CN110544002A - scientific research credit evaluation method and system for science and technology workers - Google Patents
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Abstract
the invention discloses a scientific research credit evaluation method and system for science and technology workers, and belongs to the technical field of credit evaluation. The method comprises the steps of screening scientific research credit evaluation indexes of scientific research workers, constructing an index evaluation system and the like, wherein the system comprises an index construction module for selecting the evaluation indexes for performing scientific research credit scoring calculation, a data acquisition module for acquiring scientific research credit data and other functional modules.
Description
Technical Field
the invention belongs to the technical field of credit evaluation, and particularly relates to a scientific research credit evaluation method and system for science and technology workers.
Background
on the background that global economy is greatly increased and the relationship between the scientific and technological fields and the economic market is more and more compact, the distrust behaviors of the scientific and technological fields of China are continuously emerged. The occurrence of scientific research distrust behaviors not only greatly reduces the quality of scientific and technological achievements, but also hinders the healthy development of the scientific and technological field to a great extent. In order to enhance the execution of scientific and technological projects in China and improve the overall economic level of China, a standardized scientific and technological credit evaluation method is established for the scientific and technological industry, scientific evaluation is conducted on the behaviors of scientific research personnel, the occurrence rate of the phenomenon of losing confidence in the scientific and technological activities in China is reduced, and therefore the quality of scientific and technological achievements in China is further improved.
At present, quantitative research on comprehensive quality is lacked in evaluation of science and technology personnel, methods such as peer evaluation and literature measurement are mainly adopted, and due to the fact that the evaluation of the science and technology personnel involves a lot of contents and is complex in process, in order to guarantee the pertinence of the evaluation, a corresponding evaluation model needs to be built for the science and technology personnel. When facing complex objects, many scholars use a special evaluation model as a tool, and the armyworm diagram (ATSI) and the weather chart diagram (BTS) are used for constructing a scientific and technical personnel pertinence evaluation model by using the armyworm diagram (ATSI) and the weather chart diagram (BTS) of the Yanghuang English, so that the expression condition of each index of the scientific and technical personnel is visually shown; liaidan and the like propose to establish a credit evaluation index system for scientific researchers based on a YAAHP method, and adopt international ten-grade evaluation grades for evaluation.
the amikaprid map (ATSI) is an ATSI map which is formed by comprehensively displaying evaluation levels of a plurality of indexes in a radar coordinate map, respectively marking a plurality of evaluation index scores on radioactive line segments of each index SI and mutually connecting the radioactive line segments. Although the amicula map (ATSI) clearly shows the level of each index of an evaluation object, the comprehensive level of the evaluation object cannot be intuitively obtained from the map.
The weather chart (BTS) is used for reducing the dimension of a plurality of evaluation indexes to form two evaluation indexes, a two-dimensional coordinate system is constructed, the scores of the two indexes are obtained through weighting calculation, and the points of the two indexes on the coordinate chart can visually reflect the comprehensive level of an evaluation object. However, there is a limitation in reducing the dimension of the evaluation index into two evaluation indexes, and the model needs to be improved or an applicable multi-dimensional index evaluation model needs to be adopted when the method is applied to credit evaluation of scientific researchers.
in addition, the method of AHP (analytic Hierarchy process) is used for obtaining the weight of the evaluation index by constructing a judgment matrix, is a multi-target evaluation decision method, is suitable for decomposing a complex problem into various composition factors, and grouping the factors according to a domination relationship to form a hierarchical structure, and determines the relative importance of the factors in the Hierarchy by a pairwise comparison method. The method has the disadvantages that the evaluation standard value is generally obtained by summarizing an expert review conference of an evaluation organization, and the evaluation standard value can reflect the development trend of the evaluated object only by selecting data of continuous years to comprehensively evaluate the data.
disclosure of Invention
The invention provides a scientific research credit evaluation method and system for technologists aiming at the technical problems.
in order to achieve the above purpose, the invention provides the following technical scheme:
the invention provides a scientific research credit evaluation method for science and technology workers, which comprises the following steps:
S01, screening scientific research credit evaluation indexes of technologists, and constructing an index evaluation system;
step S02, acquiring the index data of the evaluated object;
step S03, formatting the unformatted data to form scientific research credit data of the evaluated object;
Step S04, verifying the authenticity and consistency of the processed credit data of the evaluated object;
step S05, setting initial values of weight, basic requirement and full score requirement of each index, and setting credit cardinal number N;
step S06, calculating the credit score of the evaluated object which is manually evaluated by the expert through a segmentation interpolation method;
Step S07, carrying out credit rating on the evaluated objects according to a 10-level classification method;
step S08, judging the consistency rate of the algorithm credit rating result and the credit rating result given by the expert, and turning to step S10 if the accuracy rate is more than or equal to 95%; if the accuracy rate is less than 95%, turning to step S09;
step S09, adjusting the weight, basic requirement and full score requirement of each index, and turning to step S06; the adjustment rules are as follows:
If the grading result is lower than the credit grade given by the expert, the numerical value larger index weight in the evaluated object is adjusted to be larger, and the numerical value smaller index weight in the evaluated object is adjusted to be smaller; otherwise, the numerical value larger index weight in the evaluated object is adjusted to be smaller, and the numerical value smaller index weight in the evaluated object is adjusted to be larger;
Step S10, the rating result is output.
