: 0.5 * 0.15 + 0.9091 * 0.20 + 0.6667 * 0.10 + 0.625 * 0.10 + 0.8 *0.10 + 0.94 * 0.10 + 0.85 * 0.05 + 0.78 * 0.05 + 0.92 * 0.10 + 0.88 * 0.05 = 0.7668 Therefore, the comprehensive evaluation score of the distribution network project's consistency is 0.7668.

[0076] Interpretation and application of evaluation results Based on the calculated comprehensive consistency evaluation score, distribution network projects are evaluated and decision support is provided.

[0077] The specific implementation steps are as follows: Develop evaluation criteria: Based on historical project data and expert experience, develop evaluation criteria. For example: Excellent: 0.9-1.0 Good: 0.8-0.9 Average: 0.7-0.8 Poor: 0.6-0.7 Unqualified: 0-0.6 Result interpretation: Compare the project's consistency comprehensive evaluation score with the evaluation criteria to draw an evaluation conclusion. In this case, the score is 0.7668, which is at the "average" level.

[0078] Application of results: Provide support for project decision-making based on the evaluation results. For example: For projects at the "average" level, targeted improvements can be recommended. Based on the scores of each sub-indicator, it can be found that the project has room for improvement in terms of return on investment, line loss rate and equipment utilization. It is recommended that the project team focus on these aspects and formulate improvement measures.

[0079] At the same time, the project performs well in terms of technological advancement, safety and power supply reliability, and these advantages can be further developed and promoted.

[0080] Evaluation method optimization By continuously collecting and analyzing project data, the evaluation methods are continuously optimized to improve their accuracy and applicability.

[0081] The specific implementation steps are as follows: Data accumulation: Continuously collect actual operating data of the evaluated projects, including the actual performance of various indicators.

[0082] Result comparison: Compare and analyze the actual operation results of the project with the evaluation scores to identify possible deviations in the evaluation method.

[0083] Method adjustment: According to the comparative analysis results, the evaluation method is adjusted and optimized. Possible adjustments include: Update the indicator system: add or delete certain evaluation indicators according to new technological developments and industry trends. Adjust indicator weights: appropriately adjust the weights of each indicator according to its actual importance. Optimize quantitative methods: improve the fuzzy evaluation method of qualitative indicators and improve its accuracy.

[0084] For example, if it is found that the importance of the "system scalability" indicator in actual project operation exceeds expectations, you can consider increasing its weight from 0.05 to 0.08, and adjust the weights of other indicators accordingly.

[0085] Through the above detailed steps, this method realizes the comprehensive quantitative processing of the consistency evaluation indicators of distribution network projects, providing an objective and reliable basis for project evaluation and decision-making. At the same time, through continuous data accumulation and method optimization, the dynamic adaptability and long-term effectiveness of the evaluation method are ensured.

[0086] In an optional embodiment, Based on the quantitative indicators processed by the normalization and the qualitative indicators processed by the fuzzy evaluation method, the multiple evaluation dimensions are weightedly calculated, and the weighted calculation includes assigning different weight values ​​according to the importance of each evaluation dimension to the consistency of the distribution network project. The weight values ​​are determined by expert scoring or empirical analysis of historical data, including: Based on the quantitative and qualitative indicators in the distribution network project consistency evaluation index system, multiple evaluation dimensions are weightedly calculated, and the evaluation dimensions at least include economic dimension, technical dimension, operation reliability dimension and environmental adaptability dimension. The quantitative indicators are obtained by normalization processing, and the qualitative indicators are obtained by fuzzy evaluation method processing; Different weight values ​​are assigned according to the importance of each evaluation dimension to the consistency of the distribution network project. The weight value assignment includes the following steps: Determine the initial weight of each evaluation dimension based on empirical analysis of historical data, the historical data including the operation history, dispatching records and equipment performance evaluation data of the distribution network project, the initial weight reflects the relative importance of each evaluation dimension in the project implementation; The initial weights are modified through expert scoring, which is based on a comprehensive consideration of the actual operating environment, load demand, equipment characteristics and regional policies of the specific distribution network project. The scoring process adopts a hierarchical scoring system to ensure that the weight assignment of each evaluation dimension is reasonable and targeted; The weight value is applied to the quantitative index of each evaluation dimension, and the quantitative index includes the quantitative index score after normalization and the qualitative index score after fuzzy evaluation method processing. Specifically, according to the weight value of each evaluation dimension, the corresponding quantitative score is weighted calculated to form the weighted score of the evaluation dimension; the weighted scores of multiple evaluation dimensions are summarized through the weighted calculation results to form a comprehensive evaluation score of the consistency of the distribution network project.

