CN117670610A - College teaching laboratory use benefit assessment method based on ANP-FCE - Google Patents
College teaching laboratory use benefit assessment method based on ANP-FCE Download PDFInfo
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Abstract
The invention relates to the technical field of laboratory assessment, in particular to an ANP-FCE-based college teaching laboratory use benefit assessment method, which comprises the following steps: s1: establishing corresponding FCE fuzzy sets aiming at specific contents of 32 three-level indexes, and focusing on qualitative indexes; s2: constructing an ANP network structure model, calculating to obtain an index weight distribution scheme based on ANP, and converting the index weight distribution scheme into an FCE weight distribution vector; s3: marking a target teaching laboratory by referring to the FCE comment set obtained in the step S1 and the ANP weight distribution scheme obtained in the step S2 to obtain FCE basic data; s4: performing fuzzy synthesis algorithm selection and calculation analysis on the FCE weight distribution vector obtained in the step S2 and the FCE basic data obtained in the step S3 to obtain a final FCE calculation result, namely a use benefit evaluation result of the teaching laboratory; the comprehensive utilization benefits of the laboratory can be comprehensively evaluated by comprehensively considering various aspects of the laboratory, including indexes such as teaching effects, management efficiency, equipment conditions and the like.
Description
Technical Field
The invention relates to the technical field of laboratory assessment, in particular to an ANP-FCE-based college teaching laboratory use benefit assessment method.
Background
The higher school laboratory is a teaching or scientific research entity which belongs to school or depends on school management, and is used for experimental teaching or scientific research, production test and technical development. Laboratory assessment is an important link and means of laboratory work value judgment in universities and scientific management in universities, and has increasingly attracted attention from educational administration departments at all levels and from laboratory workers.
Laboratory construction and management plays an increasingly important role in talent culture. The laboratory use benefit evaluation is an evaluation means for various achievements obtained in the use process of the laboratory, including economic benefit, social benefit, environmental benefit and the like. The method establishes a scientific and sound college teaching laboratory use benefit evaluation system, actively develops college laboratory benefit evaluation, and has extremely high practical significance for promoting laboratory construction and management, improving laboratory utilization and exerting laboratory functions to the maximum extent. The university teaching laboratory benefit evaluation system constructed in the prior art has the following problems:
1. the application range is limited: the existing research results mainly pay attention to assessment of national-level and provincial key laboratories or military universities, and are not applicable to teaching laboratories of wide universities. This is because laboratories of different levels have different characteristics and evaluation criteria, and the research results cannot be simply applied to teaching laboratories of other universities.
2. The index weight calculation method is unreasonable: the prior art often uses the AHP method to calculate the weights of the metrics. However, the AHP method has certain limitations, mainly expressed in: (1) The inter-dependency relationship between indexes is not accurately processed, and complex association and influence between indexes cannot be fully considered; (2) The weight calculation process lacks scientificity, is sometimes based on subjective judgment of subjective sense, and lacks objective basis and data support; (3) Is easily affected by subjectivity and prejudice of expert opinion, and lacks consistency and reliability.
3. Qualitative index evaluation is difficult to quantify: in laboratory benefit assessment, some of the indicators have qualitative properties, such as teaching effects and management efficacy. However, the evaluation of these qualitative indicators is difficult to accurately quantify in the prior art, resulting in lack of objectivity and comparability of the evaluation results. This is mainly due to the lack of suitable methods and tools to convert qualitative to quantitative indicators.
The difficulty in solving these problems is the complexity and multi-dimensionality of the laboratory assessment that needs to be addressed. College teaching laboratories are involved in evaluating metrics including teaching effectiveness, management efficiency, equipment status, etc., where complex correlations and dependencies exist between these metrics. Meanwhile, expert experience and data analysis are needed in the evaluation process, and actual conditions and characteristics of a laboratory are needed to be fully considered. Therefore, to solve the above problems, it is necessary to comprehensively use various methods and techniques, build a scientific and reasonable evaluation system, and perform weight calculation and quantitative treatment of qualitative indexes by using a suitable method.
Aiming at the problems, the invention provides an ANP-FCE-based college teaching laboratory use benefit evaluation method, which promotes construction and management of a college laboratory, improves the utilization rate of the laboratory, exerts the talent culture, scientific research and social service of the laboratory to the maximum extent, and has practical significance and popularization value.