further, the scientific research credit evaluation indexes of the step S01 include 2 primary indexes, 5 secondary indexes and 14 tertiary indexes;
the first level index comprises a performance index and a performance index;
The performance capability index comprises 2 secondary indexes which are a seniority index and a capability index:
the seniority index comprises 2 three-level indexes which are respectively a title, a working age limit and an academic duties;
the capability index comprises 5 three-level indexes which are respectively patent application, patent authorization, thesis and monograph, and are used for bearing scientific research projects and awards;
the performance index comprises 3 secondary indexes, namely a scientific research credit index, a social credit index and a loss of credit index;
The scientific research credit index comprises 2 three-level indexes, namely project declaration material authenticity and project completion condition;
the social credit index comprises 2 three-level indexes, namely personal loan and repayment data and personal tax payment data;
the loss of trust index comprises 3 three-level indexes, namely a reported condition, an administrative punishment condition and a criminal punishment condition.
further, the data acquisition method of step S02 includes: the user autonomously reports, exchanges with other third-party platforms and obtains the information from the Internet through a crawler technology;
Wherein the other third party platforms include: a government scientific research project application platform, a scientific research management department of a unit where an evaluated object is located, a Chinese people bank credit investigation center, a small loan institution, a P2P platform and a business tax department;
the target website of the crawler technology comprises: national professional qualification certificate national networking inquiry system, academic journal network, national intellectual property office website, science and technology reward administration department and referee document network.
further, the data acquired at step S03 includes formatted data and unformatted data; the unformatted data comprises pictures and texts; for picture data, an image recognition technology is adopted to obtain related evaluation index data; and for text data, analyzing and extracting related evaluation index data by adopting named entity recognition, natural language processing and semantic recognition technologies.
further, step S04, verifying authenticity and consistency of the filled data by the accessory material for authenticity and consistency of the same source data; the confirmation rule for the authenticity and consistency of the same index data in different source data is as follows:
rule 401: if the certification document exists, taking data corresponding to the certification document as index data to be evaluated;
Rule 402: the time is inconsistent, and the data extracted from the data source at the nearest time is taken as the index data to be evaluated;
rule 403: the data which is provided with other materials and can prove the authenticity of the data is used as the index data to be evaluated without directly proving the materials;
rule 404: the data of the index to be evaluated cannot be included for the time when the certification document cannot be provided.
further, the initial values of the weight, the basic requirement and the filling score requirement of each three-level index in the step S05 are set according to the statistical data related to the "yearbook of Chinese science and technology" and the "statistical report of development survey and development research on the reform of social public welfare research institutions in the department of State administration".
Further, step S05 sets the credit base N to 50.
further, the segment interpolation calculation rule of step S06 is as follows:
rule 601: when the index value is greater than or equal to the full score requirement, the index score is full score, namely the index score is equal to the weight in the table 1;
Rule 602: when the index value is equal to the basic requirement, the index score is 60% of the weight;
rule 603: when the index value is 0, the index score is 0;
rule 604: when the index value is between 0 and the basic requirement, the index score is calculated by a linear interpolation method, and the specific calculation formula is as follows:
index score is 60% of index value/basic requirement x weight;
Rule 605: when the index value is between the basic requirement and the full score requirement, the index score is calculated according to a linear interpolation method, and the specific calculation formula is as follows:
The index score is (full-score requirement-base requirement)/(index value-base requirement) × 40% of the weight + 60% of the weight.
further, the credit score calculation process in step S06 is as follows:
step S0601: calculating the numerical value Ci of each index;
job title and working age index value C1: calculating based on the working age limit value, and adjusting based on the standard that the job name is higher than the reference standard; the 10-year equivalent of the middle-level job; if the working years are 15 years and the job title is senior, the adjustment value is 20;
academic job value C2: calculating according to the academic vocational number as a numerical value and the academic institution level of the vocational and the professional level of the vocational; the national academic institution takes the leadership role as a coefficient, takes 1 as any common role and takes 0.8 as a coefficient; the system plays a role of leadership of other academic institutions, the coefficient is 0.8, and the coefficient is 0.5 for general jobs;
patent application number value C3: based on the numerical value, the inventor ranks as coefficients to be adjusted, the coefficient of the first inventor is 1, the coefficients of the second inventor and the third inventor are 0.9, and the coefficients of the other inventors are 0.6;
patent grant number C4: based on the numerical value, the inventor ranks as coefficients to adjust, the first inventor coefficient takes 1, the second and third inventor coefficients take 0.9, and the other inventor coefficients take 0.6. The numerical calculation method is the same as C3;
paper and monograph quantitative value C5: adjusting by taking the ranking and the grade as coefficients on the basis of the total number of the statistics; taking an international paper coefficient of 1, taking a domestic core journal paper coefficient of 0.9, taking a domestic general journal paper coefficient of 0.7, and taking a monograph coefficient of 1; the first author coefficient is 1, the second author coefficient and the third author coefficient are 0.9, and other ranking coefficients are 0.7;
Number of assumed scientific research projects C6: on the basis of the total amount of the assumed project data, the project level and the role assumed are taken as coefficients to be adjusted. The coefficient of the national level project is 1, the coefficient of the provincial level project is 0.9, and the coefficients of the city level project and the following projects are 0.7; the first person in charge coefficient is 1, the second person in charge and the third person in charge coefficients are 0.9, and the other person in charge coefficients are 0.6;
scientific reward data value C7: on the basis of the prize winning quantity, adjusting by taking the grade, the grade and the ranking as coefficients; the coefficient of the part province is 1, and the coefficients of other levels are 0.8; the first-class prize coefficient is 1, the second-class prize coefficient is 0.9, and the third-class prize coefficient is 0.7; the first coefficient of the ranking is 1, the second coefficient and the third coefficient of the ranking are 0.9, and the coefficients of the other responsible persons of the ranking are 0.7;
declared material authenticity data value C8: the index value is calculated in a deduction mode, C8 is 2-which is used as the number of declaration items of which the authenticity of the project responsible person has problems, the data value takes 0 as the minimum value, and when the number of the items is more than or equal to 2, the index is 0;
Project completion data value C9: the data is in units of item completion percentage, C9 is the number of normal knot items/total number of items × 100;
Personal loan and repayment data value C10: the index value is calculated in a deduction mode, C10 is 2-bad credit times, when the bad credit times are larger than or equal to 2, the data value takes 0 as the minimum value, and the index is 0;
personal tax data value C11: the index value is calculated in a deduction mode, C11 is 2-bad credit times, when the bad credit times are larger than or equal to 2, the data value takes 0 as the minimum value, and the index is 0;
reported case value C12: taking the value as the reported times;
administrative penalty case value C13: taking the value as the number of administrative punishments;
criminal penalty case value C14: taking the value as the penalty times of criminals;
step S0602: calculating a composite credit score N
in the formula:
n: a credit base number;
wi: the weight of the ith index in the third-level indexes of scientific research credit indexes of the science and technology workers;
ci: the numerical value of the ith index in the third-level indexes of the scientific research credit index of the scientific research workers.
