CN116307841A - Construction method of college evaluation system integrating multiple indexes - Google Patents
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Abstract
The invention discloses a construction method of an integrated multi-index college evaluation system, which comprises the following steps: step 1, obtaining public data; step 2, establishing an evaluation index system, cleaning data in the index system and performing standardization treatment; providing a multi-index evaluation method, and calculating the weight of each index in an index system by using the method; calculating a collaborative innovation center performance score through an ideal solution; step 3, combining the methods in the step 2, integrating typical multiple multi-index evaluation methods, and constructing a collaborative innovation center performance evaluation system; step 4, dynamically updating a collaborative innovation center performance evaluation system based on a preferred strategy, and dynamically recommending an evaluation method according to user requirements and attention points; and 5, constructing a college collaborative innovation performance evaluation visualization system. The invention carries out comprehensive and objective evaluation on the cooperative innovation performance data of the colleges and universities and assists relevant personnel of scientific research management departments of the colleges and universities to carry out decision analysis.
Description
Technical Field
The invention relates to the technical field of scientific research information management and evaluation, in particular to a construction method of an integrated multi-index college evaluation system.
Background
Relevant business data are generated by the obstetric and research collaborative innovation centers, and various elements of the whole development process of each collaborative innovation center are completely recorded. However, such data are not fully utilized at present, and there is an urgent need for how to analyze and evaluate the development situation of the collaborative innovation center of the university by using such data. The existing research shows that the evaluation model for the cooperative innovation of the universities is single, the subjectivity is strong, the model or the system integrating multiple evaluation methods is lacked, and the comprehensive evaluation on the performance of the cooperative innovation center of the universities is difficult to develop objectively from multiple angles.
Disclosure of Invention
The invention aims to solve the technical problem of providing a construction method of a college evaluation system integrating multiple indexes, which is used for comprehensively and objectively evaluating college collaborative innovation performance data and assisting relevant personnel of a college scientific research management department in carrying out decision analysis.
In order to solve the technical problems, the invention provides a construction method of an integrated multi-index college evaluation system, which comprises the following steps:
step 1, obtaining public data;
step 2, establishing an evaluation index system, cleaning data in the index system, and performing standardization treatment by using a min-max method to enable the result to be between 0 and 1; providing a multi-index evaluation method based on the combination of mixed cross weighting relation analysis and MEREC method, and calculating the weight of each index in an index system by the method; calculating a collaborative innovation center performance score through an ideal solution;
step 3, combining the methods in the step 2, integrating typical multiple multi-index evaluation methods, and constructing a collaborative innovation center performance evaluation system;
step 4, dynamically updating a collaborative innovation center performance evaluation system based on a preferred strategy, and dynamically recommending an evaluation method according to user requirements and attention points;
and 5, constructing a college collaborative innovation performance evaluation visualization system based on Django and MongoDB, and displaying an evaluation result.
Preferably, in step 2, the dimensionless processing is performed on the data by adopting a min-max normalization method:
wherein the method comprises the steps of
Is the data after dimensionless treatment, y ij For initial data, index i indicates the index type, j indicates the different collaborative innovation centers, min is the minimum value in the same index, and max is the maximum value in the same index.
Preferably, in step 2, a multi-index evaluation method fused with MEREC is used to calculate a performance score, wherein G1 is a subjective evaluation method, and the method comprises the following steps:
(a) Determining index X i The weight ordering among the indexes is used for constructing a priority order relation among different indexes, and the priority order relation is recorded as:
X 1 >X 2 >X 3 >…>X n (2)
(b) Determining a variation coefficient value of the evaluation index:
calculating standard deviation sigma of evaluation index k ：
Calculating the variation coefficient C of the evaluation index k ：
(c) Determining the degree of importance r between adjacent indexes in the order relation based on the ratio of the variation coefficients of the evaluation indexes obtained in (b) k ：
(d) Determining final weight among indexes according to a sequence relation analysis method, and solving the weight omega of the kth index, namely the index with highest priority in the sequence relation according to the relative importance scale obtained in the last step k The calculation formula is as follows:
and then according to the weight omega n To calculate the following weight values adjacent to the weight values as follows:
ω k-1 ＝r k ω k k＝n,…2,1 (7)
preferably, the MEREC method in step 2 is a guest evaluation method, and includes the following steps:
(a) Constructing an evaluation matrix D:
(b) Calculating performance evaluation result S of overall index system i In this step, logarithmic measures with equal weights are employed to obtain the overall performance of the selected collaborative innovation center:
(c) The performance of the alternative is calculated by deleting each index, in which step the logarithmic measure is used in a similar manner to the previous step, with individual removal of each index at the time of calculation of the index performance, the calculation formula being as follows:
(d) Calculate the sum of absolute deviations E j ：
(e) Determining final weight ω of an indicator j ：
The mixed cross weighting relation analysis method and the MEREC method can respectively obtain the weights between the two-level indexes in the first-level indexes and the weights between the indexes of different levels, and the weights are multiplied to calculate the final weights.
