Performance assessment method for experimental technicians in colleges and universities based on improved ANP

Abstract

The invention relates to the technical field of scientific research management evaluation, in particular to a college experiment technician performance assessment method based on improved ANP, comprising the following steps: S1, establishing a network structure model based on ANP for index content; s2, introducing a fuzzy theory, a TOPSIS model, an entropy weight method and a combined weighting method, and establishing an experimental technician performance assessment index weight calculation model based on improved ANP; and S3, designing a questionnaire, and calculating by using an index weight calculation model based on improved ANP to obtain an index weight distribution scheme. S4, performance assessment work of experimental technicians is carried out, a fuzzy linear transformation method is used for data statistics and analysis, and obtained data is a final performance assessment result; the problems that in the prior art, performance assessment index content is not comprehensive and systematic, experimental technicians are not assessed in a classified and post mode, relevance among indexes cannot be considered, subjective interference exists, and the weight calculation process is not scientific enough are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of scientific research management evaluation, and particularly relates to a performance evaluation method for university experimental technical personnel based on improved ANP. BACKGROUND

[0002] Experimental technical personnel are not only an important part of the university teacher team, but also an indispensable important force of the university talent team. As the core force of experimental teaching, laboratory construction and management, the overall level of experimental technical personnel not only affects the improvement of school innovative talent training, scientific research achievement output and social service ability, but also is an important factor affecting the construction of the school “double first-class”. The State Council of the People's Republic of China issued the “National Medium and Long-term Educational Reform and Development Plan Outline (2010-2020)” in 2010, which clearly pointed out that the competition mechanism should be introduced into universities, performance evaluation should be implemented, and dynamic management should be carried out.

[0003] The core of the evaluation method is the content and weight of the performance index. The existing performance index has the problems of imperfect content and lack of scientific weight calculation, so it is necessary to optimize the performance index first. To perfect the performance index content, the name, connotation and scoring standard of the existing index need to be supplemented and adjusted on the basis of sufficient research and demonstration; to make the performance index weight more authoritative, the internal influence of the index and the different degrees of one-way or two-way influence between different indexes, as well as the interference of subjective factors on the weight calculation, need to be fully considered, and a scientific evaluation model and weight algorithm need to be established, so as to solve the decision problem about the index weight.

[0004] In recent years, many colleges and universities have researched and explored the performance evaluation methods of experimental technicians: Du Yingmei proposed three types of performance evaluation methods: quality-oriented, behavior-oriented, and result-oriented, and seven commonly used performance evaluation methods; Li Yan proposed a "three categories and four benefits" evaluation system, which divides the functions of the laboratory into teaching, scientific research, and social service, and divides the benefits of the laboratory into teaching benefits, scientific research benefits, investment benefits, and social benefits; Cai Xiao divides the evaluators into three categories: leaders, students, and teachers, and establishes a "three-party evaluation index system" to determine the scores of the three parties using principal component analysis, avoiding subjectivity and rent-seeking behavior; Wei Jun establishes a fuzzy gray comprehensive evaluation model for experimental technicians based on the characteristics and quality requirements of their positions, and evaluates them from four aspects: morality, ability, diligence, and performance; Qian Xiaoming takes 15,000-18,000 experimental hours per technician as the reference value, calculates the workload of different disciplines after weighting, and quantifies and evaluates performance based on "standard hours"; Gong Haijun points out that there is a lack of perfect evaluation system and policy for experimental technicians, and the evaluation system should combine qualitative and quantitative methods and reasonably use the evaluation results to constrain and motivate experimental technicians; Zhang Yu proposes new evaluation indicators for experimental technicians based on the actual situation of the school, including experimental teaching, teaching research and quality improvement, academic achievements and scientific research, participation in collective and public activities, and demerit items, and considers age differences in evaluation to improve the incentive effect of evaluation; Yang Fengkai proposes an AHP quantification method for performance evaluation of experimental technicians, applies AHP to determine the weight of indicators, and uses fuzzy comprehensive evaluation method to score, quantifying the performance of experimental technicians; Cui Jiarui establishes OKR for teams and individuals based on OKR and key results method, and practices CRAFT steps; Zhao Yanfeng believes that the impact of title coefficient on the performance of experimental technicians should be considered in workload, and the results of the examination should be used to reward the advanced and spur the laggards, linking individual assessment with evaluation and reward, and title promotion.

