CN112766765A - Professional learning ability evaluation method and system based on interval middle intelligence theory - Google Patents
Professional learning ability evaluation method and system based on interval middle intelligence theory Download PDFInfo
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Abstract
本发明提供基于区间中智理论的专业学习能力评价方法，包括以下步骤：确定评价对象、评价指标、评审专家及其权重；确定指标权重：获取评审专家对评价指标的评价值，确定各指标的权重；对关键指标进行评价：获取评审专家的聚合后决策矩阵，设定关键指标并计算确定度，根据确定度阈值判断关键指标是否合格；综合优选评价：对关键指标评价合格的对象进行加权相似度计算，根据加权相似度排序，确定评价对象的专业学习能力。本发明还提出基于区间中智理论的专业学习能力评价系统。本发明将区间中智理论引入学生专业学习能力评价过程中，可有效提高决策的准确性和有效性；使权重分配更加合理。
The invention provides a professional learning ability evaluation method based on interval neutrosophic theory, which includes the following steps: determining evaluation objects, evaluation indicators, evaluation experts and their weights; determining index weights: obtaining evaluation values of evaluation experts on evaluation indicators, and determining the value of each index. Weight; evaluate key indicators: obtain the aggregated decision matrix of review experts, set key indicators and calculate the certainty, and judge whether the key indicators are qualified according to the certainty threshold; comprehensive selection evaluation: weighted similarity of the objects that pass the evaluation of key indicators Degree calculation, according to the weighted similarity ranking, to determine the professional learning ability of the evaluation object. The invention also proposes a professional learning ability evaluation system based on interval neutrosophic theory. The invention introduces interval neutrosophic theory into the evaluation process of students' professional learning ability, which can effectively improve the accuracy and effectiveness of decision-making, and make the weight distribution more reasonable.
Description
Technical Field
The invention relates to the technical field of learning ability evaluation, in particular to a professional learning ability evaluation method and system based on an interval mesointelligence theory.
Background
In the process of professional talent selection such as professional shunting and experimental class building in colleges and universities, professional learning ability evaluation is generally needed to comprehensively investigate the comprehensive ability and quality of students. The evaluation index may be set according to an evaluation target.
At present, the commonly used evaluation methods mainly comprise a written test and a written test, wherein the written test is the talent selection performed by a score standard through an examination mode and belongs to a quantitative evaluation mode; the interview is that the students show the comprehensive ability of the students to experts through various means such as written material display, oral report and the like, and the experts judge in a qualitative mode through experience. In qualitative evaluation, the existing methods generally adopt a scoring mode, and this mode requires experts to give deterministic scores for some indexes with uncertainty and ambiguity (such as innovation capability, professional potential, etc.), so that the decision process of the experts is inconvenient and the results are inaccurate. Along with the development of a fuzzy theory, a plurality of fuzzy set expression concepts are proposed successively, fuzzy multi-index decision is made at the same time, and from the evaluation method, professional learning ability evaluation belongs to a typical fuzzy multi-index decision problem.
The existing achievements mainly focus on two aspects of construction of an evaluation index system and design of an evaluation method in research on comprehensive ability evaluation of students. For example, the construction problem of a comprehensive quality evaluation system for research of industrial students is solved by applying an analytic hierarchy process; in order to research the mathematic quality evaluation problem of college students, a grey fuzzy evaluation model is established; performing comprehensive ability evaluation on students according to an application principal component analysis method and intuitionistic fuzzy entropy; although the methods have certain feasibility in solving the qualitative evaluation of student ability, the methods have room for improvement in fuzzy expression of qualitative evaluation indexes and decision methods. For a specific decision problem, how to reasonably select a fuzzy value expression mode and what decision method to use all have direct influence on the reliability of a decision result.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides a professional learning ability evaluation method based on an interval mesomeric theory.
The scheme provided by the invention is as follows:
the professional learning ability evaluation method based on the interval mesopic theory comprises the following steps of:
determining an evaluation object, an evaluation index, an evaluation expert and the weight of the evaluation expert;
determining the index weight: acquiring evaluation values of evaluation indexes by evaluation experts, and determining the weight of each index;
and (3) evaluating key indexes: acquiring a decision matrix of a review expert after aggregation, setting key indexes, calculating the certainty factor, and judging whether the key indexes are qualified according to the certainty factor threshold;
comprehensive preference evaluation: and performing weighted similarity calculation on the objects qualified in the key index evaluation, and determining the professional learning ability of the evaluation objects according to the weighted similarity sequencing.