[0087] First, it is necessary to prepare all the subdivided evaluation index data in the consistency evaluation index system of the distribution network project. These indicators include quantitative indicators and qualitative indicators, which correspond to different evaluation dimensions.

[0088] Examples of evaluation dimensions and their corresponding indicators: 1. Economic dimension Quantitative indicators: return on investment (%), cost-effectiveness ratio Qualitative indicators: project feasibility, economic development driving effect 2. Technical dimension quantitative indicators: equipment utilization rate (%), automation level (%) Qualitative indicators: technological advancement, system scalability 3. Operational reliability dimension quantitative indicators: power supply reliability rate (%), fault recovery time (hours) Qualitative indicators: system stability, emergency response capability 4. Quantitative indicators of environmental adaptability: line loss rate (%), environmental protection investment ratio (%) Qualitative indicators: environmental friendliness, social benefits For each indicator, you need to collect historical data and relevant data of the current project. For example, for the quantitative indicator "return on investment", you may need to collect the return on investment data of all similar projects in the past five years, as well as the expected return on investment of the current evaluation project.

[0089] Normalization of quantitative indicators The quantitative indicators are normalized so that indicators of different dimensions can be compared and calculated comprehensively.

[0090] The specific implementation steps are as follows: Determine the maximum and minimum values ​​of the indicator: Based on historical data, determine the maximum and minimum values ​​of each quantitative indicator. For example, for the "Return on Investment" indicator, assume that the maximum value in the historical data is 25% and the minimum value is 5%.

[0091] Calculate the normalized score of the current project indicator: Use the maximum and minimum normalization method to convert the indicator value of the current project into a score between 0 and 1. The specific calculation method is: subtract the minimum value from the current value, and then divide it by the difference between the maximum and minimum values.

[0092] Taking "return on investment" as an example, assuming that the expected return on investment of the current project is 15%, its normalized score is: (15% - 5%) / (25% - 5%) = 0.5.

[0093] Repeat the above steps for all quantitative indicators: Perform the same normalization process on all quantitative indicators in the consistency evaluation indicator system. For example: Equipment utilization rate: current value 75%, maximum value 90%, minimum value 50%, normalized score 0.625 Power supply reliability rate: current value 99.9%, maximum value 99.99%, minimum value 99%, normalized score 0.9091 Fault recovery time: current value 2 hours, maximum value 8 hours, minimum value 0.5 hours, normalized score 0.8 Line loss rate: current value 6%, maximum value 10%, minimum value 4%, normalized score 0.6667 Fuzzy evaluation processing of qualitative indicators The fuzzy evaluation method is used to quantify the qualitative indicators, converting the subjective evaluation of experts into objective quantitative scores.

[0094] The specific implementation steps are as follows: Formulate multi-level scoring standards: Based on a comprehensive analysis of the actual operation of the distribution network project and historical data, formulate a multi-level scoring standard suitable for each qualitative indicator. For example, for the "technical advancement" indicator, the following five-level scoring standard can be formulated: Very advanced: 0.8-1.0 Moderately advanced: 0.6-0.8 Average: 0.4-0.6 Moderately backward: 0.2-0.4 Very backward: 0-0.2 Expert scoring: Invite multiple field experts to score each qualitative indicator. Each expert assigns a degree of membership to each qualitative indicator at different evaluation levels based on a multi-level scoring standard.