[0005] In summary, in recent years, domestic colleges and universities have made some achievements in the research of performance evaluation methods for experimental technicians, but there are still some problems such as incomplete and systematic evaluation index content, lack of classification and post evaluation for experimental technicians, inability to consider the relevance between indicators, subjectivity interference, and unscientific weight calculation process.

[0006] Due to the diversity of tasks undertaken by experimental technicians, including experimental teaching, laboratory construction and management, auxiliary scientific research, talent cultivation, and other public and transactional work, the time and effort required for different tasks are different, making it more complex to determine the performance evaluation indicators of experimental technicians. Therefore, how to build a scientific and perfect performance evaluation method for experimental technicians is a problem that needs to be researched and solved. SUMMARY

[0007] The application aims to provide a college experimental technician performance evaluation method based on improved ANP, aiming at a series of problems such as that the existing experimental technician performance evaluation index content is not comprehensive and systematic, experimental technicians are not classified and evaluated, the correlation between indexes cannot be considered, there is subjective interference, and the weight calculation process is not scientific, a network structure model and an index weight calculation model are established, and a scientific and perfect experimental technician performance evaluation method is constructed according to the actual situation of colleges and universities, so as to mobilize the working enthusiasm of experimental technicians, better perform the present post responsibilities, improve the experimental teaching quality and scientific research level, and promote the improvement of laboratory construction and management level.

[0008] In order to achieve the above technical purposes and achieve the above technical effects, the application is implemented by the following technical solutions:

[0009] A college experimental technician performance evaluation method based on improved ANP, comprising the following steps:

[0010] S1: A network structure model based on ANP is established according to the specific content of 6 first-level indexes and 21 second-level indexes.

[0011] S2: On the basis of the ANP method, the fuzzy theory, the TOPSIS model, the entropy weight method and the combination weighting method are introduced, the calculation process of the ANP method is reconstructed, and an "experimental technician performance evaluation index weight calculation model based on improved ANP" is established.

[0012] S3: A questionnaire is designed, and the index weight calculation model based on improved ANP is used to calculate the index weight distribution scheme.

[0013] S4: On the basis of the index weight distribution scheme, the experimental technician performance evaluation work is carried out, the fuzzy linear transformation method is used for data statistics and analysis, and the obtained data is the final performance evaluation result.

[0014] Further, the step S1 specifically comprises the following sub-steps:

[0015] S1.1: The complex correlation between the 6 first-level indexes and the 21 second-level indexes is analyzed, the correlation includes single-direction and double-direction two pointing modes, and one-to-one, one-to-many, many-to-one and many-to-many four forms of expression, so there are at most 8 kinds of correlation modes.

[0016] S1.2: A complete ANP network structure model is established according to the index level and the correlation between indexes, and the model includes the criterion layer, the network layer and the correlation mode of each index.

[0017] Further, the step S2 specifically comprises the following sub-steps:

[0018] S2.1: In the process of constructing the ANP judgment matrix, the "1-9 scale" commonly used in the traditional ANP method is abandoned. Fuzzy theory is introduced to reduce the subjective factors of humans, thereby obtaining a more realistic and accurate judgment matrix.

[0019] S2.2: The TOPSIS model is used to further calculate the weight ranking results obtained by the ANP method combined with fuzzy theory, so as to make the weight ranking results more objective.

[0020] S2.3: After obtaining a set of weighted ranking results using the ANP method that combines fuzzy theory and the TOPSIS model, use the entropy weight method to calculate another set of weighted ranking results.