The evaluation indexes comprise professional basic ability, professional development potential, professional innovation consciousness, team cooperation consciousness and language expression ability, and the index weight is determined according to the evaluation value of the review expert.
The method comprises the following steps of obtaining evaluation values of evaluation indexes by evaluation experts, and determining the weight of each index; the method specifically comprises the following steps:
is provided with l evaluation objects (students) and is recorded as Si(i-1, 2, …, l) and g evaluation indices, denoted C, for each evaluation objectj(j-1, 2, …, g), and the corresponding weight is expressed as
The total number of the evaluation experts is q and is marked as Rk(k is 1, 2, …, q) and the corresponding weight is
The importance of the evaluation index is determined by an expert collective decision mode; each expert evaluates the importance of each index according to the linguistic variables; according to the corresponding relation, the evaluation result is converted into INV to obtain the following decision matrixDMz：
In the formula
Is INV;
decision matrix DMzDz in (2)kjIndicating that the k-th expert is directed to index CjA given evaluation value; for a certain index CjThe evaluation values of all experts may form a set Dj＝[dz1j，dz2j，…，dzkj](ii) a Giving the weight corresponding to each expert
Set intelligence in interval DjPolymerizing to obtain an importance evaluation set of each index after polymerization
Wherein
Calculating entropy E (C) corresponding to each indexj)；
The weight of each index is calculated by the following calculation formula:
the method comprises the following steps of acquiring a decision matrix of a review expert after aggregation, setting key indexes, calculating the certainty factor, and judging whether the key indexes are qualified according to a threshold value of the certainty factor; the method specifically comprises the following steps:
each expert gives an evaluation value to each index of each evaluation object according to the language variable, and k decision matrixes are obtained as follows:
in the formula (I), the compound is shown in the specification,
indicates the evaluation value given by the k-th expert, the elements of which
Represents the evaluation value given by the kth expert to the jth index of the ith evaluation object,
giving the weight corresponding to each expert
Will be provided with
According to the formula (1) aggregation, an aggregated decision matrix can be obtained:
in the formula
An evaluation value of a j-th index indicating an ith evaluation target;
and (4) setting the jth index as a key index, calculating the certainty factor of the jth index, and judging whether the key index is qualified or not according to the threshold value of the certainty factor.
Further, the calculation certainty is specifically calculated according to the following formula when a conservative decision is adopted:
setting delta as a certainty threshold when
And if not, the key index is qualified, otherwise, the key index is unqualified.
Further, the calculation certainty is specifically calculated according to the following formula when a risk-type decision is adopted:
setting delta as a certainty threshold when
And if not, the key index is qualified, otherwise, the key index is unqualified.
The further technical scheme of the invention is that the weighted similarity calculation is carried out on the objects qualified in the key index evaluation, and the professional learning ability of the evaluation objects is determined according to the weighted similarity sequencing; the method specifically comprises the following steps:
when the evaluation index CjWhen all the indexes are high-priority indexes, the optimal index set is determined according to the following formula:
calculating weighted similarity S (C) between each evaluation object and the optimal index setj,C+) (ii) a According to S (C)j,C+) And (4) sorting the sizes, and determining the professional learning capacity of each evaluation object.
The invention also provides a professional learning ability evaluation system based on the interval intelligent theory, which comprises the following steps:
the data module to be evaluated is used for storing the evaluation object, the evaluation index and the evaluation value thereof;
the index weight determining module is used for acquiring the evaluation value of the evaluation indexes by the evaluation experts and determining the weight of each index;
the key index evaluation module is used for acquiring a decision matrix of the evaluation experts after aggregation, setting key indexes, calculating the certainty factor and judging whether the key indexes are qualified or not according to the certainty factor threshold;
and the comprehensive optimization evaluation module is used for performing weighted similarity calculation on the objects qualified in key index evaluation, and determining the professional learning ability of the evaluation objects according to the weighted similarity sequencing.