Disclosure of Invention
The invention aims to provide an ANP-FCE-based college teaching laboratory use benefit evaluation method, which adopts an ANP (network analysis method) as an evaluation index weight calculation method, avoids limitations and defects of AHP (analytic hierarchy process) used in the prior art, and improves the scientificity and rationality of an index weight distribution scheme. And the FCE (fuzzy comprehensive evaluation method) is adopted as an evaluation mode of all evaluation indexes, so that quantitative evaluation of qualitative indexes is realized, and the accuracy and the authenticity of an evaluation result are improved.
In order to achieve the technical purpose and the technical effect, the invention is realized by the following technical scheme:
an ANP-FCE-based college teaching laboratory use benefit evaluation method comprises the following steps:
s1: corresponding FCE fuzzy sets (FCE factor sets and FCE comment sets) are established for specific contents of the 32 three-level indexes, and qualitative indexes are considered seriously.
S2: and constructing an ANP network structure model, calculating to obtain an index weight distribution scheme based on ANP, and converting the index weight distribution scheme into an FCE weight distribution vector.
S3: and (3) scoring the target teaching laboratory by referring to the FCE comment set obtained in the step (S1) and the ANP weight distribution scheme obtained in the step (S2) to obtain FCE basic data.
S4: and (3) selecting and calculating and analyzing the FCE weight distribution vector obtained in the step (S2) and the FCE basic data obtained in the step (S3) by a fuzzy synthesis algorithm to obtain a final calculation result of the FCE, namely a use benefit evaluation result of the teaching laboratory.
Further, the step S1 specifically includes the following substeps:
s1.1: establishing a fuzzy comprehensive evaluation factor set U= { U 1 ,u 2 ,…,u l I=32, where u 1 Laboratory utilization, u 2 Laboratory openness, and so on, corresponds to 32 tertiary index contents, respectively.
S1.2: establishing a fuzzy comprehensive evaluation comment set V= { V 1 ,v 2 ,…,v m M=5, where v 1 Excellent, v 2 Good, v 3 In general, v 4 Acceptable, v 5 Poor =.
S1.3: further refine the comment set discrimination degree about qualitative indexes in all indexes, v= { V 1 ,v 2 ,…,v n N=7, where n 1 =very excellent, n 2 Excellent, n 3 Good, n 4 N is =in general 5 Acceptable, n 6 Poor, n 7 =very bad.
Further, the step S2 specifically includes the following substeps:
s2.1: an ANP network structure model is constructed for each level of index, comprising a criterion layer, a network layer and the interrelation of each index.
S2.2: designing a questionnaire, and comparing and judging the 32 three-level evaluation indexes one by adopting a 1-9 scale according to the content of the ANP network structure model to obtain the judgment matrix original data required by calculation, wherein the judgment matrix original data comprises a control layer judgment matrix, a cluster judgment matrix and a node judgment matrix.
S2.3: and importing the original data contained in all the collected questionnaires into a professional auxiliary tool yaanp for group decision aggregation to obtain a judgment matrix, wherein the judgment matrix comprises an element judgment matrix and an element group judgment matrix.
S2.3: and calculating the element judgment matrix by using a feature root method, a normalized feature vector and consistency test successively to obtain a cluster weight super matrix, and then combining the cluster weight super matrices corresponding to each cluster in the ANP network structure model to obtain an unweighted super matrix.
S2.4: and calculating the element group judgment matrix by using a feature root method, a normalized feature vector and consistency test in sequence to obtain a weighting matrix.
S2.5: and (5) weighting the unweighted super matrix obtained in the step (S2.3) by using the weighting matrix obtained in the step (S2.4) to obtain the weighted super matrix.
S2.6: repeatedly multiplying the weighted supermatrixes to obtain limit supermatrixes, wherein the feature vector of each limit supermatrix is a local weight, and combining all the local weights to obtain a global weight.
S2.7: performing sensitivity analysis on the global weight to obtain an index weight distribution scheme based on ANP, namely a percentage system distribution scheme A= { a of 4 primary indexes, 11 secondary indexes and 32 tertiary indexes 1 ,a 2 ,…,a l },l＝32。
S2.8: the ANP weight distribution scheme a= { a obtained in S2.7 1 ,a 2 ,…,a l FCE factor set u= { U established by l=32 and S1.1 } 1 ,u 2 ,…,u l I=32, and establishes a one-to-one multiplication relationship.