Preferably, in step 2, the final score is calculated by an ideal solution method, the quality of the evaluation object is judged by calculating the distance between the evaluation object and the positive ideal solution and the negative ideal solution, the closer the index value is to the positive ideal solution, the higher the score is, the grade is determined according to the score ranking, the top 0% -top 25% is the grade A, the top 26% -top 50% is the grade B, the top 51% -top 75% is the grade C, and the top 76% -top 100% is the grade D.
Preferably, in step 3, a collaborative innovation center performance evaluation system is constructed by combining the method in step 2 and integrating typical multiple multi-index evaluation methods, including an entropy weight method, a network analysis method, a data envelope analysis method, a random multi-criterion acceptability analysis method and a sequence relation analysis-entropy weight method.
Preferably, in step 5, a college collaborative innovation performance evaluation visualization system is constructed, and Django and MongoDB are adopted; the Django is used for constructing a visual system background, and the MongoDB is used for cooperating with the innovation center data storage.
Preferably, the system comprises four modules, namely an annual data visualization module, a data comparison analysis module, a comprehensive evaluation visualization module and a data early warning module; the calendar year data visualization module is used for displaying performance data; the data comparison analysis module is used for analyzing performance development of different years and different collaborative innovation centers; the comprehensive evaluation visualization module is used for realizing a comprehensive evaluation system and analyzing and displaying performance evaluation results; the data early warning module is used for carrying out abnormal early warning on the collaborative innovation center with abnormal performance data.
The beneficial effects of the invention are as follows: the invention can comprehensively evaluate the collaborative innovation capability of the colleges and universities, is beneficial to decision analysis by the scientific research management department of the colleges and universities, is beneficial to the advantages and disadvantages of the colleges and universities, and can timely adjust the working key points and the working directions in future so as to assist the collaborative innovation center of the colleges and universities to better build and develop in the future.
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FIG. 1 is a schematic flow chart of the construction method of the present invention.
Fig. 2 is a schematic diagram of a performance evaluation visualization system of a cooperative innovation center in Jiangsu universities.
Detailed Description
As shown in fig. 1, a construction method of a college evaluation system integrating multiple indexes includes the following steps:
step 1, acquiring public data from a work plan and a annual report of a innovation center cooperated with Jiangsu university 76 in 2015-2021;
step 2-1, establishing an evaluation index system, dividing the index system into two stages, taking 8 aspects of (1) capital investment and construction, (2) the number and construction of the present recruiters, (3) excellent talent introduction, (4) major rewards acquisition, (5) paper output, (6) patent output, (7) international communication and cooperation, (8) social service and contribution as first-level indexes of the evaluation index system, and selecting 22 typical second-level indexes for the 8 different first-level indexes, wherein all indexes are derived from annual report contents of a collaborative innovation center;
step 2-2, cleaning the data in the index system and performing standardized treatment by using a min-max method;
step 2-3, providing a multi-index evaluation method based on the combination of mixed cross weighting relation analysis and MEREC (Method based on the Removal Effects of Criteria), and calculating the weight of each index in an index system by the method;
step 2-4, calculating performance scores of collaborative innovation centers of all Jiangsu universities through an ideal solution (TOPSIS);
step 3, integrating other typical multi-index evaluation methods with the method in the step 2, including an entropy weight method (EW), a network analysis method (ANP), a data envelope analysis method (DEA), a random multi-criterion acceptability analysis method (SMAA) and a sequence relation analysis-entropy weight method (G1-EW method), and constructing a college collaborative innovation center performance evaluation system;
step 4, dynamically updating a collaborative innovation center performance evaluation system based on a preferred strategy, and dynamically recommending an evaluation method according to user requirements and points of interest;
step 5, constructing a college collaborative innovation performance evaluation visualization system based on Django and MongoDB, which comprises the following steps: the system comprises a calendar year data visualization module, a data comparison analysis module, a comprehensive evaluation result visualization module and a data early warning module.