The invention has the beneficial effects that:
the invention provides a professional learning ability evaluation method based on interval intelligent set cosine similarity measurement, which introduces an interval intelligent theory into a student professional learning ability evaluation process and can effectively improve the accuracy and effectiveness of decision making; the weight of each index is calculated by an intelligent set index entropy calculation method in the interval and based on the index entropy, so that the weight distribution is more reasonable; by two determination degree calculation methods, possibility is provided for key index evaluation; the cosine similarity measurement function is adopted to perform fuzzy relative evaluation, and a more reasonable result can be obtained.
Drawings
FIG. 1 is a flow chart of a professional learning ability evaluation method based on an interval mesopic theory, which is provided by the invention;
fig. 2 is a structural diagram of a professional learning ability evaluation system based on an interval middle intelligence theory according to the present invention.
Detailed Description
The conception, specific structure, and technical effects of the present invention will be described clearly and completely with reference to one embodiment and the accompanying drawings to fully understand the objects, features, and effects of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and those skilled in the art can obtain other embodiments without inventive effort based on the embodiments of the present invention, and all embodiments are within the protection scope of the present invention.
In the process of professional talent selection such as professional shunting and experimental class building in colleges and universities, professional learning ability evaluation is generally needed to comprehensively investigate the comprehensive ability and quality of students. The evaluation index may be set according to the evaluation target, and generally, the following 5 evaluation indexes may be considered: professional basic ability, professional development potential, professional innovation awareness, team cooperation awareness and language expression ability.
In the evaluation process, the evaluation is generally carried out according to the comprehensive performance of 5 indexes; however, sometimes the requirements of different specialties on talents are different, and some key indexes should be set at this time, for example, students should change from other specialties to computer specialties for learning, and when evaluating learning ability, professional basic ability (such as mathematical calculation and programming ability) should be set as the key index. If students need to go from other specialties to teachers and professions for learning, the language expression ability is set as a key index. Before the comprehensive ability evaluation, whether the key indexes meet the basic requirements is judged firstly, and if not, the key indexes are rejected by one ticket, so that the key indexes cannot participate in the comprehensive evaluation.
In the actual evaluation process, experts generally adopt linguistic variables to perform fuzzy evaluation on the 5 indexes. The linguistic variables can be divided into 7 levels, and each level is represented by the corresponding interval middle intelligence number, which is shown in table 1.
TABLE 1 correspondence between linguistic variables and INV
| Serial number | Language terminology | INV |
| 1 | Very good/important | <[0.85，0.95]，[0.05，0.15]，[0.05，0.15]> |
| 2 | Good/important | <[0.75，0.85]，[0.15，0.25]，[0.15，0.25]> |
| 3 | Better/important | <[0.65，0.75]，[0.25，0.35]，[0.25，0.35]> |
| 4 | In general | <[0.55，0.65]，[0.35，0.45]，[0.35，0.45]> |
| 5 | Poor/not important | <[0.45，0.55]，[0.45，0.55]，[0.45，0.55]> |
| 6 | Poor/not important | <[0.30，0.40]，[0.60，0.70]，[0.60，0.70]> |
| 7 | Very poor/not important | <[0.15，0.25]，[0.75，0.85]，[0.75，0.85]> |
The concept of the wisdom set in the interval related by the invention is as follows:
definition 1: let X be a given domain, X ═ { X1, X2, …, xm }; then an intelligent set (INS) N defined in an interval over the domain of discourse X may be represented in the form: n ═ tone<xj,TN(xj),IN(xj),FN(xj)>|xj∈X}；
In the formula, true membership function
Function of uncertainty
Function of false membership
And satisfy
Intelligently collecting basic elements in N in interval
It is briefly described as
Called interval Intelligence Number (INV).