[0021] S2.4: The ANP method, which combines fuzzy theory and the TOPSIS model, is used as the "subjective weight," and the entropy weight method is used as the "objective weight." The combined weighting method is used to encapsulate the entire calculation process, comprehensively considering both subjective and objective factors, making the calculation results more authoritative.

[0022] S2.5: Combining sub-steps S2.1-S2.4, the calculation process of the ANP method is reconstructed by introducing fuzzy theory, TOPSIS model, entropy weight method and combined weighting method, and a "weight calculation model for performance evaluation indicators of experimental technicians based on improved ANP" is established.

[0023] Furthermore, step S3 specifically includes the following sub-steps:

[0024] S3.1: Design a questionnaire that includes direct pairwise dominance comparisons between 6 primary indicators and 21 secondary indicators, as well as indirect pairwise dominance comparisons relative to a third element. Based on the "1-9 scale", introduce fuzzy theory and use triangular fuzzy numbers or trapezoidal fuzzy numbers to define the upper and lower limits of the relative importance between elements.

[0025] S3.2: Import all questionnaire results into the professional calculation software yaanp for group decision aggregation, and split the judgment matrix into a primary indicator judgment matrix and a secondary indicator judgment matrix.

[0026] S3.3: Calculate the weighted matrix from the first-level indicator judgment matrix, obtain the unweighted judgment matrix from the second-level indicator judgment matrix, and merge the two to obtain the weighted super matrix.

[0027] S3.4: Perform limit operations on the weighted hypermatrix until all elements in the matrix have been calculated to the limit to obtain the limit hypermatrix. Combine the eigenvectors of each limit hypermatrix to form the initial weight sorting result.

[0028] S3.5: Substitute the initial weight ranking results into the TOPSIS model, calculate the weighted Euclidean distance and relative proximity between the initial weights and the positive and negative ideal solutions, and obtain the final weight ranking results, which are the "subjective weights" in the combined weighting method.

[0029] S3.6: Using the entropy weight method, the performance appraisal indicators are standardized, and then the information entropy of the 6 primary indicators and 21 secondary indicators is calculated. The weight ranking result is then calculated again, which is the "objective weight" in the combined weighting method.

[0030] S3.7: Use the combined weighting method to calculate the importance coefficients of subjective weights and objective weights respectively, and obtain the "weight allocation scheme for performance appraisal indicators of experimental technicians based on improved ANP".

[0031] Furthermore, step S4 specifically includes the following sub-steps:

[0032] S4.1: Use the "Performance Appraisal Index Weighting Scheme for Experimental Technicians Based on Improved ANP" to score the performance of experimental technicians.

[0033] S4.2: Using the Fuzzy linear transformation method, when scoring the statistical indicators, the method of taking the arithmetic mean is abandoned, and a secondary indicator evaluation matrix is ​​constructed.

[0034] S4.3: Based on the primary indicator evaluation vector and the overall evaluation vector, determine the scoring results of the indicators according to the principle of maximizing membership, so as to minimize the subjectivity of the scoring and ensure that the assessment results accurately reflect the actual performance level of the experimental technicians.

[0035] S4.4: Publish the performance appraisal results, accept feedback, and continuously revise and improve the content, weighting, and methods of the performance appraisal indicators.

[0036] Furthermore, the indicators are divided into qualitative and quantitative parts. The qualitative part covers common assessment points for three types of positions: experimental teaching, public technical services, and laboratory construction and management. The quantitative part focuses on assessing the specific responsibilities of each of the three types of positions. There are a total of 6 primary indicators and 21 secondary indicators. Specifically, the qualitative indicators include 3 primary indicators and 7 secondary indicators, while the quantitative indicators include 3 primary indicators and 14 secondary indicators.