[0095] For example, for the "technological advancement" indicator, suppose there are three experts scoring, and their scoring results are as follows: Expert 1: Very advanced (0.7), relatively advanced (0.3), average (0), relatively backward (0), very backward (0) Expert 2: Very advanced (0.6), relatively advanced (0.4), average (0), relatively backward (0), very backward (0) Expert 3: Very advanced (0.8), relatively advanced (0.2), average (0), relatively backward (0), very backward (0) Determine the fuzzy membership: Based on the expert scoring results, calculate the average membership of each qualitative indicator at different evaluation levels. Continuing with "technical advancement" as an example: Very advanced: (0.7 + 0.6 + 0.8) / 3 = 0.7 Moderately advanced: (0.3 + 0.4 + 0.2) / 3 = 0.3 Average: (0 + 0 + 0) / 3 = 0 Moderately backward: (0 + 0 + 0) / 3 = 0Very backward: (0 + 0 + 0) / 3 = 0 Calculate the quantitative score: Use the fuzzy comprehensive evaluation model to convert the fuzzy membership degree into the corresponding quantitative score. The specific steps are as follows: The fuzzy membership is weighted averaged, and the weights can be set according to the importance of the evaluation level. For example, the weights can be set to: [1.0, 0.8, 0.6, 0.4, 0.2].

[0096] Calculate the weighted average: 0.7 * 1.0 + 0.3 * 0.8 + 0 * 0.6 + 0 * 0.4 + 0 * 0.2 = 0.94 The weighted average is normalized to obtain the final quantitative score. In this case, since the weighted average is already in the range of 0-1, no normalization is required. Therefore, the final quantitative score of the "technological advancement" indicator is 0.94.

[0097] Repeat the above steps for all qualitative indicators: Perform the same fuzzy evaluation process on all qualitative indicators in the consistency evaluation indicator system. For example: Project feasibility: Quantitative score 0.82 System scalability: Quantitative score 0.78 System stability: Quantitative score 0.88 Environmental friendliness: Quantitative score 0.85 Determine the weight of evaluation dimensions The weight of each evaluation dimension is determined based on empirical analysis of historical data and expert scoring.

[0098] The specific implementation steps are as follows: Initial weight determination: The initial weights of each evaluation dimension are determined by analyzing historical data, including the operation history, dispatch records and equipment performance evaluation data of the distribution network project. These initial weights reflect the relative importance of each evaluation dimension in project implementation.

[0099] For example, after analyzing historical data, the following initial weights are obtained: Economic dimension: 0.30 Technical dimension: 0.25 Operational reliability dimension: 0.30 Environmental adaptability dimension: 0.15 Expert scoring correction: Invite experts in the field of distribution network to revise the initial weights. The expert scoring process is based on a comprehensive consideration of the actual operating environment, load demand, equipment characteristics and regional policies of the specific distribution network project.

[0100] Scoring is done using a tiered scoring system, for example: Very important: 1.2-1.5 Somewhat important: 1.0-1.2 Moderately important: 0.8-1.0 Not important: 0.6-0.8 Assume that there are 5 experts participating in the scoring, and their scoring results are as follows: Economic dimension: 1.2, 1.3, 1.1, 1.2, 1.3 Technical dimension: 1.1, 1.0, 1.2, 1.1, 1.0 Operational reliability dimension: 1.3, 1.4, 1.2, 1.3, 1.3 Environmental adaptability dimension: 0.9, 1.0, 0.8, 0.9, 1.1 Calculate the average score: Economic dimension: (1.2 + 1.3 + 1.1 + 1.2 + 1.3) / 5 = 1.22 Technical dimension: (1.1 + 1.0 + 1.2 + 1.1 + 1.0) / 5 = 1.08 Operational reliability dimension: (1.3 + 1.4 + 1.2 + 1.3 + 1.3) / 5 = 1.30 Environmental adaptability dimension: (0.9 + 1.0 + 0.8 + 0.9 + 1.1) / 5 = 0.94 Modified weight calculation: Multiply the initial weight by the average expert score to get the modified weight: Economic dimension: 0.30 * 1.22 = 0.366 Technical dimension: 0.25 * 1.08 = 0.270 Operational reliability dimension: 0.30 * 1.30 = 0.390 Environmental adaptability dimension: 0.15 * 0.94 = 0.141 Normalization: Normalize the corrected weights to ensure that the sum of all weights is 1: Economic dimension: 0.366 / (0.366 + 0.270 + 0.390 + 0.141) = 0.314 Technical dimension: 0.270 / (0.366 + 0.270 + 0.390 + 0.141) = 0.231 Operational reliability dimension: 0.390 / (0.366 + 0.270 + 0.390 + 0.141) = 0.334 Environmental adaptability dimension: 0.141 / (0.366 + 0.270+ 0.390 + 0.141) = 0.121 Finally, the weight values ​​of each evaluation dimension are obtained: Economic dimension: 0.314 Technical dimension: 0.231 Operational reliability dimension: 0.334 Environmental adaptability dimension: 0.121 Multi-dimensional weighted calculation Multi-dimensional weighted calculation is performed based on the normalized quantitative indicators, the qualitative indicators processed by the fuzzy evaluation method, and the determined evaluation dimension weights.

