Cable accessory installation personnel capability comprehensive evaluation method based on improved combination empowerment-TOPSIS

Abstract

A cable accessory installation personnel capability comprehensive evaluation method based on improved combination empowerment-TOPSIS comprises the steps that cable accessory installation personnel training regulations and standard files are analyzed, and evaluation indexes are determined; establishing a comprehensive evaluation index system; comparing the importance of each evaluation index by using an analytic hierarchy process to obtain a judgment matrix, and obtaining a subjective weight vector of each evaluation index; key behavior data of different persons are collected, and normalization processing is carried out on the key behavior data; obtaining objective weights of the evaluation indexes; performing combination optimization on the subjective weight and the objective weight to obtain a final comprehensive weight; the Euclidean distance between each evaluated object and the ideal solution is calculated, and then the close degree coefficient of the evaluated object is calculated; and sorting all the evaluation objects according to the closeness, and outputting a final grade evaluation result according to a preset score interval. According to the evaluation method, a subjective and objective weight optimization mechanism and a TOPSIS multi-attribute decision model are fused, and the capability quality of cable accessory installation personnel is scientifically, quantitatively and comprehensively evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cable accessory installation personnel ability evaluation, and particularly relates to a cable accessory installation personnel ability comprehensive evaluation method based on improved combination weighting-TOPSIS. BACKGROUND

[0002] In modern power systems, cable accessories are an important part of the connection between cables and related electrical equipment, and are crucial to the stable operation of power system networks. From the data of high-voltage cable line fault cases in recent years, the installation quality of cable accessories has become the main cause of cable line faults, and the installation and maintenance of cable accessories are facing unprecedented challenges. Under this situation, it is particularly urgent to improve the professional skills and standardized operation level of installation personnel and to carry out systematic skill training and evaluation. However, scientifically and accurately evaluating the ability and performance of training personnel is still a problem that needs in-depth research. Therefore, in the cable accessory installation examination, the accurate assessment of the ability and quality of installation personnel is not only an important prerequisite for optimizing training effect, but also a key guarantee for ensuring installation quality and stable operation of power grids.

[0003] At present, the power industry has implemented relevant training and examination systems, but the existing evaluation methods generally have the following problems:

[0004] (1) Strong subjectivity due to reliance on expert scoring;

[0005] (2) Lack of systematic and detailed ability evaluation system;

[0006] (3) Evaluation methods are outdated and cannot meet the examination needs in the new technical environment.

[0007] Therefore, it is urgent to build a scientific, systematic and objective comprehensive evaluation method for cable accessory installation personnel ability, which can cover multiple levels of ability indicators and introduce various weight analysis and decision-making methods to realize accurate, quantitative and traceable evaluation of personnel ability and quality. It provides a more objective and reasonable evaluation framework for personnel evaluation in cable accessory installation training, and can more truly reflect the comprehensive ability of training personnel. SUMMARY

[0008] To solve the above problems, the present application provides a comprehensive evaluation method for cable accessory installation personnel ability based on improved combination weighting-TOPSIS. The evaluation method combines the subjective and objective weight optimization mechanism with the TOPSIS multi-attribute decision-making model to realize scientific, quantitative and comprehensive evaluation of the ability and quality of cable accessory installation personnel.

[0009] The technical scheme adopted by the present application is as follows:

[0010] The improved combination weighting-TOPSIS-based comprehensive evaluation method for cable accessory installation personnel capability is characterized by comprising the following steps:

[0011] Step 1: Analyze the cable accessory installation personnel training regulations and standard files, and determine the evaluation indexes;

[0012] Step 2: Establish a hierarchical structure including a target layer, a criterion layer and an index layer based on the evaluation indexes and their membership relations determined in step 1, and finally form a comprehensive evaluation index system;

[0013] Step 3: Compare the importance of each evaluation index by using the analytic hierarchy process to obtain a judgment matrix, and pass the consistency test to obtain a subjective weight vector u j of each evaluation index;

[0014] Step 4: Collect key behavior data of different personnel in the process of performing cable accessory installation tasks in a virtual simulation system, and normalize the key behavior data;

[0015] Step 5: Perform information entropy analysis on the standardized data based on an improved entropy weight method to avoid zero value interference and distortion caused by an entropy value of 1, and obtain an objective weight w j of each evaluation index;

[0016] Step 6: Combine and optimize the subjective weight and the objective weight by using the least square method to obtain a final comprehensive weight;

[0017] Step 7: Calculate the Euclidean distance between each evaluated object and an ideal solution by using the TOPSIS method based on the final comprehensive weight obtained in step 6, and then calculate the closeness coefficient thereof;

[0018] Step 8: Sort all the evaluation objects according to the closeness, and output the final grade evaluation results including excellent, good, medium, pass and fail according to a preset score interval.