The implementation case establishes a college collaborative innovation center performance evaluation index system. The system comprises 8 first-level indexes, namely, fund input and composition (A1), excellent talent introduction (A2), actual recruiter quantity and composition (A3), important rewards acquisition (A4), paper output (A5), patent output (A6), international communication and cooperation (A7) and social service and contribution (A8). 22 typical secondary indexes are selected from the 8 different primary indexes, namely, fund investment and financial special expenditure (A11) for saving finance under the primary indexes, national education and science and technology expenditure (A12), industry departments and local government investment (A13) and enterprise investment and college self-care (A14); the number of newly increased yards (A21), the number of newly increased Yangtze river scholars (A22), the number of newly increased thousands of people (A23) and the number of newly increased Jieqing (A24) under the first-level index of the introduction of excellent talents; the number of the present recruiters and the number of full-time fixed personnel (A31), the number of double recruiters (A32) and the number of visiting mobile personnel (A33) under the first-level index are formed; national grade rewards acquisition (A41) and provincial grade rewards acquisition (A42) under the primary index of the major rewards acquisition; the first-level index of paper output issues the number of international core journals (A51) and the number of domestic first-class journals (A52); patent output first-level index lower patent grant number (A61) and patent acceptance number (A62); the method comprises the steps of developing the number of major international cooperative researches (A71), the number of sponsored international academic communication conferences (A72) and the number of sponsored domestic academic communication conferences (A73) under the first-level index of international communication and cooperation; the number of intelligent base decisions (A81) and the number of popularization technological achievements (A82) are provided under the first-level indexes of social services and contribution.
Step 2-2, carrying out dimensionless processing on the data by adopting a min-max normalization method:
wherein the method comprises the steps of
Is the data after dimensionless treatment, y ij For initial data, index type is denoted by index i, different collaborative innovation centers are denoted by j, min is the minimum value within the same index, max is the maximum value in the same index, and the data of the evaluation index is between 0 and 1 after dimensionless treatment.
Step 2-3, calculating performance score by adopting a multi-index evaluation method fused with the mixed cross weighting relation analysis and MEREC (Method based on the Removal Effects of Criteria), wherein G1 is a subjective evaluation method, and determining index X in the first step i The weight ordering among the indexes is used for constructing a priority order relation among different indexes, and the priority order relation is recorded as:
X 1 >X 2 >X 3 >…>X n (2)
second, determining the variation coefficient value of the evaluation index:
calculating standard deviation sigma of evaluation index k ：
Calculating the variation coefficient C of the evaluation index k ：
A third step of determining the degree of importance r between adjacent indexes in the order relation based on the ratio of the variation coefficients of the evaluation indexes obtained in the second step k ：
And fourthly, determining the final weight among the indexes according to a sequence relation analysis method. Solving the weight omega of the kth index, i.e. the index with the highest priority in the sequence relation, according to the relative importance scale obtained in the last step k The calculation formula is as follows:
and then according to the weight omega n To calculate the following weight values adjacent to the weight values as follows:
ω k-1 ＝r k ω k k＝n,…2,1 (7)
in the step 2, the MEREC method is a guest evaluation method, and in the first step, an evaluation matrix D is constructed:
step two, calculating a performance evaluation result S of the whole index system i In this step, logarithmic measures with equal weights are employed to obtain the overall performance of the selected collaborative innovation center:
third, the performance of the alternative is calculated by deleting each index, in which step the logarithmic measure is used in a similar manner to the previous step. The difference is that each index is removed independently when calculating the index performance, and the calculation formula is as follows:
fourth step, calculate the sum E of absolute deviation j ：
Fifth step, determining final weight omega of the index j ：
The mixed cross weighting relation analysis method and the MEREC method can respectively obtain the weights between the two-level indexes in the first-level indexes and the weights between the indexes of different levels, and the weights are multiplied to calculate the final weights.