[0101] The specific implementation steps are as follows: Calculate the score of each evaluation dimension: For each evaluation dimension, take the weighted average of the scores of the quantitative and qualitative indicators it contains to obtain the score of the dimension.

[0102] For example, for the economic dimension: Return on investment (quantitative): 0.5, weight 0.6 Project feasibility (qualitative): 0.82, weight 0.4 Economic dimension score = 0.5 * 0.6 + 0.82 * 0.4 = 0.628 Similarly, calculate the scores of other dimensions: Technical dimension score: 0.782 Operation reliability dimension score: 0.860 Environmental adaptability dimension score: 0.758 Calculate the comprehensive evaluation score: Multiply the score of each evaluation dimension by its corresponding weight, and then sum them up to get the final consistency comprehensive evaluation score.

[0103] The calculation process is as follows: 0.628 * 0.314 + 0.782 * 0.231 + 0.860 * 0.334 + 0.758 *0.121 = 0.766 Therefore, the comprehensive evaluation score of the distribution network project's consistency is 0.766.

[0104] Interpretation and application of evaluation results Based on the calculated comprehensive consistency evaluation score, distribution network projects are evaluated and decision support is provided.

[0105] The specific implementation steps are as follows: Develop evaluation criteria: Based on historical project data and expert experience, develop evaluation criteria. For example: Excellent: 0.9-1.0 Good: 0.8-0.9 Average: 0.7-0.8 Poor: 0.6-0.7 Unqualified: 0-0.6 Result interpretation: Compare the project's consistency comprehensive evaluation score with the evaluation criteria to draw an evaluation conclusion. In this case, the score is 0.766, which is at the "average" level.

[0106] Application of results: Provide support for project decision-making based on the evaluation results. For example: For projects at the "average" level, targeted improvements can be recommended. By analyzing the scores of each evaluation dimension, it can be found that the project's performance in the economic dimension is relatively weak. It is recommended that the project team focus on improving the return on investment and optimizing the feasibility of the project.

[0107] At the same time, the project performed well in terms of operational reliability, and we can consider further developing and promoting these advantages.

[0108] Evaluation method optimization By continuously collecting and analyzing project data, the evaluation methods are continuously optimized to improve their accuracy and applicability.

[0109] The specific implementation steps are as follows: Data accumulation: Continuously collect actual operating data of the evaluated projects, including the actual performance of each indicator and the actual importance of each evaluation dimension.

[0110] Result comparison: Compare and analyze the actual operation results of the project with the evaluation scores to identify possible deviations in the evaluation method.

[0111] Method adjustment: According to the comparative analysis results, the evaluation method is adjusted and optimized. Possible adjustments include: Update the indicator system: add or delete certain evaluation indicators according to new technological developments and industry trends. Adjust dimension weights: appropriately adjust the weight distribution according to the actual importance of each evaluation dimension. Optimize quantitative methods: improve the fuzzy evaluation method of qualitative indicators and improve its accuracy.

[0112] For example, if it is found that the environmental adaptability dimension is more important than expected in actual project operation, we can consider increasing its weight from 0.121 to 0.150, and adjust the weights of other dimensions accordingly.

[0113] Through the above detailed steps, this method realizes the consistency evaluation of distribution network projects based on multi-dimensional weighted calculation. This method comprehensively considers multiple dimensions such as economy, technology, operational reliability and environmental adaptability. Through the normalization of quantitative indicators and fuzzy evaluation of qualitative indicators, combined with the weights determined by expert experience, a comprehensive and objective evaluation result is obtained. At the same time, through continuous data accumulation and method optimization, the dynamic adaptability and long-term effectiveness of the evaluation method are ensured, providing reliable support for the decision-making and management of distribution network projects.