[0037] The beneficial effects of this invention are:

[0038] 1. More Comprehensive and Systematic Performance Indicators: Based on thorough research and demonstration, this invention supplements and adjusts the names, connotations, and scoring standards of existing indicators, dividing the indicator content into qualitative and quantitative categories. The qualitative indicators include three primary indicators: moral character, experimental teaching quality, and experimental technology and research capability. The quantitative indicators include three primary indicators: the amount of experimental teaching tasks undertaken, technological development and research achievements, and the level of laboratory construction and management. Each primary indicator includes several more specific secondary indicators. This makes the performance indicators more comprehensive and systematic, and better suited to the work characteristics and task requirements of experimental technicians.

[0039] 2. Performance Appraisal Indicators by Category and Position: Existing technologies do not categorize and assess experimental technicians by position. Experimental technicians undertake diverse tasks, and each individual's workload varies in different areas, making it difficult to use a single standard for assessment. This invention establishes common assessment points for three categories of experimental technicians: experimental teaching, public technical services, and laboratory construction and management. It clarifies the baseline workload for each position and emphasizes assessment based on job responsibilities. This better reflects the work performance and contributions of experimental technicians in different positions.

[0040] 3. Considering the correlation between indicators: Traditional AHP hierarchical analysis models cannot consider the correlation between indicators. This invention fully considers the correlation between indicators when establishing the indicator network structure model. By analyzing the complex correlations between indicators, including unidirectional and bidirectional pointing methods, and four manifestations: one-to-one, one-to-many, many-to-one, and many-to-many, a complete ANP network structure model is established. This can more accurately reflect the interrelationships and influences between indicators.

[0041] 4. Introducing an Improved ANP Method for Weight Calculation: The traditional ANP method is susceptible to subjective interference, and its weight calculation process is not scientific enough. This invention reconstructs the ANP calculation process by introducing fuzzy theory, the TOPSIS model, the entropy weight method, and a combined weighting method. By introducing fuzzy theory, the subjective factors of humans are reduced; the TOPSIS model is used to further calculate the weight ranking results; the entropy weight method is used to calculate a set of weight ranking results; and the combined weighting method comprehensively considers both subjective and objective factors, making the weight calculation results more scientific and accurate.

[0042] 5. More Objective and Accurate Performance Appraisal Results: Existing performance appraisal data is calculated and statistically analyzed using Excel. This invention employs Fuzzy Linear Transformation to statistically analyze the data during the performance appraisal process, abandoning the traditional method of taking the arithmetic mean and constructing a secondary indicator evaluation matrix. By determining the scoring results of indicators based on the primary indicator evaluation vector and the overall evaluation vector, according to the principle of maximizing membership, the subjectivity of scoring is reduced, ensuring the objectivity and accuracy of the appraisal results.

[0043] In summary, this invention, by fully considering the scientific nature of the performance indicator content and weight calculation, can more comprehensively and systematically evaluate the work performance and contributions of laboratory technicians. It solves the problems of existing technologies, such as insufficiently comprehensive and systematic indicator content, inadequate indicator classification and job assignment, inability to consider the correlation between indicators, subjective interference, and unscientific weight calculation processes. This invention can more accurately assess the performance level of laboratory technicians, improve the quality of experimental teaching and scientific research, and promote the improvement of laboratory construction and management.

[0044] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Fig. 1 This is a schematic diagram of the performance appraisal method of the present invention;

[0047] Fig. 2 This is a schematic diagram of the performance evaluation indicator weight calculation model of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1

[0050] like Figs. 1-2 As shown

[0051] The performance evaluation method for university experimental technicians based on an improved ANP, as described in this embodiment, includes the following steps:

[0052] S1: Establish a network structure model based on ANP for the specific content of 6 primary indicators and 21 secondary indicators.

[0053] S2: Based on the ANP method, fuzzy theory, TOPSIS model, entropy weight method and combined weighting method are introduced to reconstruct the calculation process of the ANP method and establish a "weight calculation model for performance evaluation indicators of experimental technicians based on improved ANP".

[0054] S3: Design a questionnaire and use an indicator weight calculation model based on the improved ANP to calculate the indicator weight allocation scheme.