[0019] The step 1 in the specification is a deep analysis of four key industry standards: “Power Industry Cable Accessory Installation Personnel Training and Examination Specification”, “Installation Regulations for Joint of XLPE Insulated Power Cable with Rated Voltage of 66kV-220kV”, “Installation Regulations for Outdoor Terminal of XLPE Insulated Power Cable with Rated Voltage of 66kV-220kV”, and “Installation Regulations for GIS Terminal of XLPE Insulated Power Cable with Rated Voltage of 66kV-220kV”. The system systematically sorts out the specific requirements in these standards, including personnel operation process, technical parameter precision, safety operation specification, tool and instrument usage, drawing identification and understanding, and quality acceptance standards. Combined with the virtual simulation system, which can automatically record task completion time, step execution accuracy, tool usage, safety specification compliance, and other technical features, the qualitative requirements in the regulations are converted into quantifiable behavior data. Finally, seven core evaluation indicators are selected and determined: task completion time, accuracy rate, installation quality, drawing identification accuracy rate, instrument usage accuracy rate, tool usage accuracy rate, and safety specification mastery rate.

[0020] In step 2, the comprehensive evaluation index system is achieved by decomposing complex evaluation content into specific levels and indicators, quantifying the performance of each aspect, and combining the set weights to obtain a comprehensive score or evaluation result. The comprehensive evaluation index system usually includes target layer, criterion layer, and indicator layer.

[0021] The target layer elements include the comprehensive ability of cable accessory installation personnel.

[0022] The comprehensive ability of cable accessory installation personnel is precisely evaluated through an evaluation model based on improved combination weighting-TOPSIS. First, according to the “Power Industry Cable Accessory Installation Personnel Training and Examination Specification”, the comprehensive ability is divided into two dimensions: basic ability and professional ability. Basic ability includes cable installation drawing identification accuracy, electrician instrument usage accuracy, tool usage accuracy, and safety protection mastery. Professional ability covers task completion time, installation accuracy, and installation quality. Then, the subjective weight is determined by the analytic hierarchy process, the objective weight is calculated by the improved entropy weight method, and the least squares method is used to optimize the combination to obtain the comprehensive weight of each indicator. Finally, the TOPSIS method is used to calculate the relative closeness of the evaluated personnel to the ideal solution in the weighted standardized matrix. According to the closeness value, the personnel ability is divided into five levels: excellent ([0.8, 1]), good ([0.6, 0.8)), medium ([0.4, 0.6)), pass ([0.2, 0.4]), and fail ([0, 0.2]), forming a systematic, quantifiable, and hierarchical comprehensive evaluation result.

[0023] The criterion layer elements include professional ability and basic ability; the professional ability includes task completion time, accuracy rate and installation quality; and the basic ability includes picture recognition accuracy rate, instrument use correctness rate, tool use correctness rate and safety specification mastery rate;

[0024] The index layer includes task completion time, accuracy rate, installation quality, picture recognition accuracy rate, instrument use correctness rate, tool use correctness rate and safety specification mastery rate.

[0025] The index quantification formula is as follows:

[0026]

[0027] The above formula tests the understanding and mastery of personnel on the "Electric Power Safety Work Procedures" and related safety regulations through the safety knowledge test module of the virtual system, and the questions cover personal protection, on-site safety measures, emergency disposal and the like.