Step 2-4 the final score was calculated by the ideal solution (TOPSIS) method. The quality of the evaluation object is judged by calculating the distance between the evaluation object and the positive ideal solution and the negative ideal solution, and the score is higher as the index value is closer to the positive ideal solution. And determining the grade according to the score ranking, wherein the top 0% -25% is the grade A, the top 25% -50% is the grade B, the top 50% -75% is the grade C, and the top 75% -100% is the grade D.
And (3) integrating other typical multi-index evaluation methods in the step (3), including an entropy weight method (EW), a network analysis method (ANP), a data envelope analysis method (DEA), a random multi-criterion acceptability analysis method (SMAA) and a sequence relation analysis-entropy weight method (G1-EW), and constructing a college collaborative innovation center performance evaluation system.
And step 4, dynamically updating a collaborative innovation center performance evaluation system based on a preferred strategy, and dynamically recommending an evaluation method according to user requirements and attention points.
And 5, constructing a college collaborative innovation performance evaluation visualization system based on Django and MongoDB, wherein the basic structure of the system is shown in figure 2. Wherein Django is used to build the visualization system background, mongoDB is used to collaborate with the innovation center data store, HTML, bootstrap, and Echarts framework is used to build the Web front end. The system is divided into four modules, namely a calendar year data visualization module, a data comparison analysis module, a comprehensive evaluation visualization module and a data early warning module. The calendar year data visualization module is used for displaying performance data; the data comparison analysis module is used for analyzing performance development of different years and different collaborative innovation centers; the comprehensive evaluation visualization module is used for realizing the comprehensive evaluation system in the step 4 and analyzing and displaying performance evaluation results; the data early warning module is used for carrying out anomaly analysis on the collaborative innovation center with abnormal performance data.
The evaluation system and the application system can provide a scientific and visual evaluation result for measuring implementation results of the college collaborative innovation center, are beneficial to improving innovation capacity of the college collaborative innovation center and promote collaborative innovation practice of the colleges.
Claims (8)
1. The construction method of the college evaluation system integrating multiple indexes is characterized by comprising the following steps of:
step 1, obtaining public data;
step 2, establishing an evaluation index system, cleaning data in the index system, and performing standardization treatment by using a min-max method to enable the result to be between 0 and 1; providing a multi-index evaluation method based on the combination of mixed cross weighting relation analysis and MEREC method, and calculating the weight of each index in an index system by the method; calculating a collaborative innovation center performance score through an ideal solution;
step 3, combining the methods in the step 2, integrating typical multiple multi-index evaluation methods, and constructing a collaborative innovation center performance evaluation system;
step 4, dynamically updating a collaborative innovation center performance evaluation system based on a preferred strategy, and dynamically recommending an evaluation method according to user requirements and attention points;
and 5, constructing a college collaborative innovation performance evaluation visualization system based on Django and MongoDB, and displaying an evaluation result.
2. The method for constructing an integrated multi-index college assessment system according to claim 1, wherein in step 2, the data is subjected to dimensionless processing by using a min-max normalization method:
wherein the method comprises the steps of
Is the data after dimensionless treatment, y ij For initial data, index i indicates the index type, j indicates the different collaborative innovation centers, min is the minimum value in the same index, and max is the maximum value in the same index.