the invention also provides a scientific research credit evaluation system for scientific research workers, which comprises an index construction module 101, a data acquisition module 102, a data processing module 103, a data verification module 104, a credit score calculation module 105, an evaluation result display module 106, an internet 107 and an external system 108, wherein the index construction module 101 is connected with each other and used for selecting evaluation indexes for performing scientific research credit score calculation, the data acquisition module 102 is used for acquiring scientific research credit data, the data processing module 103 is used for formatting the acquired data, the data verification module 104 is used for verifying the authenticity and consistency of the acquired data, the credit score calculation module 105 is used for calculating the scientific research credit score of an evaluation object, the evaluation result display module 106 is used for displaying the evaluation result, the internet 107 is.
compared with the prior art, the invention has the beneficial effects that:
the scientific research credit evaluation method and system for the scientific research workers select design evaluation indexes from the consideration of availability and appraisability of the indexes, ensure the application range and the application effect of the method, design a comprehensive scoring method from multiple angles of performance capability, performance, positive indexes, negative indexes and the like according to the professional characteristics of the scientific research workers, improve the credibility of evaluation results, compare the credibility of credit grading with expert scoring grading, and continuously adjust the model to obtain the closest practical scientific research credit grading.
drawings
in order to more clearly illustrate the embodiments of the present application or technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a schematic flow chart of an embodiment of a scientific research credit evaluation method for technologists according to an embodiment of the present invention;
FIG. 2 is a block diagram of a scientific research credit evaluation system for scientists in accordance with an embodiment of the present invention;
FIG. 3 is a diagram illustrating an index system of a scientific research credit evaluation method for scientific researchers according to an embodiment of the present invention;
fig. 4 is a schematic deployment diagram of a scientific research credit evaluation system for technologists according to an embodiment of the present invention.
Detailed Description
the scientific research credit evaluation method for science and technology workers provided by the invention, as shown in figure 1, comprises the following steps:
s01, screening scientific research credit evaluation indexes of technologists, and constructing an index evaluation system;
step S02, acquiring the index data of the evaluated object;
step S03, formatting the unformatted data to form scientific research credit data of the evaluated object;
step S04, verifying the authenticity and consistency of the processed credit data of the evaluated object;
Step S05, setting initial values of weight, basic requirement and full score requirement of each index, and setting credit cardinal number N;
step S06, calculating the credit score of the evaluated object which is manually evaluated by the expert through a segmentation interpolation method;
step S07, carrying out credit rating on the evaluated objects according to a 10-level classification method;
Step S08, judging the consistency rate of the algorithm credit rating result and the credit rating result given by the expert, and turning to step S10 if the accuracy rate is more than or equal to 95%; if the accuracy rate is less than 95%, turning to step S09;
Step S09, adjusting the weight, basic requirement and full score requirement of each index, and turning to step S06; the adjustment rules are as follows:
if the grading result is lower than the credit grade given by the expert, the numerical value larger index weight in the evaluated object is adjusted to be larger, and the numerical value smaller index weight in the evaluated object is adjusted to be smaller; otherwise, the numerical value larger index weight in the evaluated object is adjusted to be smaller, and the numerical value smaller index weight in the evaluated object is adjusted to be larger;
step S10, the rating result is output.
The invention also provides a scientific research credit evaluation system 100 for scientific research workers, which comprises an index construction module 101 for selecting evaluation indexes for performing scientific research credit score calculation, a data acquisition module 102 for acquiring scientific research credit data, a data processing module 103 for formatting the acquired data, a data verification module 104 for verifying the authenticity and consistency of the acquired data, a credit score calculation module 105 for calculating the scientific research credit score of an evaluation object, an evaluation result display module 106 for displaying the evaluation result, an internet 107 for information interaction between the system and an external system, and an external system 108 for providing/acquiring credit data, which are connected with each other, as shown in fig. 2.
in order to make those skilled in the art better understand the technical solution of the present invention, the present invention will be further described in detail with reference to the following embodiments.