[0028] In the above formula:

[0029] The accuracy rate formula reflects the mastery of the operation personnel on the installation process by calculating the ratio of the number of correctly executed steps to the total number of steps;

[0030] The installation quality formula evaluates the conformity of the installation results, and calculates the proportion of the number of installation tasks meeting the standards to the total number of tasks;

[0031] The picture recognition accuracy rate formula measures the reading ability of personnel on the cable structure drawing, and tests the identification accuracy of key structure elements through various cable structure drawings provided by the virtual system;

[0032] The instrument use correctness rate formula tests the standardization of personnel on the operation of the multimeter, insulation resistance meter and grounding resistance meter, and the correctness of each instrument use is automatically determined by the system according to the standard operation process;

[0033] The tool use correctness rate formula evaluates the standardization of the tool operation, tests the rationality of tool selection, the correctness of operation method and the safety of use, and calculates the proportion of the number of correctly used tools to the total number of tool uses;

[0034] The safety specification mastery rate formula calculates the proportion of the number of correctly answered safety specification questions to the total number of questions through the safety knowledge test module of the virtual system, and reflects the understanding and mastery of personnel on the safety specification. All the quantification formulas are in percentage form, which ensures the uniformity of the dimensions of each index and intuitively reflects the achievement degree of each ability.

[0035] The step 3 includes the following steps:

[0036] S3.1: Based on the determined evaluation objectives and evaluation factors, construct the judgment matrix A using the TLSaaty1-9 scaling method; the meaning of the scaling method is shown in Table 1 below:

[0037] Table 1. Meaning of each value in the scaling method.

[0038]

[0039] S3.2: Using the characteristic polynomial, find all the eigenvalues ​​of the judgment matrix A. The specific steps are as follows:

[0040] Suppose that the judgment matrix A is an r-order square matrix. If there exists a number λ and a non-zero vector x such that Ax = λx and x ≠ 0, then λ is called an eigenvalue of A, and x is the eigenvector of A corresponding to the eigenvalue λ. Here, |A-λE| is the characteristic polynomial of matrix A, denoted as f(λ), and E is the identity matrix. f(λ) = |A-λE|. Setting f(λ) = 0, i.e., |A-λE| = 0, we can find the eigenvalue λ. Suppose that f(λ) = 0 has p roots in the complex number domain, λ1, λ2, ..., λ3. p That is, A has p eigenvalues, p = 1, 2, 3, 4, ...; among them, the largest eigenvalue is selected, and the eigenvector corresponding to the largest eigenvalue is obtained using Ax = λx, as follows: in λ1, λ2, ..., λ p Select the largest eigenvalue λ max and using (A-λ) max If E)w = 0, the value of the eigenvector w can be obtained, and the obtained eigenvector w is the weight vector of the index.

[0041] S 3.3: Consistency test of matrix A, where the test method is as follows:

[0042]

[0043] In the formula: CI is the consistency index; λ max The largest eigenvalue of the judgment matrix is ​​r; the order of the judgment matrix is ​​r; CR is the consistency ratio; RI is the random consistency index; the values ​​of RI are shown in Table 2.

[0044] Table 2 RI Value Table

[0045]

[0046] When CR < 0.1, the judgment matrix A passes the consistency test, thus allowing the weights of the evaluation indicators to be determined. If this condition is not met, it must be rechecked and adjusted to ensure that it passes the consistency test.

[0047] S 3.4: If the consistency test is passed, determine the subjective weight vector u. j =w.

[0048] In step 4, key behavioral data of different personnel during the cable accessory installation process in the virtual simulation system are collected, including: task completion time, number of steps correctly executed, assembly dimensions, number of installation tasks that meet the standards, number of cable structures correctly identified, number of times instruments are used correctly, number of times tools are used correctly, and number of safety specification questions answered correctly.

[0049] Step 4 includes the following steps:

[0050] S 4.1: Create the original data decision matrix H:

[0051]

[0052] Where: m is the number of evaluation objects, n is the number of evaluation indicators; h ij The initial data for each indicator are: i = 1, 2, 3, ..., m, j = 1, 2, 3, ..., n; forming the original data decision matrix H.

[0053] S 4.2: Standardization of the decision matrix H of the original data:

[0054] Evaluation indicators can be divided into positive indicators, moderate indicators, and negative indicators, specifically:

[0055] Positive indicators refer to indicators whose data are positively correlated with the evaluation results; that is, the larger the value of the indicator, the better the evaluation result.