[0055] S4: Based on the indicator weight allocation scheme, conduct performance appraisal of experimental technicians, use fuzzy linear transformation method for data statistics and analysis, and the data obtained is the final performance appraisal result.

[0056] In this embodiment, step S1 specifically includes the following sub-steps:

[0057] S1.1: Analyze the complex relationships between the 6 primary indicators and 21 secondary indicators. The relationships include two types of pointing methods: one-way and two-way, as well as four forms of expression: one-to-one, one-to-many, many-to-one, and many-to-many. Therefore, there may be a maximum of 8 relationship types.

[0058] S1.2: Establish a complete ANP network structure model based on the indicator hierarchy and the correlation between indicators. The model includes the criterion layer, the network layer, and the correlation method of each indicator.

[0059] In this embodiment, step S2 specifically includes the following sub-steps:

[0060] S2.1: In the process of constructing the ANP judgment matrix, the "1-9 scale" commonly used in the traditional ANP method is abandoned. Fuzzy theory is introduced to reduce the subjective factors of humans, thereby obtaining a more realistic and accurate judgment matrix.

[0061] S2.2: The TOPSIS model is used to further calculate the weight ranking results obtained by the ANP method combined with fuzzy theory, so as to make the weight ranking results more objective.

[0062] S2.3: After obtaining a set of weighted ranking results using the ANP method that combines fuzzy theory and the TOPSIS model, use the entropy weight method to calculate another set of weighted ranking results.

[0063] S2.4: The ANP method, which combines fuzzy theory and the TOPSIS model, is used as the "subjective weight," and the entropy weight method is used as the "objective weight." The combined weighting method is used to encapsulate the entire calculation process, comprehensively considering both subjective and objective factors, making the calculation results more authoritative.

[0064] S2.5: Combining sub-steps S2.1-S2.4, the calculation process of the ANP method is reconstructed by introducing fuzzy theory, TOPSIS model, entropy weight method and combined weighting method, and a "weight calculation model for performance evaluation indicators of experimental technicians based on improved ANP" is established.

[0065] In this embodiment, step S3 specifically includes the following sub-steps:

[0066] S3.1: Design a questionnaire that includes direct pairwise dominance comparisons between 6 primary indicators and 21 secondary indicators, as well as indirect pairwise dominance comparisons relative to a third element. Based on the "1-9 scale", introduce fuzzy theory and use triangular fuzzy numbers or trapezoidal fuzzy numbers to define the upper and lower limits of the relative importance between elements.

[0067] S3.2: Import all questionnaire results into the professional calculation software yaanp for group decision aggregation, and split the judgment matrix into a primary indicator judgment matrix and a secondary indicator judgment matrix.

[0068] S3.3: Calculate the weighted matrix from the first-level indicator judgment matrix, obtain the unweighted judgment matrix from the second-level indicator judgment matrix, and merge the two to obtain the weighted super matrix.

[0069] S3.4: Perform limit operations on the weighted hypermatrix until all elements in the matrix have been calculated to the limit to obtain the limit hypermatrix. Combine the eigenvectors of each limit hypermatrix to form the initial weight sorting result.

[0070] S3.5: Substitute the initial weight ranking results into the TOPSIS model, calculate the weighted Euclidean distance and relative proximity between the initial weights and the positive and negative ideal solutions, and obtain the final weight ranking results, which are the "subjective weights" in the combined weighting method.

[0071] S3.6: Using the entropy weight method, the performance appraisal indicators are standardized, and then the information entropy of the 6 primary indicators and 21 secondary indicators is calculated. The weight ranking result is then calculated again, which is the "objective weight" in the combined weighting method.

[0072] S3.7: Use the combined weighting method to calculate the importance coefficients of subjective weights and objective weights respectively, and obtain the "weight allocation scheme for performance appraisal indicators of experimental technicians based on improved ANP".