Definition 2: is provided with
Then the following weighted aggregation operator INWA can be defined:
wherein wjIs hjWeight of (1), wj∈[0,1]，
Definition 3: let N, H be two INSs, the cosine weighted similarity between them can be calculated by:
wherein wjIs the weight of the jth element in the set N, H, wj∈[0,1]，
Definition 4: assuming that N is an INS, the entropy in the exponent may be defined as follows:
referring to fig. 1, it is a flow chart of a professional learning ability evaluation method based on the interval mesopic theory proposed in the present invention;
as shown in fig. 1, the professional learning ability evaluation method based on the interval intelligent theory includes the following steps:
The evaluation indexes comprise professional basic ability, professional development potential, professional innovation awareness, team cooperation awareness and language expression ability.
In step 102, obtaining evaluation values of evaluation indexes by evaluation experts, and determining the weight of each index; the method specifically comprises the following steps:
is provided with l evaluation objects (students) and is recorded as Si(i-1, 2, …, l) and g evaluation indices, denoted C, for each evaluation objectj(j-1, 2, …, g), and the corresponding weight is expressed as
The total number of the evaluation experts is q and is marked as Rk(k is 1, 2, …, q) and the corresponding weight is
The importance of the evaluation indexes is determined by means of expert collective decision, each expert evaluates the importance of each index according to 7-level linguistic variables listed in table 1, the evaluation result is converted into INV according to the corresponding relation, and the following decision matrix DM can be obtainedz：
In the formula (I), the compound is shown in the specification,
is INV.
Decision matrix DMzDz in (2)kjIndicating that the k-th expert is directed to index CjGiven evaluation value, for a certain index CjThe evaluation values of all experts may form a set Dj＝[dz1j，dz2j，…，dzkj]Given the weight corresponding to each expert
Set intelligence in interval DjPolymerization is carried out in place of the formula (1), and an importance evaluation set of each index after polymerization can be obtained
Wherein
By substituting the formula (3), entropy values E (C) corresponding to the indexes can be calculatedj)；
According to E (C)j) The weight of each index can be calculated by the following calculation formula:
in step 103, the key indicators are evaluated: acquiring a decision matrix of a review expert after aggregation, setting key indexes, calculating the certainty factor, and judging whether the key indexes are qualified according to the certainty factor threshold; the method specifically comprises the following steps:
each expert gives an evaluation value to each index of each evaluation object according to 7-level linguistic variables listed in table 1, and k decision matrices can be obtained as follows:
in the formula
Indicates the evaluation value given by the k-th expert, the elements of which
Represents the evaluation value given by the kth expert to the jth index of the ith evaluation object,
giving the weight corresponding to each expert
Will be provided with
According to the formula (1) aggregation, an aggregated decision matrix can be obtained:
in the formula
An evaluation value of a j-th index indicating an ith evaluation target;
and (4) setting the jth index as a key index, calculating the certainty factor of the jth index, and judging whether the key index is qualified or not according to the threshold value of the certainty factor.
In the embodiment of the present invention, for the calculation certainty, when a conservative decision is adopted: calculated as follows:
setting delta as a certainty threshold when
And if not, the key index is qualified, otherwise, the key index is unqualified.
In the embodiment of the invention, the certainty is calculated, and when a risk type decision is adopted, the calculation is carried out according to the following formula:
setting delta as a certainty threshold when
And if not, the key index is qualified, otherwise, the key index is unqualified.
When in conservative decision making, all uncertainty is regarded as a false membership value; when the risk type decision is made, part of the uncertainty is distributed to the true membership degree according to the proportion of the true membership degree and the false membership degree, and the other part of the uncertainty is distributed to the false membership degree, so that certain decision risk exists.
In step 104, performing weighted similarity calculation on the objects qualified in the key index evaluation, and determining the professional learning ability of the evaluation objects according to the weighted similarity sequence; the method specifically comprises the following steps:
when the evaluation index CjWhen all the indexes are high-quality indexes (benefit indexes), the optimal index set is determined according to the following formula:
according to the formula (2), the weighted similarity S (C) between each evaluation object and the optimal index set can be calculatedj,C+) (ii) a According to S (C)j,C+) And (4) sorting the sizes, namely determining the professional learning capacity of each evaluation object.