example 1
The scientific research credit evaluation method for science and technology workers provided by the invention, as shown in figure 1, comprises the following steps:
s01, screening scientific research credit evaluation indexes of technologists, and constructing an index evaluation system;
as shown in fig. 3, the scientific research credit evaluation indexes include 2 primary indexes, 5 secondary indexes, and 14 tertiary indexes;
the first level index comprises a performance index and a performance index;
the performance capability index comprises 2 secondary indexes which are a seniority index and a capability index:
the seniority index comprises 2 three-level indexes which are respectively a title, a working age limit and an academic duties;
the capability index comprises 5 three-level indexes which are respectively patent application, patent authorization, thesis and monograph, and are used for bearing scientific research projects and awards;
the performance index comprises 3 secondary indexes, namely a scientific research credit index, a social credit index and a loss of credit index;
the scientific research credit index comprises 2 three-level indexes, namely project declaration material authenticity and project completion condition;
the social credit index comprises 2 three-level indexes, namely personal loan and repayment data and personal tax payment data;
the loss of trust index comprises 3 three-level indexes, namely a reported condition, an administrative punishment condition and a criminal punishment condition.
step S02, acquiring the index data of the evaluated object;
The data acquisition mode comprises the following steps: the user autonomously reports, exchanges with other third-party platforms and obtains the information from the Internet through a crawler technology;
wherein the other third party platforms include: a government scientific research project application platform, a scientific research management department of a unit where an evaluated object is located, a Chinese people bank credit investigation center, a small loan institution, a P2P platform and a business tax department;
The target website of the crawler technology comprises: national professional qualification certificate national networking inquiry system, academic journal network, national intellectual property office website, science and technology reward administration department and referee document network.
step S03, formatting the unformatted data to form scientific research credit data of the evaluated object;
the acquired data comprises formatted data and unformatted data; the unformatted data comprises pictures and texts; for picture data, an image recognition technology is adopted to obtain related evaluation index data; and for text data, analyzing and extracting related evaluation index data by adopting named entity recognition, natural language processing and semantic recognition technologies.
step S04, verifying the authenticity and consistency of the processed credit data of the evaluated object;
the authenticity and consistency of the data filled in are proved through accessory materials according to the authenticity and consistency of the data from the same source; the confirmation rule for the authenticity and consistency of the same index data in different source data is as follows:
rule 401: if the certification document exists, taking data corresponding to the certification document as index data to be evaluated;
rule 402: the time is inconsistent, and the data extracted from the data source at the nearest time is taken as the index data to be evaluated;
rule 403: the data which is provided with other materials and can prove the authenticity of the data is used as the index data to be evaluated without directly proving the materials;
rule 404: the data of the index to be evaluated cannot be included for the time when the certification document cannot be provided.
step S05, setting initial values of weight, basic requirement and full score requirement of each index, and setting credit cardinal number N;
the initial values of the weight, the basic requirement and the full score requirement of each three-level index are set according to the relevant statistical data of Chinese scientific and technological statistics yearbook and research and development condition survey and statistics report of social public welfare class in the department of State administration, and the numerical values are shown in table 1.
TABLE 1 index weight, base requirement and score full requirement
the credit base number N is set to be 50 so as to reflect the basic credit of achievements and careless scientific research personnel, the parameter can be adjusted according to the unit property of the evaluated personnel in practical application, and different credit technologies are adopted by different types of research institutions.
step S06, calculating the credit score of the evaluated object which is manually evaluated by the expert through a segmentation interpolation method;
the segment interpolation calculation rule is as follows:
rule 601: when the index value is greater than or equal to the full score requirement, the index score is full score, namely the index score is equal to the weight in the table 1;
rule 602: when the index value is equal to the basic requirement, the index score is 60% of the weight;
rule 603: when the index value is 0, the index score is 0;
Rule 604: when the index value is between 0 and the basic requirement, the index score is calculated by a linear interpolation method, and the specific calculation formula is as follows:
Index score is 60% of index value/basic requirement x weight;
rule 605: when the index value is between the basic requirement and the full score requirement, the index score is calculated according to a linear interpolation method, and the specific calculation formula is as follows:
the index score is (full-score requirement-base requirement)/(index value-base requirement) × 40% of the weight + 60% of the weight.