[0073] In this embodiment, step S4 specifically includes the following sub-steps:

[0074] S4.1: Use the "Performance Appraisal Index Weighting Scheme for Experimental Technicians Based on Improved ANP" to score the performance of experimental technicians.

[0075] S4.2: Using the Fuzzy linear transformation method, when scoring the statistical indicators, the method of taking the arithmetic mean is abandoned, and a secondary indicator evaluation matrix is ​​constructed.

[0076] S4.3: Based on the primary indicator evaluation vector and the overall evaluation vector, determine the scoring results of the indicators according to the principle of maximizing membership, so as to minimize the subjectivity of the scoring and ensure that the assessment results accurately reflect the actual performance level of the experimental technicians.

[0077] S4.4: Publish the performance appraisal results, accept feedback, and continuously revise and improve the content, weighting, and methods of the performance appraisal indicators.

[0078] In this embodiment, the indicators are divided into qualitative and quantitative parts. The qualitative part includes common assessment points for three types of positions: experimental teaching, public technical services, and laboratory construction and management. The quantitative part focuses on assessing the responsibilities of each of the three types of positions. A total of 6 primary indicators and 21 secondary indicators are included. Specifically, the qualitative indicators include 3 primary indicators and 7 secondary indicators, while the quantitative indicators include 3 primary indicators and 14 secondary indicators, as shown in Table 1.

[0079] Table 1. Performance Appraisal Indicators for Laboratory Technicians

[0080]

[0081]

[0082] Example 2

[0083] Using the assessment method of this invention, a two-year trial run was conducted at our school. The average performance assessment score of the 36 experimental technicians who participated in the trial run increased by 2.62 points (out of 100) over the two years, with a positive growth rate of 100%, as shown in Table 2.

[0084] Table 2 Results of the Trial Operation Assessment for Experimental Technicians

[0085]

[0086]

[0087]

[0088] In summary, this invention, by fully considering the scientific nature of the performance indicator content and weight calculation, can more comprehensively and systematically evaluate the work performance and contributions of laboratory technicians. It solves the problems of existing technologies, such as insufficiently comprehensive and systematic indicator content, inadequate indicator classification and job assignment, inability to consider the correlation between indicators, subjective interference, and unscientific weight calculation processes. This invention can more accurately assess the performance level of laboratory technicians, improve the quality of experimental teaching and scientific research, and promote the improvement of laboratory construction and management.

[0089] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A performance evaluation method for university experimental technicians based on an improved ANP, characterized in that, Includes the following steps: S1: Establish a network structure model based on ANP for the specific content of 6 primary indicators and 21 secondary indicators; S2: Based on the ANP method, fuzzy theory, TOPSIS model, entropy weight method and combined weighting method are introduced to reconstruct the calculation process of the ANP method and establish a "weight calculation model for performance evaluation indicators of experimental technicians based on improved ANP". S3: Design a questionnaire and use an indicator weight calculation model based on the improved ANP to calculate the indicator weight allocation scheme. S4: Based on the indicator weight allocation scheme, conduct performance appraisal of experimental technicians, use fuzzy linear transformation method for data statistics and analysis, and the data obtained is the final performance appraisal result.

2. The performance evaluation method for university experimental technicians based on improved ANP as described in claim 1, characterized in that: Step S1 specifically includes the following sub-steps: S1.1: Analyze the complex relationships between the 6 primary indicators and 21 secondary indicators. The relationships include two types of pointing methods: one-way and two-way, as well as four forms of expression: one-to-one, one-to-many, many-to-one, and many-to-many. Therefore, there may be a maximum of 8 types of relationships. S1.2: Establish a complete ANP network structure model based on the indicator hierarchy and the correlation between indicators. The model includes the criterion layer, the network layer, and the correlation method of each indicator.