S2.9: obtain FCE weight distribution vector B= { a 1 u 1 ,a 2 u 2 ,…,a l u l },l＝32。
Further, the step S3 specifically includes the following substeps:
s3.1: 4 first-order indexes are scored by referring to the FCE comment set obtained in the step S1 and the ANP weight distribution scheme obtained in the step S2.
S3.2: on the basis of the scoring of S3.1, 11 secondary indexes are scored.
S3.3: 32 tertiary indexes are scored on the basis of the scoring of S3.2.
S3.4: and aggregating scoring result data of S3.1, S3.2 and S3.3 to obtain fundamental data of FCE (fuzzy comprehensive evaluation method).
Further, the step S4 specifically includes the following substeps:
s4.1: the FCE fuzzy set (FCE factor set and FCE comment set) obtained in S1 and the FCE weight distribution vector obtained in S2.9 are imported into the professional auxiliary tool yafce.
S4.2: and (3) importing all scoring result data into a professional auxiliary tool yafce, and carrying out group decision aggregation to obtain a fuzzy comprehensive evaluation matrix.
S4.3: according to the actual demand, selecting among the following 4 fuzzy synthesis algorithms: principal factor determination type, multiplication+amplification, subtraction+summation and weighted average, and then the fuzzy comprehensive evaluation matrix is calculated.
S4.4: and determining a final calculation result of the FCE, namely a use benefit evaluation result of the teaching laboratory according to the membership maximization principle.
The invention has the beneficial effects that:
comprehensive evaluation of laboratory benefits: the traditional evaluation method is usually only focused on evaluation in a certain aspect, and the ANP-FCE-based method can comprehensively consider various aspects of the laboratory, including indexes such as teaching effect, management efficiency, equipment condition and the like, and comprehensively evaluate comprehensive use benefits of the laboratory; for the satisfaction survey of "ANP-FCE based college teaching laboratory use benefit assessment method", the "very satisfactory" was improved by 20.45% (now 23.14%), "satisfactory" was improved by 30.91% (now 48.98%), "general" was reduced by 30.60% (now 27.88%), "unsatisfactory" was reduced by 20.76% (now 0%).
The calculation accuracy of the index weight is improved: the adoption of the ANP method for index weight calculation can more accurately process the interdependence relationship among indexes compared with the traditional AHP method. The ANP method can fully consider complex association and influence among indexes, and a scientific and reasonable index weight distribution scheme is obtained through specific steps of constructing a judgment matrix, calculating a feature vector and the like.
Quantitative evaluation of qualitative indexes: in laboratory benefit evaluation, qualitative indexes such as teaching effect and management efficiency often exist. Based on the ANP-FCE method, the qualitative indexes can be accurately and quantitatively evaluated, and subjective evaluation is converted into objective quantitative indexes through the FCE method. Therefore, the accuracy and comparability of the evaluation result can be improved, and the evaluation result is more scientific and accurate.
The scientificity and reliability of the evaluation result are improved: conventional assessment methods are often susceptible to expert subjective opinion and bias, and lack consistency and reliability. By adopting an ANP-FCE-based method and analyzing the opinion and data of an expert, mathematical modeling and quantitative analysis are utilized, so that the influence of subjective factors is reduced, and the scientificity and accuracy of an evaluation result are improved. Meanwhile, the method can also perform sensitivity analysis, and the reliability and stability of the result are evaluated.
Promote the development of laboratory construction and management: the method based on the ANP-FCE can provide scientific basis for construction and management of university laboratories. Through the evaluation result, the problems and defects of a laboratory can be found in time, the improvement of laboratory construction and management is guided, the utilization rate and benefit of the laboratory are improved, and the development of experimental teaching and scientific research is promoted.
In summary, the ANP-FCE-based college teaching laboratory use benefit evaluation method has the professional beneficial effects of improving the scientificity, the accuracy and the reliability of evaluation in practice. The method comprehensively considers indexes in multiple aspects, accurately calculates the index weight, realizes quantitative evaluation of qualitative indexes, provides scientific basis for laboratory construction and management, and has important professional value for promoting laboratory development and improving benefit.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a college teaching laboratory use benefit evaluation method based on ANP-FCE;
FIG. 2 is a schematic diagram showing steps for calculating an ANP-based index weight distribution scheme according to the present invention;
fig. 3 is a schematic diagram of a flow chart for formulating an index evaluation scheme based on FCE according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1-3
The method for evaluating the use benefits of the college teaching laboratory based on the ANP-FCE comprises the following steps:
s1: corresponding FCE fuzzy sets (FCE factor sets and FCE comment sets) are established for specific contents of the 32 three-level indexes, and qualitative indexes are considered seriously.