the credit score calculation process is as follows:
step S0601: calculating the numerical value Ci of each index;
job title and working age index value C1: calculating based on the working age limit value, and adjusting based on the standard that the job name is higher than the reference standard; the 10-year equivalent of the middle-level job; if the working years are 15 years and the job title is senior, the adjustment value is 20;
academic job value C2: calculating according to the academic vocational number as a numerical value and the academic institution level of the vocational and the professional level of the vocational; the national academic institution takes the leadership role as a coefficient, takes 1 as any common role and takes 0.8 as a coefficient; the system plays a role of leadership of other academic institutions, the coefficient is 0.8, and the coefficient is 0.5 for general jobs; if 1 national academic institution takes the leadership role, 2 national academic institutions take any common role, and 3 other academic institutions take the leadership role, C2 is 1 × 1+2 × 0.8+3 × 0.5 is 4.1;
patent application number value C3: based on the numerical value, the inventor ranks as coefficients to be adjusted, the coefficient of the first inventor is 1, the coefficients of the second inventor and the third inventor are 0.9, and the coefficients of the other inventors are 0.6; if the total number of patent applications is 10, the number of the first invention is 1, the number of the second inventor is 2, and the number of the other inventors is 7, then C3 is 1 × 1+2 × 0.9+7 × 0.6 is 7;
patent grant number C4: based on the numerical value, the inventor ranks as coefficients to adjust, the first inventor coefficient takes 1, the second and third inventor coefficients take 0.9, and the other inventor coefficients take 0.6. The numerical calculation method is the same as C3;
Paper and monograph quantitative value C5: adjusting by taking the ranking and the grade as coefficients on the basis of the total number of the statistics; taking an international paper coefficient of 1, taking a domestic core journal paper coefficient of 0.9, taking a domestic general journal paper coefficient of 0.7, and taking a monograph coefficient of 1; the first author coefficient is 1, the second author coefficient and the third author coefficient are 0.9, and other ranking coefficients are 0.7; if the evaluated subjects published 10 international papers (first author 3, second, third author 5, other author 2), 12 domestic core journal papers (first author 5, second, third author 5, other author 2), 35 general journal papers (first author 8, second, third author 15, other author 12), and 2 books not written by the former 3 authors, C5 ═ 3 × 1 × 1+5 × 1 × 0.9+2 × 1 × 0.7+5 × 0.9 × 0.7+5 × 0.9 × 1+5 × 0.9 × 0.9+2 × 0.9 × 0.7+8 × 0.7 × 1+15 × 0.7 × 0.9+12 × 0.7 × 0.7+2 × 1 × 0.7 ═ 40.34;
number of assumed scientific research projects C6: on the basis of the total amount of the assumed project data, the project level and the role assumed are taken as coefficients to be adjusted. The coefficient of the national level project is 1, the coefficient of the provincial level project is 0.9, and the coefficients of the city level project and the following projects are 0.7; the first person in charge coefficient is 1, the second person in charge and the third person in charge coefficients are 0.9, and the other person in charge coefficients are 0.6; if the first person in charge undertakes the national project 1, the provincial project 1 and the municipal project 2; the second and third responsible persons undertake the province level item 2 and the city level item 1, and then C6 is 1 × 1 × 0.9+2 × 1 × 0.7+2 × 0.9 × 0.9+1 × 0.9 × 0.7 is 5.48;
Scientific reward data value C7: on the basis of the prize winning quantity, adjusting by taking the grade, the grade and the ranking as coefficients; the coefficient of the part province is 1, and the coefficients of other levels are 0.8; the first-class prize coefficient is 1, the second-class prize coefficient is 0.9, and the third-class prize coefficient is 0.7; the first coefficient of the ranking is 1, the second coefficient and the third coefficient of the ranking are 0.9, and the coefficients of the other responsible persons of the ranking are 0.7; if the 3 rd province-level prize first-awarded 1 item, the 2 nd province-level second-awarded 1 item, the 1 st province-level third-awarded 1 item, the 1 st city-level first-awarded 3 item and the other actual second-awarded 4 items are ranked, then C7 is 1 × 1 × 0.9+2 × 1 × 0.7+2 × 0.9 × 0.9+1 × 0.9 × 0.7 is 5.48;
declared material authenticity data value C8: the index value is calculated in a deduction mode, C8 is 2-which is used as the number of declaration items of which the authenticity of the project responsible person has problems, the data value takes 0 as the minimum value, and when the number of the items is more than or equal to 2, the index is 0;
Project completion data value C9: the data is in units of item completion percentage, C9 is the number of normal knot items/total number of items × 100;
Personal loan and repayment data value C10: the index value is calculated in a deduction mode, C10 is 2-bad credit times, when the bad credit times are larger than or equal to 2, the data value takes 0 as the minimum value, and the index is 0;
Personal tax data value C11: the index value is calculated in a deduction mode, C11 is 2-bad credit times, when the bad credit times are larger than or equal to 2, the data value takes 0 as the minimum value, and the index is 0;
Reported case value C12: taking the value as the reported times;
administrative penalty case value C13: taking the value as the number of administrative punishments;
criminal penalty case value C14: taking the value as the penalty times of criminals;
step S0602: calculating a composite credit score N
in the formula:
n: a credit base number;
wi: the weight of the ith index in the third-level indexes of scientific research credit indexes of the science and technology workers;
ci: the numerical value of the ith index in the third-level indexes of the scientific research credit index of the scientific research workers.
step S07, carrying out credit rating on the evaluated objects according to a 10-level classification method;
carrying out credit rating on the evaluated objects according to a 10-level classification method; the international 10-level identification level division is shown in table 2.
TABLE 2 credit rating label for technologists
and determining the credit rating of the evaluated object according to the comprehensive scientific research credit score of the evaluated object.
Step S08, judging the consistency rate of the algorithm credit rating result and the credit rating result given by the expert, and turning to step S10 if the accuracy rate is more than or equal to 95%; if the accuracy rate is less than 95%, turning to step S09;
Step S09, adjusting the weight, basic requirement and full score requirement of each index, and turning to step S06; the adjustment rules are as follows:
if the grading result is lower than the credit grade given by the expert, the numerical value larger index weight in the evaluated object is adjusted to be larger, and the numerical value smaller index weight in the evaluated object is adjusted to be smaller; otherwise, the numerical value larger index weight in the evaluated object is adjusted to be smaller, and the numerical value smaller index weight in the evaluated object is adjusted to be larger;
step S10, the rating result is output.
Example 2
the invention also provides a scientific research credit evaluation system 100 for scientific research workers, which comprises an index construction module 101 for selecting evaluation indexes for performing scientific research credit score calculation, a data acquisition module 102 for acquiring scientific research credit data, a data processing module 103 for formatting the acquired data, a data verification module 104 for verifying the authenticity and consistency of the acquired data, a credit score calculation module 105 for calculating the scientific research credit score of an evaluation object, an evaluation result display module 106 for displaying the evaluation result, an internet 107 for information interaction between the system and an external system, and an external system 108 for providing/acquiring credit data, which are connected with each other, as shown in fig. 2.
the index construction module 101 establishes 2 first-level indexes, 5 second-level indexes and 14 third-level indexes on the basis of the research results of the existing scientific research credit evaluation system and the easy acquireability of the indexes. As shown in fig. 3.
the primary indexes include: performance index and performance index;
the secondary indexes include: seniority index, capability index, scientific research credit index, social credit index and loss of credit index;
the three-level indexes comprise: title and year of employment, academic duties, patent applications, patent authorizations, treatises and treatises, undertaking scientific research projects, awards earned, project declaration material authenticity, project completion, personal loans and redemptions, personal tax data, reported situations, administrative penalties and criminal penalties.