Referring to fig. 2, a structure diagram of a professional learning ability evaluation system based on an interval middle intelligence theory is provided in the present invention;
as shown in fig. 2, the system for evaluating professional learning ability based on the intelligent theory in the interval includes:
a to-be-evaluated data module 201, configured to store an evaluation object, an evaluation index, and an evaluation value thereof;
an index weight determining module 202, configured to obtain an evaluation value of the evaluation index by the review expert, and determine a weight of each index;
the key index evaluation module 203 is used for acquiring the aggregated decision matrix of the review experts, setting key indexes, calculating the certainty factor, and judging whether the key indexes are qualified or not according to the certainty factor threshold;
and the comprehensive optimization evaluation module 204 is used for performing weighted similarity calculation on the objects qualified in the key index evaluation, and determining the professional learning ability of the evaluation objects according to the weighted similarity ranking.
The invention selects an intelligent set in the interval to represent the uncertainty and the ambiguity of the professional ability evaluation index value. It can handle incomplete, uncertain and inconsistent information better than other fuzzy sets. By providing a certainty evaluation function, optimization decision is carried out through a cosine similarity measurement function on the basis of standard evaluation of key indexes of each evaluation object, and a decision idea of emphasizing on key and good selection is embodied.
Example one
In a period of transferring to a professional registration of a school organization, 6 classmates of non-computer specialties are transferred to computer specialties of our hospital for study, the limitation of the number of students and teaching resources is considered comprehensively, and at most 3 classmates are agreed to be transferred. Fully understand the basic data of 6 classmates in the early stage (e.g. learning intoAchievement, place of birth, primary specialty, etc.), 5 experts are now prepared and organized for 6 classmates (S)1～S6) Interviewing was performed to evaluate their professional learning ability. The evaluation original data of the index importance given by the expert and the professional learning ability of the student are shown in a table 2 and a table 3, wherein the corresponding evaluation grades are represented by sequence numbers in the table, the divided numbers of the '/' in the table 3 represent the evaluation values of 5 experts, and the professional basic ability is set (C)1) Is a key index.
TABLE 2 index importance evaluation data
| C1 | C2 | C3 | C4 | C5 | |
| R1 | 1 | 2 | 3 | 4 | 5 |
| R2 | 2 | 2 | 4 | 3 | 4 |
| R3 | 2 | 1 | 5 | 4 | 3 |
| R4 | 1 | 1 | 3 | 5 | 4 |
| R5 | 2 | 2 | 4 | 3 | 5 |
TABLE 3 evaluation data for students
| C1 | C2 | C3 | C4 | C5 | |
| S1 | 3/4/4/5/4 | 4/5/6/3/6 | 4/3/6/4/6 | 5/2/5/4/5 | 6/3/5/5/5 |
| S2 | 6/6/5/5/6 | 5/5/5/5/7 | 4/4/6/4/5 | 4/3/4/6/6 | 5/6/5/5/7 |
| S3 | 4/5/6/4/ | 3/4/5/5/4 | 2/3/2/3/4 | 2/2/2/5/5 | 3/3/3/4/3 |
| S4 | 3/2/2/3/5 | 2/3/3/2/3 | 2/2/3/3/2 | 3/3/2/2/4 | 2/2/2/2/2 |
| S5 | 2/2/2/2/1 | 1/1/1/2/2 | 2/2/2/1/1 | 2/2/2/1/2 | 1/2/3/2/4 |
| S6 | 4/3/6/6/3 | 5/4/4/5/6 | 3/2/3/6/5 | 6/3/6/3/5 | 5/4/5/4/4 |
The above raw data were analyzed according to the evaluation method of the present invention:
step 1: determining the weight of the evaluation index:
the data in table 2 are converted to interval wisdom numbers according to table 1. According to the differences of experts in terms of reading, title and the like, the weights corresponding to each expert are respectively set to be 0.3, 0.2, 0.20, 15 and 0.15, the evaluation values of the 5 experts are aggregated according to the formula (1), and the entropy value corresponding to each index can be calculated by substituting the formula (3): e (c) ═ 0.1949, 0.2065, 0.3630, 0.3656, 0.3793]. Substituting E (C) into the formula (4), and cutting the weight corresponding to each index into:
step 2: key index evaluation:
and (3) according to the expert weight, aggregating the data corresponding to the tables 2-6 by using an expression (1) to obtain the evaluation value of each index of each student. And calculating key index C under two decision modes according to (5) and (6)1The degree of certainty of (c).