S2: and constructing an ANP network structure model, calculating to obtain an index weight distribution scheme based on ANP, and converting the index weight distribution scheme into an FCE weight distribution vector.
S3: and (3) scoring the target teaching laboratory by referring to the FCE comment set obtained in the step (S1) and the ANP weight distribution scheme obtained in the step (S2) to obtain FCE basic data.
S4: and (3) selecting and calculating and analyzing the FCE weight distribution vector obtained in the step (S2) and the FCE basic data obtained in the step (S3) by a fuzzy synthesis algorithm to obtain a final calculation result of the FCE, namely a use benefit evaluation result of the teaching laboratory.
Further, the step S1 specifically includes the following substeps:
s1.1: establishing a fuzzy comprehensive evaluation factor set U= { U 1 ,u 2 ,…,u l I=32, where u 1 Laboratory utilization, u 2 Laboratory openness, and so on, corresponds to 32 tertiary index contents, respectively.
S1.2: establishing a fuzzy comprehensive evaluation comment set V= { V 1 ,v 2 ,…,v m M=5, where v 1 Excellent, v 2 Good, v 3 In general, v 4 Acceptable, v 5 Poor =.
S1.3: further refine the comment set discrimination degree about qualitative indexes in all indexes, v= { V 1 ,v 2 ,…,v n N=7, where n 1 =very excellent, n 2 Excellent, n 3 Good, n 4 N is =in general 5 Acceptable, n 6 Poor, n 7 =very bad.
Further, the step S2 specifically includes the following substeps:
s2.1: an ANP network structure model is constructed for each level of index, comprising a criterion layer, a network layer and the interrelation of each index.
S2.2: designing a questionnaire, and comparing and judging the 32 three-level evaluation indexes one by adopting a 1-9 scale according to the content of the ANP network structure model to obtain the judgment matrix original data required by calculation, wherein the judgment matrix original data comprises a control layer judgment matrix, a cluster judgment matrix and a node judgment matrix.
S2.3: and importing the original data contained in all the collected questionnaires into a professional auxiliary tool yaanp for group decision aggregation to obtain a judgment matrix, wherein the judgment matrix comprises an element judgment matrix and an element group judgment matrix.
S2.3: and calculating the element judgment matrix by using a feature root method, a normalized feature vector and consistency test successively to obtain a cluster weight super matrix, and then combining the cluster weight super matrices corresponding to each cluster in the ANP network structure model to obtain an unweighted super matrix.
S2.4: and calculating the element group judgment matrix by using a feature root method, a normalized feature vector and consistency test in sequence to obtain a weighting matrix.
S2.5: and (5) weighting the unweighted super matrix obtained in the step (S2.3) by using the weighting matrix obtained in the step (S2.4) to obtain the weighted super matrix.
S2.6: repeatedly multiplying the weighted supermatrixes to obtain limit supermatrixes, wherein the feature vector of each limit supermatrix is a local weight, and combining all the local weights to obtain a global weight.
S2.7: performing sensitivity analysis on the global weight to obtain an index weight distribution scheme based on ANP, namely a percentage system distribution scheme A= { a of 4 primary indexes, 11 secondary indexes and 32 tertiary indexes 1 ,a 2 ,…,a l },l＝32。
S2.8: the ANP weight distribution scheme a= { a obtained in S2.7 1 ,a 2 ,…,a l FCE factor set u= { U established by l=32 and S1.1 } 1 ,u 2 ,…,u l I=32, and establishes a one-to-one multiplication relationship.
S2.9: obtain FCE weight distribution vector B= { a 1 u 1 ,a 2 u 2 ,…,a l u l },l＝32。
Further, the step S3 specifically includes the following substeps:
s3.1: 4 first-order indexes are scored by referring to the FCE comment set obtained in the step S1 and the ANP weight distribution scheme obtained in the step S2.
S3.2: on the basis of the scoring of S3.1, 11 secondary indexes are scored.
S3.3: 32 tertiary indexes are scored on the basis of the scoring of S3.2.
S3.4: and aggregating scoring result data of S3.1, S3.2 and S3.3 to obtain fundamental data of FCE (fuzzy comprehensive evaluation method).
Further, the step S4 specifically includes the following substeps:
s4.1: the FCE fuzzy set (FCE factor set and FCE comment set) obtained in S1 and the FCE weight distribution vector obtained in S2.9 are imported into the professional auxiliary tool yafce.
