CN114202226A - Scientific and technological talent growth data analysis method and system - Google Patents

Scientific and technological talent growth data analysis method and system Download PDF

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CN114202226A
CN114202226A CN202111550720.1A CN202111550720A CN114202226A CN 114202226 A CN114202226 A CN 114202226A CN 202111550720 A CN202111550720 A CN 202111550720A CN 114202226 A CN114202226 A CN 114202226A
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余有成
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Zhibo Tiangong Suzhou Artificial Intelligence Industry Research Institute Co ltd
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Abstract

The invention provides a scientific and technological talent growth data analysis method and system. The scientific and technological talent growth data analysis method comprises the following steps: s1, acquiring scientific and technological talent growth data according to years, and establishing a scientific and technological talent growth data model TG (i, j); s2, dividing the scientific and technological talent types in the scientific and technological talent growth data model TG (i, j); s3, performing weight analysis calculation on the scientific and technological talent growth data model TG (i, j) based on the scientific and technological talent type to obtain a weight factor WFi matched with the scientific and technological talent type; s4, acquiring the scientific talent growth index I, and drawing a scientific talent growth index-time relation graph to complete the analysis of scientific talent growth data. The scientific and technological talent growth data analysis method and system can efficiently and objectively classify and analyze talent database data, and analyze the incidence relation of the data based on time dimension based on data classification and index division, thereby effectively classifying and analyzing talent data.