According to a conservative decision mode, the determination values of 6-bit classmates are respectively as follows: -0.138, -0.779, -0.430, 0.356, 0.36267, -0.443, if the threshold is set at δ -0, S4And S5Qualified, and the others are not qualified.
According to a risk type decision mode, the determination values of 6 classmates are respectively as follows: 0.333, -0.296, 0.069, 0.693, 0.697, 0.056, if the threshold is δ is 0, S1、S3、S4、S5、S6Qualified, only S2And (7) failing to be qualified.
The present example is intended to adopt a risk-type decision-making manner for key index evaluation, so S1、S3、S4、S5、S6And entering a subsequent preferred procedure.
And step 3: comprehensive preference evaluation:
an optimal index set is obtained according to equation (7): c+＝{<[0.74，0.84]，[0.16，0.26]，[0.16，0.26]>，<[0.83，0.93]，[0.07，0.17]，[0.07，0.17]>，<[0.79，0.89]，[0.11，0.21]，[0.11，0.21]>，<[0.77，0.87]，[0.13，0.23]，[0.13，0.23]>，<[0.75，0.86]，[0.14，0.25]，[0.14，0.25]>}。
Using the weights determined in step 1
According to formula (2):
S(Cj,C+)＝[0.2232，0.2421，0.2598，0.2629，0.2233]，j＝1，2，3，4，5。
according to S (C)j,C+) The 5-bit classmates entering the comprehensive optimization stage are ranked from large to small and have the following ranking: s5、S4、S3、S6、S1. Thus, S is finally selected5、S4、S3Entering computer professional learning in our hospital.
Using reference [12 ]]The Euclidean distance method in (1) is used for calculating the similarity to obtain a similarity value S (C)j,C+)＝[0.4324，0.4957，0.5304，0.6098，0.3415]. The ordering result should be S5、S4、S3、S1、S6. As can be seen from the calculation results, S in the two methods5、S4、S3Are completely consistent in the sequence of S1、S6The bit order is different. The main reason for the above difference is S1And S6Are relatively close, and are prone to differences when calculated using two different methods.The Euclidean distance method mainly measures the absolute numerical difference between objects, and the cosine function principle mainly reflects the angle difference between the objects and can reflect the change of relative trend. Therefore, under the condition that the experts have different confidence in the absolute standards of quality, the cosine similarity measurement is more reasonable. As can be seen from the comparison of the index evaluation values of the two indexes, the result obtained by the algorithm is more reasonable.
The invention provides a professional learning ability evaluation method based on interval intelligent set cosine similarity measurement, which introduces an interval intelligent theory into a student professional learning ability evaluation process and can effectively improve the accuracy and effectiveness of decision making; the weight of each index is calculated by an intelligent set index entropy calculation method in the interval and based on the index entropy, so that the weight distribution is more reasonable; by two determination degree calculation methods, possibility is provided for key index evaluation; the example proves that a more reasonable result can be obtained by adopting the cosine similarity measurement function to carry out fuzzy relative evaluation.
The present invention has been described in detail, but the present invention is not limited to the above embodiments, and various changes can be made without departing from the gist of the present invention within the knowledge of those skilled in the art. Many other changes and modifications can be made without departing from the spirit and scope of the invention. It is to be understood that the invention is not to be limited to the specific embodiments, but only by the scope of the appended claims.
Claims (8)
1. The professional learning ability evaluation method based on the interval mesopic theory is characterized by comprising the following steps of:
determining an evaluation object, an evaluation index, an evaluation expert and the weight of the evaluation expert;
determining the index weight: acquiring evaluation values of evaluation indexes by evaluation experts, and determining the weight of each index;
and (3) evaluating key indexes: acquiring a decision matrix of a review expert after aggregation, setting key indexes, calculating the certainty factor, and judging whether the key indexes are qualified according to the certainty factor threshold;
comprehensive preference evaluation: and performing weighted similarity calculation on the objects qualified in the key index evaluation, and determining the professional learning ability of the evaluation objects according to the weighted similarity sequencing.