[0114] In an optional embodiment, Sorting multiple distribution network projects based on a set evaluation threshold, wherein the sorting result is used to determine the optimal distribution network project, including: Obtaining a comprehensive consistency evaluation score of a plurality of distribution network projects, wherein the comprehensive consistency evaluation score is based on a weighted calculation result of evaluation dimensions of each distribution network project, wherein the evaluation dimensions include an economic dimension, a technical dimension, an operational reliability dimension, and an environmental adaptability dimension; Setting an evaluation threshold, which is a standard value used to screen and sort distribution network projects. The evaluation threshold is obtained based on the analysis of operating data of multiple historical projects. The operating data includes the actual economic benefits, technical reliability and operating stability of the project. The evaluation threshold can be dynamically adjusted for a specific area, a specific project type or a specific time period; Based on the set evaluation threshold, the consistency comprehensive evaluation score of each distribution network project is compared. Specifically, for each distribution network project, if the consistency comprehensive evaluation score is higher than the evaluation threshold, the project is marked as a qualified project; if the consistency comprehensive evaluation score is lower than the evaluation threshold, the project is marked as an unqualified project; All distribution network projects marked as qualified are sorted according to the consistency comprehensive evaluation scores. In the sorting process, projects with higher consistency comprehensive evaluation scores are prioritized. The sorting method is in descending order from the highest score to the lowest score, ensuring that the project with the highest consistency comprehensive evaluation score is at the top of the sorting results; Determine the optimal distribution network project according to the sorting results, wherein the optimal distribution network project is the project with the highest consistency comprehensive evaluation score in the sorting results, and is used for priority recommendation for implementation or obtaining resource allocation; The ranking results are fed back to the distribution network project management system, and the distribution network project management system provides decision support according to the ranking results, including resource allocation, project scheduling optimization and project implementation priority adjustment.

[0115] First, obtain the consistency comprehensive evaluation scores of multiple distribution network projects. This score is based on the weighted calculation results of the evaluation dimensions of each distribution network project. The evaluation dimensions include economic dimension, technical dimension, operational reliability dimension and environmental adaptability dimension. Specifically, the analytic hierarchy process (AHP) can be used to determine the weight of each dimension. For example, the weight of the economic dimension is 0.4, the weight of the technical dimension is 0.3, the weight of the operational reliability dimension is 0.2, and the weight of the environmental adaptability dimension is 0.1. For each dimension, several specific indicators can be set. For example, the economic dimension can include indicators such as return on investment and net present value; the technical dimension can include indicators such as equipment advancement and technology maturity; the operational reliability dimension can include indicators such as failure rate and repair time; the environmental adaptability dimension can include indicators such as carbon emissions and noise level. Score each indicator, then calculate the score of each dimension according to the weight, and finally summarize to obtain the consistency comprehensive evaluation score.

[0116] Next, set the evaluation threshold. The evaluation threshold is a standard value used to screen and sort distribution network projects. It is based on the analysis of the operating data of multiple historical projects. The operating data includes the actual economic benefits, technical reliability and operational stability of the project. The evaluation threshold can be determined by statistical analysis methods, such as regression analysis or cluster analysis. For example, 100 distribution network projects completed in the past five years can be selected as samples, and their operating data can be analyzed to obtain a reasonable evaluation threshold, such as 75 points (out of 100 points). It is worth noting that the evaluation threshold can be dynamically adjusted for specific areas, specific project types or specific time periods. For example, for projects in urban areas, a higher evaluation threshold may be required; for new technology application projects, a relatively low evaluation threshold may be required to encourage innovation.

[0117] Then, based on the set evaluation threshold, the consistency comprehensive evaluation score of each distribution network project is compared. The specific operation is: for each distribution network project, if the consistency comprehensive evaluation score is higher than the evaluation threshold (for example, 75 points), the project is marked as a qualified project; if the consistency comprehensive evaluation score is lower than the evaluation threshold, the project is marked as an unqualified project. This step can be automated by writing a simple conditional judgment program.

[0118] All distribution network projects marked as qualified are sorted according to the consistency comprehensive evaluation score. During the sorting process, projects with higher consistency comprehensive evaluation scores are prioritized. The sorting method is descending order from the highest score to the lowest score, ensuring that the projects with the highest consistency comprehensive evaluation scores are at the top of the sorting results. A quick sort algorithm can be used to implement this step to improve the efficiency of sorting a large number of projects.