The data acquisition module 102 is used for acquiring related data for carrying out scientific and technological credit evaluation on scientific and technological workers according to the credit evaluation indexes;
the data acquisition mode comprises the following steps: data actively provided by the user, data exchanged with other platforms and scientific research credit related data acquired through a crawler technology.
The title and working age data are derived from the self-filled data of the evaluated object and the related data extracted from the crawling of the latest public data from other websites;
the academic vocational data is derived from the self-filled data of the evaluated object, the national networking query system query of the national vocational qualification certificate and the related inquired data published on the Internet of the vocational academic conference and the academic journal;
The patent data is derived from the independent filling data of the evaluated object and the public data inquired from the national intellectual property office website;
the paper data is derived from the self-filling data of the evaluated object and the public data inquired from the website;
The data of the scientific research projects are born from the independent filling data of the evaluated objects, the data provided by the scientific research competent department and the data exchanged by the government scientific research project application platform;
The scientific and technological reward data are derived from the independent filling data of the evaluated object, the portal website public data of the unit and the public data of the scientific and technological reward administration department;
the truth data of the declared materials are derived from the declaration and verification conclusion data exchanged with the administrative department of the scientific and technological plan project;
the project completion data source and the result proving material provided by the evaluated object and the acceptance and bulletin data of the scientific and technological plan project administration department;
the personal loan and repayment data is derived from the self-filled data of the evaluated object, the data is inquired from a credit investigation center of the Chinese people bank, and the data is exchanged from a small loan institution and a P2P platform;
The personal tax data is derived from the self-filled data of the evaluated object and the data exchanged from the industrial and commercial tax department;
the reported data is mainly obtained through the report of a master organization.
administrative penalty data is collected primarily through government, colleges and universities, official paperwork and other web sites.
The criminal penalty data is mainly collected through websites such as government, official paperwork and the like.
The acquired scientific research credit data comprise formatted data and unformatted data such as pictures, texts and the like.
the data processing module 103 extracts corresponding index data from the obtained unformatted data such as pictures and texts by means of image recognition, text recognition and the like, and stores the index data in a formatted manner.
the data verification module 104 verifies the authenticity and consistency of the collected scientific research credit data of the evaluated object;
the data authenticity verification is that if the formatted data content contains the filling information and the provided attachment or the data extracted from the information collected from other sources can prove the content of the filled data, the data is considered to meet the authenticity requirement;
and the data consistency verification is that when the information obtained by different channels is inconsistent, the data confirmed by the information which is in the latest time and proves that the material is the most sufficient is taken as the required data.
the evaluation standard and score model setting module 105 sets the parameter values of each index, the credit cardinality and the score model for calculating the credit score;
each index residual candle value comprises index weight, basic requirement and full score requirement;
The credit base number is introduced by embodying the basic credit of achievements and careless scientific research personnel;
the evaluation index weight is obtained by a fuzzy analytic hierarchy process, and the sum of the forward index weights is 100;
when the evaluation index corresponds to the secondary index which is a lost signal index, the weight of the evaluation index takes a negative value;
setting initial values according to the basic requirements, the full score requirements and the credit base numbers by experience, verifying the indexes by the evaluated objects after being evaluated by the experts, and continuously adjusting the indexes until the credit grading consistency with the credit grade evaluated by the experts meets the requirements, and determining the indexes.
The credit scoring model is designed by adopting a piecewise linear interpolation method, and the calculation mode of the single index score is shown in the step S0601;
the credit score calculating module 107 obtains the credit score of the evaluated object by calculating the total score of each index;
The evaluation result display module 108 determines the credit rating (AAA, AA, A, BBB, BB, B, CCC, CC, C, D) by applying international general grade-10 identification and automatic accumulative floating
the internet 109 is a medium for collecting data of scientific research credit evaluation system of science and technology workers;
as shown in fig. 4, the external system 110 includes an autonomous data filing system 1001, a third party platform 1002, and a web portal 1003 that can query various related data;
The autonomous data filing system 1001 provides autonomous filling of various evaluation information of an evaluated object and provides a function of uploading a relevant certification file;
The third-party platform 1002 comprises administrative departments of all levels of science and technology planning projects, related management intermediary institutions of science and technology planning, credit investigation centers of Chinese people's banks, departments of industry and commerce, tax and the like;
the web portals 1003 where various related data can be queried include the national intellectual property office official web of the people's republic of china providing patent queries, the paper/periodical web sites such as the Hopkins/Uppu/all the parties providing paper queries, the government web sites or official document web where various administrative penalties and criminal penalty information are disclosed, and the like.
while certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that the described embodiments may be modified in various different ways without departing from the spirit and scope of the invention. Accordingly, the foregoing description is illustrative in nature and is not to be construed as limiting the scope of the invention as claimed.