[0056] An appropriate indicator refers to an indicator whose data is positively correlated with the evaluation result within a certain range, but whose benefits begin to decrease beyond that range.

[0057] Negative indicators refer to indicators where the evaluation data and the evaluation results are negatively correlated; that is, the larger the value of the evaluation indicator, the worse the evaluation result.

[0058] Accuracy, installation quality, blueprint reading accuracy, instrument usage accuracy, tool usage accuracy, and safety standard mastery rate are positive indicators, while task completion time is a negative indicator.

[0059] The standardization of positive, moderate, and negative indicators is as follows:

[0060]

[0061] In the above formula: y ij For standardized indicator data; minh j maxh is the minimum value of the j-th evaluation index among all samples. j is the maximum value of the j-th evaluation index among all samples; a and b are the left and right endpoints of the optimal interval.

[0062] Step 5 includes the following steps:

[0063] S 5.1: Calculate the weight of the i-th evaluation object with respect to the j-th evaluation index value:

[0064] To ensure the integrity and validity of the data and to prevent the standardized indicators from having a value of 0, this invention adds 0.001 to all 0 values ​​in the matrix elements obtained by standardization.

[0065]

[0066] In the formula: p ij This represents the weight of the i-th evaluation object relative to the j-th evaluation index value.

[0067] S 5.2: Calculate the entropy value of the index:

[0068] In entropy calculation, if the entropy value of some indicators is 1, it means that the distribution of these indicators among different samples is extremely uniform. These indicators are completely ignored in the objective weight allocation. In order to solve this problem, for indicators with an entropy value of 1, the entropy value is set to 0.99, and the entropy weight method is improved to make its application more reasonable.

[0069]

[0070] In the above formula: e j This represents the entropy value of the j-th evaluation indicator.

[0071] S 5.3: Calculate objective weights:

[0072]

[0073] Where: 1-e j w represents the difference coefficient of information entropy for the j-th evaluation indicator; j This represents the objective weight of the j-th evaluation indicator.

[0074] In step 6, the least squares method is used to combine and optimize the subjective and objective weights, as follows:

[0075] Constructing the Lagrange function:

[0076]

[0077] In the formula: L represents the Lagrangian function; u j This represents the subjective weight of the j-th index obtained by the analytic hierarchy process; z j y represents the overall weight of the j-th evaluation indicator; ij w represents the standardized data of the i-th evaluation object on the j-th evaluation indicator;j Let represent the objective weight of the j-th evaluation index obtained by the improved entropy weight method; λ represents the Lagrange multiplier; and n represents the number of evaluation indexes.

[0078] in n represents the number of evaluation indicators;

[0079] make

[0080] In the formula: L represents the Lagrangian function; w j y represents the objective weight of the j-th evaluation index obtained by the improved entropy weight method; ij Let represent the standardized data of the i-th evaluation object on the j-th evaluation index, λ represent the Lagrange multiplier, and m represent the number of evaluation objects.

[0081]

[0082] In the formula: n represents the number of evaluation indicators, z j This represents the overall weight of the j-th evaluation indicator.

[0083]

[0084] In the formula: J represents a diagonal matrix containing the sum of squares of standardized data for each evaluation indicator of the evaluation object, y i1 Let y represent the standardized data of the i-th evaluation object on the first indicator. in Let represent the standardized data of the i-th evaluation object on the n-th indicator, m represent the number of evaluation objects, and diag[i] represent the diagonal matrix.

[0085]

[0086] In the formula: e represents an n×1 unit column vector, where n is the number of evaluation indicators, e T z represents the transpose of the unit column vector e. j λ represents the overall weight for the j-th evaluation index; λ represents the Lagrange multiplier.

[0087] e = [1 1 … 1] T ;

[0088] In the formula: T is the transpose operator;

[0089]

[0090] In the formula: I represents an n×1 vector, whose elements are composed of the average of subjective and objective weights multiplied by the sum of squares of the standardized values ​​of the indicator across all evaluation objects; u1 represents the subjective weight of the first evaluation indicator; w1 represents the objective weight of the first evaluation indicator; y inThis represents the standardized data of the i-th evaluation object on the n-th indicator, where n is the number of evaluation indicators and T is the transpose operator;

[0091]

[0092] Where: z j The optimized overall weight; e T This represents the transpose of the unit column vector e.