3. The performance evaluation method for university experimental technicians based on improved ANP as described in claim 1, characterized in that: Step S2 specifically includes the following sub-steps: S2.1: In the process of constructing the ANP judgment matrix, the "1-9 scale" commonly used in the traditional ANP method is abandoned, and fuzzy theory is introduced to reduce the subjective factors of humans. S2.2: The TOPSIS model is used to further calculate the weight ranking results obtained by the ANP method combined with fuzzy theory; S2.3: After obtaining a set of weighted ranking results using the ANP method that combines fuzzy theory and the TOPSIS model, use the entropy weight method to calculate another set of weighted ranking results. S2.4: The ANP method, which combines fuzzy theory and the TOPSIS model, is used as the "subjective weights," and the entropy weight method is used as the "objective weights." The entire calculation process is encapsulated using a combined weighting method. S2.5: Combining sub-steps S2.1-S2.4, the calculation process of the ANP method is reconstructed by introducing fuzzy theory, TOPSIS model, entropy weight method and combined weighting method, and a "weight calculation model for performance evaluation indicators of experimental technicians based on improved ANP" is established.

4. The performance evaluation method for university experimental technicians based on improved ANP as described in claim 1, characterized in that: Step S3 specifically includes the following sub-steps: S3.1: Design a questionnaire that includes direct pairwise dominance comparisons between 6 primary indicators and 21 secondary indicators, as well as indirect pairwise dominance comparisons relative to a third element. Based on the "1-9 scale", introduce fuzzy theory and use triangular fuzzy numbers or trapezoidal fuzzy numbers to define the upper and lower limits of the relative importance between elements. S3.2: Import all questionnaire results into the professional calculation software yaanp for group decision aggregation, and split the judgment matrix into a primary indicator judgment matrix and a secondary indicator judgment matrix; S3.3: Calculate the weighted matrix from the first-level indicator judgment matrix, obtain the unweighted judgment matrix from the second-level indicator judgment matrix, and merge the two to obtain the weighted supermatrix; S3.4: Perform limit operations on the weighted hypermatrix until all elements in the matrix have been calculated to the limit to obtain the limit hypermatrix. Combine the eigenvectors of each limit hypermatrix to form the initial weight sorting result. S3.5: Substitute the initial weight ranking results into the TOPSIS model, calculate the weighted Euclidean distance and relative proximity between the initial weights and the positive and negative ideal solutions, and obtain the final weight ranking results, which is the "subjective weight" in the combined weighting method. S3.6: Using the entropy weight method, the performance appraisal indicators are standardized, and then the information entropy of the 6 primary indicators and 21 secondary indicators is calculated. The weight ranking result is then calculated again, which is the "objective weight" in the combined weighting method. S3.7: Use the combined weighting method to calculate the importance coefficients of subjective weights and objective weights respectively, and obtain the "weight allocation scheme of performance appraisal indicators for experimental technicians based on improved ANP".

5. The performance evaluation method for university experimental technicians based on improved ANP as described in claim 2, characterized in that: Step S4 specifically includes the following sub-steps: S4.1: Use the "Performance Appraisal Index Weighting Scheme for Experimental Technicians Based on Improved ANP" to score the performance of experimental technicians; S4.2: Construct a two-level index evaluation matrix using the Fuzzy linear transformation method; S4.3: Based on the evaluation vector of the primary indicators and the overall evaluation vector, determine the scoring results of the indicators according to the principle of maximizing the membership degree; S4.4: Publish the performance appraisal results, accept feedback, and continuously revise and improve the content, weighting, and methods of the performance appraisal indicators.

6. The performance evaluation method for university experimental technicians based on improved ANP as described in claim 4, characterized in that: The indicators are divided into two parts: qualitative and quantitative. The qualitative part covers the common assessment points for three types of positions: experimental teaching, public technical services, and laboratory construction and management. The quantitative part focuses on the assessment based on the responsibilities of the three types of positions. There are a total of 6 primary indicators and 21 secondary indicators. Among them, the qualitative indicators include 3 primary indicators and 7 secondary indicators, and the quantitative indicators include 3 primary indicators and 14 secondary indicators.

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