Claims (10)
1. a scientific research credit evaluation method for scientific research workers is characterized by comprising the following steps:
s01, screening scientific research credit evaluation indexes of technologists, and constructing an index evaluation system;
step S02, acquiring the index data of the evaluated object;
step S03, formatting the unformatted data to form scientific research credit data of the evaluated object;
step S04, verifying the authenticity and consistency of the processed credit data of the evaluated object;
Step S05, setting initial values of weight, basic requirement and full score requirement of each index, and setting credit cardinal number N;
step S06, calculating the credit score of the evaluated object which is manually evaluated by the expert through a segmentation interpolation method;
Step S07, carrying out credit rating on the evaluated objects according to a 10-level classification method;
Step S08, judging the consistency rate of the algorithm credit rating result and the credit rating result given by the expert, and turning to step S10 if the accuracy rate is more than or equal to 95%; if the accuracy rate is less than 95%, turning to step S09;
step S09, adjusting the weight, basic requirement and full score requirement of each index, and turning to step S06; the adjustment rules are as follows:
if the grading result is lower than the credit grade given by the expert, the numerical value larger index weight in the evaluated object is adjusted to be larger, and the numerical value smaller index weight in the evaluated object is adjusted to be smaller; otherwise, the numerical value larger index weight in the evaluated object is adjusted to be smaller, and the numerical value smaller index weight in the evaluated object is adjusted to be larger;
step S10, the rating result is output.
2. the scientific research credit evaluation method of scientific researchers according to claim 1, wherein the scientific research credit evaluation index of step S01 includes 2 primary indexes, 5 secondary indexes, and 14 tertiary indexes;
the first level index comprises a performance index and a performance index;
the performance capability index comprises 2 secondary indexes which are a seniority index and a capability index:
the seniority index comprises 2 three-level indexes which are respectively a title, a working age limit and an academic duties;
the capability index comprises 5 three-level indexes which are respectively patent application, patent authorization, thesis and monograph, and are used for bearing scientific research projects and awards;
the performance index comprises 3 secondary indexes, namely a scientific research credit index, a social credit index and a loss of credit index;
the scientific research credit index comprises 2 three-level indexes, namely project declaration material authenticity and project completion condition;
The social credit index comprises 2 three-level indexes, namely personal loan and repayment data and personal tax payment data;
the loss of trust index comprises 3 three-level indexes, namely a reported condition, an administrative punishment condition and a criminal punishment condition.
3. the scientific research credit evaluation method for technologists according to claim 2, wherein the data acquisition mode of step S02 comprises: the user autonomously reports, exchanges with other third-party platforms and obtains the information from the Internet through a crawler technology;
wherein the other third party platforms include: a government scientific research project application platform, a scientific research management department of a unit where an evaluated object is located, a Chinese people bank credit investigation center, a small loan institution, a P2P platform and a business tax department;
the target website of the crawler technology comprises: national professional qualification certificate national networking inquiry system, academic journal network, national intellectual property office website, science and technology reward administration department and referee document network.
4. the scientific research credit evaluation method for technologists according to claim 3, wherein the data obtained in step S03 comprises formatted data and unformatted data; the unformatted data comprises pictures and texts; for picture data, an image recognition technology is adopted to obtain related evaluation index data; and for text data, analyzing and extracting related evaluation index data by adopting named entity recognition, natural language processing and semantic recognition technologies.
5. the scientific research credit evaluation method of scientific researchers according to claim 4, wherein, in step S04, the authenticity and consistency of the filled data are proved by the accessory materials for the authenticity and consistency of the data from the same source; the confirmation rule for the authenticity and consistency of the same index data in different source data is as follows:
Rule 401: if the certification document exists, taking data corresponding to the certification document as index data to be evaluated;
rule 402: the time is inconsistent, and the data extracted from the data source at the nearest time is taken as the index data to be evaluated;
rule 403: the data which is provided with other materials and can prove the authenticity of the data is used as the index data to be evaluated without directly proving the materials;
Rule 404: the data of the index to be evaluated cannot be included for the time when the certification document cannot be provided.
6. the scientific research credit evaluation method for scientific researchers according to claim 5, wherein the initial values of the weight, the basic requirement and the full score requirement of each three-level index in step S05 are set according to the statistical data related to the annual book of Chinese scientific and technological statistics and the statistical report of research and development of the social public welfare research institution in the department of State administration.
7. the scientific research credit evaluation method of scientific researchers according to claim 6, wherein step S05 sets the credit number N to 50.
8. the scientific research credit evaluation method for scientific researchers of claim 7, wherein the calculation rule of the segmentation interpolation method in step S06 is as follows:
rule 601: when the index value is greater than or equal to the full score requirement, the index score is full score, namely the index score is equal to the weight in the table 1;
Rule 602: when the index value is equal to the basic requirement, the index score is 60% of the weight;
rule 603: when the index value is 0, the index score is 0;
rule 604: when the index value is between 0 and the basic requirement, the index score is calculated by a linear interpolation method, and the specific calculation formula is as follows:
index score is 60% of index value/basic requirement x weight;
Rule 605: when the index value is between the basic requirement and the full score requirement, the index score is calculated according to a linear interpolation method, and the specific calculation formula is as follows:
the index score is (full-score requirement-base requirement)/(index value-base requirement) × 40% of the weight + 60% of the weight.