[0093] Step 7 includes the following steps:

[0094] S7.1: Elements q of the standardized decision matrix ij :

[0095] The standardized decision matrix Y = {y ij} m×n With comprehensive weight z j Multiplication, y ij Let m represent the standardized data of the i-th evaluation object on the j-th indicator, m represent the number of evaluation objects, and n represent the number of evaluation indicators.

[0096] The weighted standardized decision matrix is ​​Q = {q} ij} m×n :

[0097] q ij =z j ·y ij ;

[0098] Where: q ij This represents the weighted standardized element of the i-th evaluation object on the j-th indicator;

[0099] S7.2: Calculate the positive and negative ideal solutions:

[0100]

[0101] In the formula, A + For the positive ideal solution, A - It is a negative ideal solution; This represents the ideal value of each index corresponding to the positive ideal solution. represents the worst value of each indicator corresponding to the negative ideal solution, and n represents the number of evaluation indicators;

[0102] S7.3: Calculate the weighted Euclidean distance between the evaluation object and the ideal solution;

[0103]

[0104] in, The Euclidean distance for the positive ideal solution. The Euclidean distance for the negative ideal solution; This represents the value of the positive ideal solution on the j-th evaluation index; This represents the value of the negative ideal solution on the j-th index; m represents the number of evaluation objects, and n represents the number of evaluation indicators.

[0105] S7.4: Calculate the ideal solution's application schedule C i :

[0106]

[0107] In step 8, the evaluation level of the cable accessory installers and the proximity threshold for each evaluation level are determined, as shown in Table 3:

[0108] Table 3. Proximity Thresholds and Corresponding Evaluation Levels

[0109]

[0110] This invention discloses a comprehensive evaluation method for the competence of cable accessory installers based on an improved combined weighting-TOPSIS approach, with the following technical advantages:

[0111] 1) Step 1 of this invention analyzes the training procedures and standard documents for cable accessory installers and determines the evaluation indicators. The advantage of this step is that by comprehensively analyzing existing standards, it ensures the scientific and professional nature of the evaluation indicators, laying a solid foundation for the subsequent evaluation system. Systematically summarizing and confirming key evaluation indicators helps ensure the accuracy and rationality of the evaluation.

[0112] 2) Step 2 of this invention establishes a hierarchical structure comprising a target layer, a criterion layer, and an indicator layer, ultimately forming a comprehensive evaluation indicator system. The advantage of this step is that the hierarchical structure makes the evaluation system clearer and more systematic, covering multiple levels of evaluation factors and ensuring comprehensive and operable evaluation results. The hierarchical structure also helps to clearly identify the weights and interrelationships of various capability indicators in practical applications.

[0113] 3) Step 3 of this invention utilizes the Analytic Hierarchy Process (AHP) to compare the importance of each evaluation indicator, obtaining a judgment matrix and acquiring the subjective weight vector through a consistency test. The advantage of this method is that it allows expert evaluation to determine the importance of each indicator, making the evaluation criteria highly professional and comparable. Simultaneously, the consistency test ensures the reliability and rationality of the evaluation, avoiding the influence of subjective bias.

[0114] 4) Step 4 of this invention involves collecting behavioral data of personnel performing installation tasks in the virtual simulation system and standardizing it. The advantage of this step is that it uses actual operational data for evaluation, ensuring the objectivity and practicality of the evaluation results. The virtual simulation system can simulate a real environment, enhancing the realism of the assessment, and data standardization avoids the influence between different units of measurement, ensuring the fairness of the evaluation process.

[0115] 5) Step 5 of this invention utilizes the improved entropy weight method to perform information entropy analysis on the standardized data, deriving the objective weights of each evaluation indicator. The advantage of this method is that it can objectively analyze the importance of each indicator, avoiding the bias that may result from manually setting weights. The improvement of the entropy value method makes it more effective in handling problems such as information distortion and zero-value interference, thereby improving the accuracy of the evaluation system.

[0116] 6) Step 6 of this invention uses the least squares method to optimize the combination of subjective and objective weights. The advantage of this step is that it achieves a scientific integration of weights through mathematical optimization, considering both expert subjective evaluation and objective data analysis. Least squares optimization effectively improves the rationality and accuracy of weight allocation, thereby ensuring the accuracy of the comprehensive evaluation results.