[0119] The optimal distribution network project is determined based on the ranking results. The optimal distribution network project is the project with the highest consistency comprehensive evaluation score in the ranking results, which is used for priority recommendation for implementation or resource allocation. For example, if the scores of the first three projects after ranking are 92 points, 88 points, and 85 points respectively, then the project with a score of 92 points will be determined as the optimal distribution network project.

[0120] Finally, the ranking results are fed back to the distribution network project management system. The distribution network project management system provides decision support based on the ranking results, including resource allocation, project scheduling optimization, and project implementation priority adjustment. Specifically, the system can automatically generate a project implementation plan based on the ranking results, allocate more human and material resources to high-scoring projects, and optimize the order of project implementation to improve overall efficiency. For example, for the top 10% of projects, the system can recommend an increase in resource investment of 20%; for the bottom 10% of qualified projects, the system can recommend delaying implementation or re-evaluation.

[0121] In practical applications, a comprehensive database can be established to store and manage project information, evaluation scores, threshold settings and other data. By regularly updating and analyzing these data, the evaluation system and decision-making process can be continuously optimized. At the same time, machine learning algorithms such as support vector machines (SVM) or random forests can be introduced to predict the long-term performance of projects and further improve the accuracy and foresight of decision-making.

[0122] In addition, in order to ensure the fairness and transparency of the evaluation process, a multi-party review mechanism can be established. For example, technical experts, economists, environmental experts, etc. can be invited to form a review panel to conduct a multi-dimensional evaluation of the project. The review results can serve as an important reference or correction basis for the consistency comprehensive evaluation score.

[0123] Finally, in order to continuously improve this ranking and decision-making method, a feedback mechanism can be established. Regularly collect actual operation data of implemented projects, compare and analyze with the initial evaluation results, find out the deficiencies in the evaluation system, and adjust the evaluation indicators, weight settings or threshold standards accordingly. This can ensure that the method can adapt to the ever-changing technical environment and market needs, and always maintain its effectiveness and practicality.

Claims

1. A comprehensive evaluation method for distribution network project consistency based on quantitative evaluation, characterized in that: The following steps are involved: Step 1: Obtain basic data of the distribution network project, including the project's geographic information, distribution network structure information, historical operation data, equipment parameters, load distribution and scheduling plan data; Based on the basic data, a distribution network project consistency evaluation index system is constructed, wherein the evaluation index system includes multiple evaluation dimensions, wherein the evaluation dimensions at least include an economic evaluation dimension, a technical evaluation dimension, an operation reliability evaluation dimension, and an environmental adaptability evaluation dimension of the project, and each evaluation dimension includes multiple subdivided evaluation indicators, wherein the subdivided evaluation indicators include quantitative indicators and qualitative indicators; the quantitative indicators are calculated based on the historical operation data and equipment parameters of the distribution network project, and the qualitative indicators are obtained by expert evaluation of the dispatching plan data and load distribution data of the distribution network project; Step 2: quantify each subdivided evaluation index, wherein the quantitative index is normalized, and the normalization includes calculating the relative value of each subdivided evaluation index according to the maximum and minimum values; the qualitative index is quantified by a fuzzy evaluation method, and the fuzzy evaluation method includes assigning fuzzy membership to the qualitative index by expert scoring, and converting the obtained membership value into a corresponding quantitative value; based on the normalized quantitative index and the qualitative index processed by the fuzzy evaluation method, weighted calculation is performed on the multiple evaluation dimensions, and the weighted calculation includes assigning different weight values ​​according to the importance of each evaluation dimension to the consistency of the distribution network project, and the weight value is determined by expert scoring or empirical analysis of historical data; Step 3: Based on the result of the weighted calculation, calculate the consistency comprehensive evaluation score of each distribution network project, the consistency comprehensive evaluation score is formed by the weighted scores of each evaluation dimension; perform a multi-project comparative analysis on the consistency comprehensive evaluation score, the comparative analysis includes sorting multiple distribution network projects based on a set evaluation threshold, and the sorting result is used to determine the optimal distribution network project; verify the sorting result with the actual operation of the distribution network project, the verification includes comparing the sorting result with the economic benefits, technical performance, operation stability and environmental impact of the actual operation of the project. If the sorting result is inconsistent with the actual operation, adjust the weighted calculation method or fuzzy evaluation method in the evaluation index system to make the evaluation result more in line with the actual operation.