3. The method for constructing an integrated multi-index college assessment system according to claim 1, wherein in step 2, a multi-index assessment method in which mixed cross weighting relation analysis and MEREC are combined is adopted to calculate a performance score, wherein G1 is a subjective assessment method, and the method comprises the steps of:
(a) Determining index X i The weight ordering among the indexes is used for constructing a priority order relation among different indexes, and the priority order relation is recorded as:
X 1 >X 2 >X 3 >…>X n (2)
(b) Determining a variation coefficient value of the evaluation index:
calculating standard deviation of the evaluation index:
calculating the variation coefficient C of the evaluation index according to the standard deviation of the evaluation index k ：
(c) Determining the degree of importance r between adjacent indexes in the order relation based on the ratio of the variation coefficients of the evaluation indexes obtained in (b) k ：
(d) Determining final weight among indexes according to a sequence relation analysis method, and solving the weight omega of the kth index, namely the index with highest priority in the sequence relation according to the relative importance scale obtained in the last step k The calculation formula is as follows:
and then according to the weight omega n To calculate the following weight values adjacent to the weight values as follows:
ω k-1 ＝r k ω k k＝n,…2,1 (7)。
4. the method for constructing an integrated multi-index college assessment system according to claim 1, wherein the merc method in step 2 is a guest assessment method, comprising the steps of:
(a) Constructing an evaluation matrix D:
(b) Calculating performance evaluation result S of overall index system i At this pointIn one step, log-measures with equal weights are employed to obtain the overall performance of the selected collaborative innovation center:
(c) The performance of the alternative is calculated by deleting each index, in which step the logarithmic measure is used in a similar manner to the previous step, with individual removal of each index at the time of calculation of the index performance, the calculation formula being as follows:
(d) Calculate the sum of absolute deviations E j ：
(e) Determining final weight ω of an indicator j ：
The mixed cross weighting relation analysis method and the MEREC method can respectively obtain the weights between the two-level indexes in the first-level indexes and the weights between the indexes of different levels, and the weights are multiplied to calculate the final weights.
5. The method for constructing an integrated multi-index college assessment system according to claim 1, wherein in step 2, the final score is calculated by an ideal solution method, the merits of the assessment object are judged by calculating the distances between the assessment object and the positive ideal solution and the negative ideal solution, the closer the index value is to the positive ideal solution, the higher the score is, and the ranking is determined according to the score rank, the top 0% to the top 25% is the grade a, the top 26% to the top 50% is the grade B, the top 51% to the top 75% is the grade C, and the top 76% to the top 100% is the grade D.
6. The method for constructing an integrated multi-index college assessment system according to claim 1, wherein in step 3, the method in step 2 is combined and integrated with typical multi-index assessment methods, including entropy weight method, network analysis method, data envelope analysis method, random multi-criterion acceptability analysis method, order relation analysis-entropy weight method, to construct a collaborative innovation center performance assessment system.
7. The method for constructing an integrated multi-index college assessment system according to claim 1, wherein in step 5, a college collaborative innovation performance assessment visualization system is constructed, and Django and MongoDB are adopted; the Django is used for constructing a visual system background, and the MongoDB is used for cooperating with the innovation center data storage.
8. The method for constructing the integrated multi-index college assessment system according to claim 7, wherein the system comprises four modules, namely an annual data visualization module, a data comparison analysis module, a comprehensive assessment visualization module and a data early warning module; the calendar year data visualization module is used for displaying performance data; the data comparison analysis module is used for analyzing performance development of different years and different collaborative innovation centers; the comprehensive evaluation visualization module is used for realizing a comprehensive evaluation system and analyzing and displaying performance evaluation results; the data early warning module is used for carrying out abnormal early warning on the collaborative innovation center with abnormal performance data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976755A (en) * | 2023-09-20 | 2023-10-31 | 北京正开科技有限公司 | Industrial collaborative analysis evaluation system based on data processing |
| CN117391646A (en) * | 2023-12-11 | 2024-01-12 | 深圳市伊登软件有限公司 | Collaborative innovation management system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116976755A (en) * | 2023-09-20 | 2023-10-31 | 北京正开科技有限公司 | Industrial collaborative analysis evaluation system based on data processing |
| CN116976755B (en) * | 2023-09-20 | 2024-03-19 | 北京正开科技有限公司 | Industrial collaborative analysis evaluation system based on data processing |
| CN117391646A (en) * | 2023-12-11 | 2024-01-12 | 深圳市伊登软件有限公司 | Collaborative innovation management system |
| CN117391646B (en) * | 2023-12-11 | 2024-03-22 | 深圳市伊登软件有限公司 | Collaborative innovation management system |
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