S4.2: and (3) importing all scoring result data into a professional auxiliary tool yafce, and carrying out group decision aggregation to obtain a fuzzy comprehensive evaluation matrix.
S4.3: according to the actual demand, selecting among the following 4 fuzzy synthesis algorithms: principal factor determination type, multiplication+amplification, subtraction+summation and weighted average, and then the fuzzy comprehensive evaluation matrix is calculated.
S4.4: and determining a final calculation result of the FCE, namely a use benefit evaluation result of the teaching laboratory according to the membership maximization principle.
Example 2
The evaluation index system proposed by the research in the current domestic related literature is fully referenced, and the content of the designed evaluation index is shown in table 1 in combination with the condition of the current domestic college teaching laboratory. Comprises 4 first-level indexes, 11 second-level indexes and 32 third-level indexes.
Table 1 index content
(1) The network analysis method (ANP) is adopted to replace an Analytic Hierarchy Process (AHP) as a calculation method of evaluating the index weight, on the basis of fully considering the AHP criterion, the interrelation of each element in the criterion layer is further considered, the complex relationship among each index is clearly and accurately described, the problem that the original evaluation index weight calculation method has limitation is well solved, and the index weight distribution result is more scientific and reasonable.
(2) The fuzzy comprehensive evaluation method (FCE) is adopted as an evaluation mode of all evaluation indexes, the evaluation of qualitative indexes is converted into quantitative evaluation according to the membership theory of fuzzy mathematics, namely, the fuzzy mathematics are used for carrying out overall evaluation on objects and qualitative evaluation indexes which are restricted by various factors, so that the problem that the evaluation of the qualitative indexes is difficult to quantify is solved, and the accuracy and convincing of an evaluation result are improved.
For the satisfaction survey of "ANP-FCE based college teaching laboratory use benefit assessment method", as shown in table 2, "very satisfactory" was improved by 20.45% (now 23.14%), "satisfactory" was improved by 30.91% (now 48.98%), "general" was reduced by 30.60% (now 27.88%), "unsatisfactory" was reduced by 20.76% (now 0%) compared to before.
Table 2 evaluation method satisfaction contrast
In summary, the method for evaluating the use benefits of the university teaching laboratory based on the ANP-FCE provided by the invention adopts the ANP (network analysis method) as the calculation method for evaluating the index weight, avoids the limitations and defects of the AHP (analytic hierarchy process) used in the prior art, and improves the scientificity and rationality of the index weight distribution scheme; the method solves the problems that the calculation method of index weight in the prior art is not scientific enough, the AHP analytic hierarchy process has a plurality of defects, the relevance among indexes cannot be considered, and the interference caused by subjective factors cannot be avoided; the invention adopts FCE (fuzzy comprehensive evaluation method) as the evaluation mode of all evaluation indexes, realizes quantitative evaluation of qualitative indexes, and improves the accuracy and the authenticity of the evaluation result; the problem that the qualitative index evaluation is difficult to quantify in the prior art is solved, and a corresponding solution is provided. For qualitative indexes, an expert cannot give accurate and real evaluation, so that the reference value of an evaluation result can be directly influenced, and the problem of guiding effect of evaluation work is weakened.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. The college teaching laboratory use benefit evaluation method based on the ANP-FCE is characterized by comprising the following steps of:
s1: establishing corresponding FCE fuzzy sets aiming at specific contents of 32 three-level indexes, wherein the FCE fuzzy sets comprise FCE factor sets and FCE comment sets, and focusing on qualitative indexes;
s2: constructing an ANP network structure model, calculating to obtain an index weight distribution scheme based on ANP, and converting the index weight distribution scheme into an FCE weight distribution vector;
s3: marking a target teaching laboratory by referring to the FCE comment set obtained in the step S1 and the ANP weight distribution scheme obtained in the step S2 to obtain FCE basic data;
s4: and (3) selecting and calculating and analyzing the FCE weight distribution vector obtained in the step (S2) and the FCE basic data obtained in the step (S3) by a fuzzy synthesis algorithm to obtain a final calculation result of the FCE, namely a use benefit evaluation result of the teaching laboratory.