9. the scientific research credit evaluation method of scientific researchers according to claim 7, wherein said credit score calculation process of step S06 is as follows:
Step S0601: calculating the numerical value Ci of each index;
job title and working age index value C1: calculating based on the working age limit value, and adjusting based on the standard that the job name is higher than the reference standard; the 10-year equivalent of the middle-level job; if the working years are 15 years and the job title is senior, the adjustment value is 20;
Academic job value C2: calculating according to the academic vocational number as a numerical value and the academic institution level of the vocational and the professional level of the vocational; the national academic institution takes the leadership role as a coefficient, takes 1 as any common role and takes 0.8 as a coefficient; the system plays a role of leadership of other academic institutions, the coefficient is 0.8, and the coefficient is 0.5 for general jobs;
patent application number value C3: based on the numerical value, the inventor ranks as coefficients to be adjusted, the coefficient of the first inventor is 1, the coefficients of the second inventor and the third inventor are 0.9, and the coefficients of the other inventors are 0.6;
Patent grant number C4: based on the numerical value, the inventor ranks as coefficients to adjust, the first inventor coefficient takes 1, the second and third inventor coefficients take 0.9, and the other inventor coefficients take 0.6. The numerical calculation method is the same as C3;
Paper and monograph quantitative value C5: adjusting by taking the ranking and the grade as coefficients on the basis of the total number of the statistics; taking an international paper coefficient of 1, taking a domestic core journal paper coefficient of 0.9, taking a domestic general journal paper coefficient of 0.7, and taking a monograph coefficient of 1; the first author coefficient is 1, the second author coefficient and the third author coefficient are 0.9, and other ranking coefficients are 0.7;
Number of assumed scientific research projects C6: on the basis of the total amount of the assumed project data, the project level and the role assumed are taken as coefficients to be adjusted. The coefficient of the national level project is 1, the coefficient of the provincial level project is 0.9, and the coefficients of the city level project and the following projects are 0.7; the first person in charge coefficient is 1, the second person in charge and the third person in charge coefficients are 0.9, and the other person in charge coefficients are 0.6;
Scientific reward data value C7: on the basis of the prize winning quantity, adjusting by taking the grade, the grade and the ranking as coefficients; the coefficient of the part province is 1, and the coefficients of other levels are 0.8; the first-class prize coefficient is 1, the second-class prize coefficient is 0.9, and the third-class prize coefficient is 0.7; the first coefficient of the ranking is 1, the second coefficient and the third coefficient of the ranking are 0.9, and the coefficients of the other responsible persons of the ranking are 0.7;
Declared material authenticity data value C8: the index value is calculated in a deduction mode, C8 is 2-which is used as the number of declaration items of which the authenticity of the project responsible person has problems, the data value takes 0 as the minimum value, and when the number of the items is more than or equal to 2, the index is 0;
project completion data value C9: the data is in units of item completion percentage, C9 is the number of normal knot items/total number of items × 100;
personal loan and repayment data value C10: the index value is calculated in a deduction mode, C10 is 2-bad credit times, when the bad credit times are larger than or equal to 2, the data value takes 0 as the minimum value, and the index is 0;
personal tax data value C11: the index value is calculated in a deduction mode, C11 is 2-bad credit times, when the bad credit times are larger than or equal to 2, the data value takes 0 as the minimum value, and the index is 0;
Reported case value C12: taking the value as the reported times;
Administrative penalty case value C13: taking the value as the number of administrative punishments;
criminal penalty case value C14: taking the value as the penalty times of criminals;
step S0602: calculating a composite credit score N
in the formula:
n: a credit base number;
wi: the weight of the ith index in the third-level indexes of scientific research credit indexes of the science and technology workers;
ci: the numerical value of the ith index in the third-level indexes of the scientific research credit index of the scientific research workers.
10. A scientific research credit evaluation system for scientific research workers is characterized by comprising an index construction module, a data acquisition module, a data processing module, a data verification module, a credit score calculation module, an evaluation result display module, an internet and an external system, wherein the index construction module is connected with the index construction module and used for selecting evaluation indexes for performing scientific research credit score calculation, the data acquisition module is used for acquiring scientific research credit data, the data processing module is used for formatting the acquired data, the data verification module is used for verifying the authenticity and consistency of the acquired data, the credit score calculation module is used for calculating the scientific research credit score of an evaluation object, the evaluation result display module is used for displaying the evaluation result, the internet is used for information interaction between the system.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111126834A (en) * | 2019-12-20 | 2020-05-08 | 北京软通智慧城市科技有限公司 | Evaluation model adjusting method and device, scoring simulator and storage medium |
| CN112184001A (en) * | 2020-09-25 | 2021-01-05 | 中国科学院上海微系统与信息技术研究所 | Evaluation method and device, electronic equipment and storage medium |
| CN112288564A (en) * | 2020-09-29 | 2021-01-29 | 北京农业信息技术研究中心 | Method and system for generating credit level of agricultural social service main body |
| CN114511376A (en) * | 2022-01-11 | 2022-05-17 | 广东企数标普科技有限公司 | Credit data processing method and device based on multiple models |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111126834A (en) * | 2019-12-20 | 2020-05-08 | 北京软通智慧城市科技有限公司 | Evaluation model adjusting method and device, scoring simulator and storage medium |
| CN112184001A (en) * | 2020-09-25 | 2021-01-05 | 中国科学院上海微系统与信息技术研究所 | Evaluation method and device, electronic equipment and storage medium |
| CN112288564A (en) * | 2020-09-29 | 2021-01-29 | 北京农业信息技术研究中心 | Method and system for generating credit level of agricultural social service main body |
| CN112288564B (en) * | 2020-09-29 | 2021-09-03 | 北京农业信息技术研究中心 | Method and system for generating credit level of agricultural social service main body |
| CN114511376A (en) * | 2022-01-11 | 2022-05-17 | 广东企数标普科技有限公司 | Credit data processing method and device based on multiple models |
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Application publication date: 20191206 |