2. The method of claim 1, wherein the evaluation indexes include professional basic ability, professional development potential, professional innovation awareness, team cooperation awareness and language expression ability, and the index weights are weights of the indexes determined according to evaluation values of review experts.
3. The method according to claim 1, wherein the method comprises the steps of obtaining evaluation values of evaluation indexes by a review expert, and determining the weight of each index; the method specifically comprises the following steps:
is provided with l evaluation objects (students) and is recorded as Si(i-1, 2, …, l) and g evaluation indices, denoted C, for each evaluation objectj(j-1, 2, …, g), and the corresponding weight is expressed as
The total number of the evaluation experts is q and is marked as Rk(k is 1, 2, …, q) with a corresponding weight of
The importance of the evaluation index is determined by an expert collective decision mode; each expert evaluates the importance of each index according to the linguistic variables; according to the corresponding relation, the evaluation result is converted into INV to obtain the following decision matrix DMz：
In the formula
Is INV;
decision matrix DMzDz in (2)kjIndicating that the k-th expert is directed to index CjA given evaluation value; for a certain index CjThe evaluation values of all experts may form a set Dj＝[dz1j，dz2j，…，dzkj](ii) a Giving the weight corresponding to each expert
Set intelligence in interval DjPolymerizing to obtain an importance evaluation set of each index after polymerization
Wherein
Calculating entropy E (C) corresponding to each indexj)；
The weight of each index is calculated by the following calculation formula:
4. the method according to claim 1, wherein the aggregated decision matrix of the review experts is obtained, a key index is set and the certainty is calculated, and whether the key index is qualified or not is judged according to a certainty threshold; the method specifically comprises the following steps:
each expert gives an evaluation value to each index of each evaluation object according to the language variable, and k decision matrixes are obtained as follows:
in the formula (I), the compound is shown in the specification,
indicates the evaluation value given by the k-th expert, the elements of which
Represents the evaluation value given by the kth expert to the jth index of the ith evaluation object,
giving the weight corresponding to each expert
Will be provided with
And aggregating to obtain an aggregated decision matrix:
in the formula
An evaluation value of a j-th index indicating an ith evaluation target;
and (4) setting the jth index as a key index, calculating the certainty factor of the jth index, and judging whether the key index is qualified or not according to the threshold value of the certainty factor.
5. The method according to claim 4, wherein the degree of certainty is calculated, in particular when a conservative decision is taken, according to the following formula:
setting delta as a certainty threshold when
And if not, the key index is qualified, otherwise, the key index is unqualified.
6. The method according to claim 4, wherein the degree of certainty is calculated, in particular when a risk-based decision is taken, according to the following equation:
setting delta as a certainty threshold when
And if not, the key index is qualified, otherwise, the key index is unqualified.
7. The method according to claim 1, characterized in that weighted similarity calculation is carried out on the objects qualified in key index evaluation, and professional learning ability of the evaluation objects is determined according to weighted similarity ranking; the method specifically comprises the following steps:
when the evaluation index CjWhen all the indexes are high-priority indexes, the optimal index set is determined according to the following formula:
calculating weighted similarity S (C) between each evaluation object and the optimal index setj,C+) (ii) a According to S (C)j,C+) And (4) sorting the sizes, and determining the professional learning capacity of each evaluation object.
8. The method of claims 1-7, which provides a professional learning ability evaluation system based on an interval wisdom theory, is characterized by comprising:
the data module to be evaluated is used for storing the evaluation object, the evaluation index and the evaluation value thereof;
the index weight determining module is used for acquiring the evaluation value of the evaluation indexes by the evaluation experts and determining the weight of each index;
the key index evaluation module is used for acquiring a decision matrix of the evaluation experts after aggregation, setting key indexes, calculating the certainty factor and judging whether the key indexes are qualified or not according to the certainty factor threshold;
and the comprehensive optimization evaluation module is used for performing weighted similarity calculation on the objects qualified in key index evaluation, and determining the professional learning ability of the evaluation objects according to the weighted similarity sequencing.
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| CN113408965B (en) * | 2021-08-17 | 2021-11-05 | 浙江省标准化研究院(金砖国家标准化(浙江)研究中心、浙江省物品编码中心) | Standard comparison method and system for textile products |
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