2. The method for evaluating the use benefits of the college teaching laboratory based on the ANP-FCE according to claim 1, wherein the step S1 specifically comprises the following substeps:
s1.1: establishing a fuzzy comprehensive evaluation factor set U= { U 1 ,u 2 ,…,u l I=32, where u 1 Laboratory utilization, u 2 The =laboratory openness, and so on corresponds to 32 tertiary index contents, respectively;
s1.2: establishing a fuzzy comprehensive evaluation comment set V= { V 1 ,v 2 ,…,v m M=5, where v 1 Excellent, v 2 Good, v 3 In general, v 4 Acceptable, v 5 Poor =;
s1.3: further refine the comment set discrimination degree about qualitative indexes in all indexes, v= { V 1 ,v 2 ,…,v n N=7, where n 1 =very excellent, n 2 Excellent, n 3 Good, n 4 N is =in general 5 Acceptable, n 6 Poor, n 7 =very bad.
3. The method for evaluating the use benefits of the college teaching laboratory based on the ANP-FCE according to claim 1, wherein the step S2 specifically comprises the following substeps:
s2.1: constructing an ANP network structure model for each level of index, wherein the model comprises a criterion layer, a network layer and the interrelation of each index;
s2.2: designing a questionnaire, and comparing and judging the 32 three-level evaluation indexes one by adopting a 1-9 scale according to the content of the ANP network structure model to obtain the original data of a judgment matrix required by calculation, wherein the original data comprises a control layer judgment matrix, a cluster judgment matrix and a node judgment matrix;
s2.3: leading the original data contained in all the collected questionnaires into a professional auxiliary tool yaanp for group decision aggregation to obtain a judgment matrix, wherein the judgment matrix comprises an element judgment matrix and an element group judgment matrix;
s2.3: calculating the element judgment matrix by using a feature root method, a normalized feature vector and consistency test in sequence to obtain a cluster weight super matrix, and then combining the cluster weight super matrices corresponding to each cluster in the ANP network structure model to obtain an unassigned super matrix;
s2.4: the element group judgment matrix is calculated by using a feature root method, a normalized feature vector and consistency check in sequence to obtain a weighting matrix;
s2.5: weighting the unweighted super matrix obtained in the step S2.3 by using the weighting matrix obtained in the step S2.4 to obtain an weighted super matrix;
s2.6: repeatedly multiplying the weighted supermatrixes to obtain limit supermatrixes, wherein the feature vector of each limit supermatrix is a local weight, and combining all the local weights to obtain a global weight;
s2.7: sensitivity analysis of global weightsObtaining an ANP-based index weight distribution scheme, namely a percentage system distribution scheme A= { a of 4 primary indexes, 11 secondary indexes and 32 tertiary indexes 1 ,a 2 ,…,a l },l＝32；
S2.8: the ANP weight distribution scheme a= { a obtained in S2.7 1 ,a 2 ,…,a l FCE factor set u= { U established by l=32 and S1.1 } 1 ,u 2 ,…,u l L=32 establishes a one-to-one multiplication relationship;
s2.9: obtain FCE weight distribution vector B= { a 1 u 1 ,a 2 u 2 ,…,a l u l },l＝32。
4. The ANP-FCE-based college teaching laboratory usage benefit assessment method according to claim 1, wherein said step S3 specifically comprises the sub-steps of:
s3.1: scoring the 4 first-level indexes by referring to the FCE comment set obtained in the step S1 and the ANP weight distribution scheme obtained in the step S2;
s3.2: scoring the 11 secondary indexes on the basis of scoring of S3.1;
s3.3: scoring the 32 tertiary indexes on the basis of scoring of S3.2;
s3.4: and aggregating scoring result data of S3.1, S3.2 and S3.3 to obtain fundamental data of FCE (fuzzy comprehensive evaluation method).
5. The method for evaluating the usage benefits of the college teaching laboratory based on ANP-FCE according to claim 1, wherein the step S4 specifically comprises the following substeps:
s4.1: introducing the FCE fuzzy set obtained in the step S1 and the FCE weight distribution vector obtained in the step S2.9 into a professional auxiliary tool yafce;
s4.2: all scoring result data are imported into a professional auxiliary tool yafce, and group decision aggregation is carried out to obtain a fuzzy comprehensive evaluation matrix;
s4.3: according to the actual demand, selecting among the following 4 fuzzy synthesis algorithms: main factor decision type, multiplication+amplification, subtraction+summation and weighted average, and then calculating a fuzzy comprehensive evaluation matrix;
s4.4: and determining a final calculation result of the FCE, namely a use benefit evaluation result of the teaching laboratory according to the membership maximization principle.
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