[0117] 7) Step 7 of this invention uses the TOPSIS method to calculate the Euclidean distance between the evaluation object and the ideal solution, thereby obtaining the proximity coefficient. The advantage of this method is that it quantifies the difference between the evaluation object and the ideal state by calculating the Euclidean distance, making the comprehensive evaluation results intuitive and comparable. The application of the TOPSIS method can help to quickly and accurately rank the evaluation objects and clarify their strengths and weaknesses. Attached Figure Description

[0118] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0119] Figure 1 A flowchart for the comprehensive evaluation of the capabilities of cable accessory installers.

[0120] Figure 2 A diagram illustrating the competency indicators for cable accessory installers. Detailed Implementation

[0121] A cable accessory installation training program at a power station was selected as an evaluation case. Ten cable accessory installers were randomly selected as research subjects to participate in virtual system testing, and the test data was used to evaluate the application of the model.

[0122] Based on step 3 above, the importance of each evaluation indicator is compared using the analytic hierarchy process (AHP) to obtain the judgment matrix. Then, through a consistency check, the subjective weight vector u of each evaluation indicator is obtained. j The results of the subjective weighting are shown in Table 4.

[0123] Table 4 Subjective Weight Results

[0124]

[0125] The test data was quantified and the raw data such as task completion time, accuracy, installation quality, blueprint reading accuracy, instrument usage accuracy, tool usage accuracy, and safety standard mastery rate for each person were calculated.

[0126] According to step 4, the original data is standardized to obtain standardized data;

[0127] Based on step 5, information entropy analysis is performed on the standardized data using the improved entropy weight method to obtain the objective weights w of each indicator. j The weighting results are shown in Table 5.

[0128] Table 5 Objective Weight Results

[0129]

[0130] According to step 6, the subjective weight u is calculated using the least squares method. j and objective weight w j The combined weight z is obtained by performing combinatorial optimization. j The results are shown in Table 6.

[0131] According to step 7, z j The proximity coefficient of each person was calculated using the TOPSIS method, and the eight people were rated according to the information in Table 3. The final evaluation results are shown in Table 7.

[0132] Table 6. Overall Weighting Results

[0133]

[0134] Table 7. Evaluation Results of Trainees

[0135]

[0136] According to the evaluation results shown in Table 7, all ten cable accessory installers who participated in the evaluation performed at or above the passing grade. Among them, personnel B, C, G, and I demonstrated excellent overall capabilities and were rated as "good"; personnel A, D, E, F, and H performed at the "average" level, with room for further improvement; personnel J's evaluation result was "pass," which, although at the lowest level, still met the basic requirements.

Claims

1. A comprehensive evaluation method for the competence of cable accessory installers based on improved combined weighting-TOPSIS, characterized in that... Includes the following steps: Step 1: Analyze the training procedures and standard documents for cable accessory installers and determine the evaluation indicators; Step 2: Establish a hierarchical structure containing the target layer, criterion layer, and indicator layer based on the evaluation indicators and their subordinate relationships determined in Step 1, and finally form a comprehensive evaluation indicator system; Step 3: Use the analytic hierarchy process (AHP) to compare the importance of each evaluation indicator, obtain the judgment matrix, and pass the consistency test to obtain the subjective weight vector u of each evaluation indicator. j ; Step 4: Collect key behavioral data of different personnel during the cable accessory installation task in the virtual simulation system, and normalize the key behavioral data; Step 5: Perform information entropy analysis on the standardized data based on the improved entropy weight method to obtain the objective weights w of each evaluation index. j ; Step 6: Use the least squares method to combine and optimize the subjective and objective weights to obtain the final comprehensive weight; Step 7: Calculate the Euclidean distance between each evaluated object and the ideal solution using the TOPSIS method based on the final comprehensive weights obtained in Step 6, and then calculate its proximity coefficient. Step 8: Sort all evaluation objects according to their relevance and output the final grade evaluation results according to the preset score range, including excellent, good, average, pass, and fail.

2. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS as described in claim 1, characterized in that: In step 1, seven evaluation indicators are determined: task completion time, accuracy, installation quality, blueprint reading accuracy, instrument usage accuracy, tool usage accuracy, and mastery of safety regulations.

3. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS as described in claim 1, characterized in that: In step 2, the comprehensive evaluation index system is to decompose the evaluation content into specific levels and indicators, quantify the performance of each aspect, and derive a comprehensive score or evaluation result based on the set weights; the comprehensive evaluation index system includes a target layer, a criterion layer, and an indicator layer. The target layer elements include the comprehensive capabilities of cable accessory installers, which are broken down into two dimensions: basic capability quantification and professional capability quantification. The criteria layer elements include professional skills and basic skills; professional skills include task completion time, accuracy, and installation quality; basic skills include blueprint reading accuracy, instrument usage accuracy, tool usage accuracy, and mastery of safety regulations. The indicator layer includes task completion time, accuracy, installation quality, blueprint reading accuracy, instrument usage accuracy, tool usage accuracy, and mastery of safety regulations.

4. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS as described in claim 3, characterized in that: The formula for quantifying the indicator is as follows:

5. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS as described in claim 4, characterized in that: Step 3 includes the following steps: S3.1: Based on the determined evaluation objectives and factors, construct the judgment matrix A using the TLSaaty1-9 scaling method; S3.2: Utilize the characteristic polynomial to obtain all eigenvalues ​​of the judgment matrix A. The specific steps are as follows: Suppose that the judgment matrix A is an r-order square matrix. If there exists a number λ and a non-zero vector x such that Ax = λx and x ≠ 0, then λ is called an eigenvalue of A, and x is the eigenvector of A corresponding to the eigenvalue λ. Here, |A-λE| is the characteristic polynomial of matrix A, denoted as f(λ), and E is the identity matrix. f(λ) = |A-λE|. Setting f(λ) = 0, i.e., |A-λE| = 0, we can find the eigenvalue λ. Suppose that f(λ) = 0 has p roots in the complex number domain, λ1, λ2, ..., λ3. p That is, the p eigenvalues ​​of A, where p = 1, 2, 3, 4, ...; Specifically, the largest eigenvalue is selected, and the eigenvector corresponding to the largest eigenvalue is obtained using Ax = λx, as follows: In λ1, λ2, ..., λ... p Select the largest eigenvalue λ max and using (A-λ) max If E)w = 0, the value of the eigenvector w can be obtained, and the obtained eigenvector w is the weight vector of the index. S 3.3: Consistency test of matrix A, where the test method is as follows: In the formula: CI is the consistency index; λ max is the largest eigenvalue of the judgment matrix; r is the order of the judgment matrix; CR is the consistency ratio; RI is the random consistency index; When CR < 0.1, the judgment matrix A passes the consistency test, and the weights of the evaluation indicators can be determined. If this condition is not met, it must be re-examined and adjusted to ensure that it passes the consistency test. S 3.4: If the consistency test is passed, determine the subjective weight vector u. j =w.

6. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS as described in claim 1, characterized in that: Step 4 includes the following steps: S 4.1: Create the original data decision matrix H: Where: m is the number of evaluation objects, n is the number of evaluation indicators; h ij The initial data for each indicator are: i = 1, 2, 3, ..., m, j = 1, 2, 3, ..., n; forming the original data decision matrix H; S 4.2: Standardization of the decision matrix H of the original data: The evaluation indicators are divided into positive indicators, moderate indicators, and negative indicators, specifically: Positive indicators refer to indicators whose data are positively correlated with the evaluation results; that is, the larger the value of the indicator, the better the evaluation result. An appropriate indicator refers to an indicator whose data is positively correlated with the evaluation result within a certain range, but whose benefits begin to decrease beyond that range. Negative indicators refer to indicators where the evaluation data and the evaluation results are negatively correlated; that is, the larger the value of the evaluation indicator, the worse the evaluation result. Accuracy, installation quality, blueprint reading accuracy, instrument usage accuracy, tool usage accuracy, and safety standard mastery rate are positive indicators, while task completion time is a negative indicator. The standardization of positive, moderate, and negative indicators is as follows: In the above formula: y ij For standardized indicator data; min h j The minimum value of the j-th evaluation index among all samples; max h j is the maximum value of the j-th evaluation index among all samples; a and b are the left and right endpoints of the optimal interval.

7. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS as described in claim 1, characterized in that: Step 5 includes the following steps: S 5.1: Calculate the weight of the i-th evaluation object with respect to the j-th evaluation index value: Add 0.001 to all zero values ​​in the elements of the standardized matrix; In the formula: p ij This represents the weight of the i-th evaluation object relative to the j-th evaluation indicator value; S 5.2: Calculate the entropy value of the index: In entropy calculation, for an index with an entropy value of 1, the entropy value is set to 0.99, thus improving the entropy weighting method. In the above formula: e j Let represent the entropy value of the j-th evaluation index; S 5.3: Calculate objective weights: Where: 1-e j w represents the difference coefficient of information entropy for the j-th evaluation indicator; j This represents the objective weight of the j-th evaluation indicator.

8. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS as described in claim 1, characterized in that: In step 6, the least squares method is used to combine and optimize the subjective and objective weights, as follows: Constructing the Lagrange function: In the formula: L represents the Lagrangian function; u j This represents the subjective weight of the j-th index obtained by the analytic hierarchy process; z j y represents the overall weight of the j-th evaluation indicator; ij w represents the standardized data of the i-th evaluation object on the j-th evaluation indicator; j This represents the objective weight of the j-th evaluation index obtained by the improved entropy weight method; λ is denoted as a Lagrange multiplier; in, make In the formula: L represents the Lagrangian function; w j y represents the objective weight of the j-th evaluation index obtained by the improved entropy weight method; ij Let λ represent the standardized data of the i-th evaluation object on the j-th evaluation index, λ represent the Lagrange multiplier, and m represent the number of evaluation objects; In the formula: z j This represents the overall weight for the j-th evaluation indicator; In the formula: J represents a diagonal matrix containing the sum of squares of standardized data for each evaluation indicator of the evaluation object, y i1 Let y represent the standardized data of the i-th evaluation object on the first indicator. in Let represent the standardized data of the i-th evaluation object on the n-th indicator, and let diag[i] represent the diagonal matrix; In the formula: e represents an n×1 unit column vector, where e T z represents the transpose of the unit column vector e. j λ represents the overall weight for the j-th evaluation index; λ represents the Lagrange multiplier. e＝[1 1…1] T ； In the formula: T is the transpose operator; In the formula: I represents an n×1 vector, whose elements are composed of the average of subjective and objective weights multiplied by the sum of squares of the standardized values ​​of the indicator across all evaluation objects; u1 represents the subjective weight of the first evaluation indicator; w1 represents the objective weight of the first evaluation indicator; y in This represents the standardized data of the i-th evaluation object on the n-th indicator; Where: z j The optimized overall weight; e T This represents the transpose of the unit column vector e.

9. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS as described in claim 1, characterized in that: Step 7 includes the following steps: S7.1: Elements q of the standardized decision matrix ij : The standardized decision matrix Y = {y ij } m×n With comprehensive weight z j Multiplication, y ij This represents the standardized data of the i-th evaluation object on the j-th indicator; The weighted standardized decision matrix is ​​Q = {q} ij } m×n : q ij =z j ·y ij ; Where: q ij This represents the weighted standardized element of the i-th evaluation object on the j-th indicator; S7.2: Calculate the positive and negative ideal solutions: In the formula, A + For the positive ideal solution, A - It is a negative ideal solution; This represents the ideal value of each index corresponding to the positive ideal solution. This represents the worst value of each index corresponding to the negative ideal solution; S7.3: Calculate the weighted Euclidean distance between the evaluation object and the ideal solution; in, The Euclidean distance for the positive ideal solution. The Euclidean distance for the negative ideal solution; This represents the value of the positive ideal solution on the j-th evaluation index; This represents the value of the negative ideal solution at the j-th index; S7.4: Calculate the ideal solution's application schedule C i :

10. The comprehensive evaluation method for cable accessory installer competence based on improved combined weighting-TOPSIS according to claim 9, characterized in that: In step 8, the evaluation level of the cable accessory installers and the proximity threshold for each evaluation level are determined, as shown in the table below: Proximity threshold and corresponding rating level

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