2. The method for comprehensive evaluation of distribution network project consistency based on quantitative evaluation according to claim 1 is characterized in that: In step 1, the basic data is collected in real time or periodically from different data sources through a multi-source data collection interface, and the multi-source data collection interface includes a geographic information system interface for collecting geographic information, a power grid topology data interface for collecting distribution network structure information, an operation monitoring system interface for collecting historical operation data, an equipment management system interface for collecting equipment parameters, a load monitoring system interface for collecting load distribution data, and a dispatch management system interface for collecting dispatch plan data; The acquired basic data is cleaned and preprocessed, and the data cleaning and preprocessing include removing invalid data, correcting abnormal data and filling missing data. The data cleaning and preprocessing are achieved by an abnormal data detection algorithm based on machine learning, and the abnormal data detection algorithm identifies and corrects abnormal data based on historical data patterns and equipment parameter models.

3. The method for comprehensive evaluation of distribution network project consistency based on quantitative evaluation according to claim 2 is characterized in that: The quantitative indicators of the economic evaluation dimension in step 1 include: return on investment, cost-effectiveness ratio, and asset utilization rate; the qualitative indicators of the economic evaluation dimension include: project feasibility, financing difficulty, and economic development driving force; The quantitative indicators of the technical evaluation dimension include: power supply reliability, voltage qualification rate, and line loss rate; the qualitative indicators of the technical evaluation dimension include: technological advancement, equipment compatibility, and system scalability; The quantitative indicators of the operational reliability evaluation dimension include: failure rate, mean repair time, and N-1 verification pass rate; the qualitative indicators of the operational reliability evaluation dimension include: emergency response capability, maintenance convenience, and anti-interference capability; The quantitative indicators of the environmental adaptability evaluation dimension include: carbon emissions, noise levels, and electromagnetic radiation intensity; the qualitative indicators of the environmental adaptability evaluation dimension include: environmental friendliness, landscape coordination, and climate adaptability.

4. The method for comprehensive evaluation of distribution network project consistency based on quantitative evaluation according to claim 1 is characterized in that: In step 2, the quantitative index normalization process specifically includes the following steps: Determine the maximum and minimum values ​​of indicators: Based on historical data, determine the maximum and minimum values ​​of each quantitative indicator; Calculate the normalized score of the current project indicator: Use the maximum and minimum normalization method to convert the indicator value of the current project into a score between 0 and 1. Specifically, subtract the minimum value from the current value, and then divide it by the difference between the maximum and minimum values; Repeat the above steps for all quantitative indicators: perform the same normalization process on all quantitative indicators in the consistency evaluation indicator system.

5. The method for comprehensive evaluation of distribution network project consistency based on quantitative evaluation according to claim 1 is characterized in that: In step 2, the quantification of the qualitative indicators using the fuzzy evaluation method specifically includes the following steps: Formulate multi-level scoring standards: Based on the comprehensive analysis of the actual operation of the distribution network project and historical data, formulate multi-level scoring standards suitable for each qualitative indicator; According to the multi-level scoring criteria, each qualitative indicator is assigned a membership degree at different evaluation levels; Calculate the average membership of each qualitative indicator at different evaluation levels; Use the fuzzy comprehensive evaluation model to convert the fuzzy membership degree into the corresponding quantitative score; Repeat the above steps for all qualitative indicators: Perform the same fuzzy evaluation process on all qualitative indicators in the consistency evaluation indicator system.

6. The method for comprehensive evaluation of distribution network project consistency based on quantitative evaluation according to claim 1 is characterized in that: The weighted calculation of the multiple evaluation dimensions in step 2 specifically includes: determining the initial weight: preliminarily determining the weight of each dimension based on historical data analysis; weight correction: using the Delphi method to obtain adjusted weights through multiple rounds of anonymous questionnaires; weight application: applying the corrected weights to the score calculation of each dimension.

7. The method for comprehensive evaluation of distribution network project consistency based on quantitative evaluation according to claim 1 is characterized in that: The calculation of the comprehensive consistency evaluation score of each distribution network project in step 3 specifically includes: determining the indicator weight: determining the weight of each detailed evaluation indicator through historical data analysis or expert scoring; weighted calculation: multiplying the quantitative score of each indicator by its corresponding weight, and then summing them to obtain the final comprehensive consistency evaluation score.

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