Description

Scientific and technological talent growth data analysis method and system
Technical Field
The invention relates to the technical field of information processing, in particular to a scientific and technological talent growth data analysis method and system.
Background
With the rapid development of science and technology in China, various scientific and technological talents are emerging continuously. The talents are the talents of science and technology mainly because of their scientific and technological achievements. The scientific and technological achievement refers to a knowledge product with a certain recognized academic or economic value obtained by people through complicated intellectual labor in scientific and technological activities. The meaning of scientific and technological achievements is defined by the Chinese academy of sciences in the management method of scientific and technological research achievements of the Chinese academy of sciences as follows: for a scientific and technical research subject, a result with certain academic significance or practical significance is obtained by observing experiments, research trial production or dialectical thinking activities. Therefore, there is no uniform standard and scientific method for acquiring or evaluating scientific and technical achievements, and the evaluation of scientific and technical talents is a very controversial matter.
The conventional scientific and technological talent evaluation is usually made by several papers and papers influence factors, patents, project topics, prize winning honor and the like. However, this method can only see the value of the scientific and technological achievements of talents at a certain time point, but cannot see the growth process.
In view of the above, it is necessary to provide a method for analyzing the growth data of the scientific talents to solve the above problems.
Disclosure of Invention
The invention aims to provide a scientific and technological talent growth data analysis method and system, which objectively and effectively analyze the internal association relationship among various types of data and refine and highlight important characteristic data in the data, so that talent data are effectively classified and analyzed, and scientific and objective data reference basis is provided for improving talent culture modes and formulating scientific talent method introduction.
In order to achieve the above object, the present invention provides a scientific and technological talent growth data analysis method, comprising the following steps:
s1, establishing a scientific and technological talent growth data model TG (i, j), acquiring scientific and technological talent growth data according to years, classifying and sorting the scientific and technological talent growth data according to scientific research indexes, teaching indexes and job title honor indexes, and establishing the scientific and technological talent growth data model TG (i, j);
s2, dividing the types of the scientific talents, acquiring index indexes corresponding to the scientific research index, the teaching index and the job title honor index, and dividing the types of the scientific talents in the scientific talent growth data model TG (i, j);
s3, performing weight analysis calculation on the scientific and technological talent growth data model TG (i, j) based on the scientific and technological talent type to obtain a weight factor WFi matched with the scientific and technological talent type;
s4, acquiring a scientific and technological talent growth index I, calculating the scientific and technological talent growth index I according to the type of the scientific and technological talents, drawing a scientific and technological talent growth index-time relation graph, and completing analysis of scientific and technological talent growth data.
As a further improvement of the present invention, the step S1 specifically includes:
s11, acquiring basic information of the scientific and technological talents, and acquiring the scientific and technological talent growth data corresponding to the scientific and technological talents according to the working years of the scientific and technological talents;
s12, dividing achievement indexes of the scientific research indexes, the teaching indexes and the job title honor indexes, and classifying the scientific talent growth data according to the achievement indexes;
and S13, matching corresponding quantitative scores according to the achievement indexes, carrying out quantitative calculation on the scientific and technological talent growth data corresponding to each achievement index, and establishing the scientific and technological talent growth data model TG (i, j).
As a further improvement of the present invention, the step S2 specifically includes:
s21, acquiring index indexes based on the scientific talent growth data model TG (i, j), and calculating the index indexes according to the scientific research indexes, the teaching indexes and the achievement indexes of the job title honor indexes;
s22, fitting the index indexes corresponding to the scientific research indexes and the teaching indexes obtained through calculation by using a least square method to obtain division parameters for dividing the types of the scientific and technological talents;
and S23, comparing the division parameters to finish the division of the scientific and technological talent types of the scientific and technological talents.
As a further improvement of the present invention, the index indexes in step S21 include a scientific research index obtained corresponding to the scientific research index, a teaching index obtained corresponding to the teaching index, and a job reputation index obtained corresponding to the job reputation index.
As a further improvement of the invention, the scientific research index
Figure BDA0003417117990000031
Figure BDA0003417117990000032
Wherein alpha isk>0, k is an adjustable parameter and is the number of the achievement indexes corresponding to the scientific research indexes, k is more than or equal to 1 and less than or equal to 4, and alpha is1+...+αk＝1；
The teaching index
Figure BDA0003417117990000033
Wherein, betah>0, h is an adjustable parameter, h is more than or equal to 1 and less than or equal to 2, beta is the number of the achievement indexes corresponding to the teaching indexes1+β2＝1；
Reputation index of the job title
Figure BDA0003417117990000034
As a further improvement of the present invention, the step S22 specifically includes:
s221, fitting the growth years and the scientific research indexes by using a least square method based on the growth years of the scientific talents and the scientific research indexes to obtain a scientific research index-growth year fitting equation, Res _ index (Y)i)＝A1·Yi+B1；
S222, fitting the growth years and the teaching indexes by using a least square method based on the growth years of the scientific and technical talents and the teaching indexes to obtain a teaching index-growth year fitting equation, namely Tea _ index (Y)i)＝A2·Yi+B2；
S223, extracting the scientific research index-growth year fitting equation andslope parameter A of teaching index-growth year fitting equation1、A2Said slope parameter A1、A2I.e. the partitioning parameter.
As a further improvement of the present invention, the step S23 specifically includes: dividing the parameter A according to a preset strategy1、A2Comparing, wherein the predetermined strategy is specifically if A1-A2If the number is more than or equal to 0.2, the scientific talent belongs to a scientific talent; if A2-A1If the number is less than or equal to 0.2, the scientific talent belongs to a teaching talent; if | A1-A2|<0.2, the scientific and technological talents belong to the teaching and research talents; and finishing the division of the types of the scientific and technological talents.
As a further improvement of the present invention, the step S4 specifically includes:
s41, matching the weight factor WFi matched with the scientific and technological talent type with the index of the scientific and technological talent according to the scientific and technological talent type;
s42, acquiring scientific talent growth index I, wherein the scientific talent growth index I comprises a scientific talent index IresEducational talent index IteaAnd educational talent index Itie_HonThe scientific talent growth index I is related to the weighting factor WFi and the index;
s43, drawing a scientific talent growth index-time relation graph according to the scientific talent growth index I of different growth years, and completing analysis of the scientific talent growth data.
As a further improvement of the present invention, in said step S42,
the scientific research talent index Ires＝(Res_index+Tea_index+Tit_Hon_index)·WFires；
The teaching talent index Itea＝(Res_index+Tea_index+Tit_Hon_index)·WFitea
The teaching and research type talent index Itie_Hon＝(Res_index+Tea_index+Tit_Hon_index)·WFitie_Hon。
In order to achieve the above object, the present invention further provides a scientific and technological talent growth data analysis system, which includes an external processing device and an internal analysis device, wherein the external processing device is used for inputting scientific and technological talent information; the internal analysis device is used for executing the scientific and technological talent growth data analysis method and displaying/printing an analysis result through the external processing device.
The invention has the beneficial effects that: according to the scientific and technological talent growth data analysis method, the scientific and technological talent growth data model TG (i, j) is established, and the scientific and technological talents are classified and analyzed based on the scientific and technological talent growth data model TG (i, j), so that the growth characteristics of the scientific and technological talent data can be objectively analyzed, the incidence relation between the scientific and technological data and time data is mined and accurately embodied, and a data reference basis is provided for improvement of talent culture and the like; the scientific and technological talent growth data analysis system using the scientific and technological talent growth data analysis method can quickly and accurately complete the growth characteristic analysis of the scientific and technological talent related data.
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FIG. 1 is a flow chart of a method for analyzing data of talent growth according to the present invention.
FIG. 2 is a block diagram of a system for analyzing talent growth data according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and/or processing steps closely related to the aspects of the present invention are shown in the drawings, and other details not closely related to the present invention are omitted.
In addition, it is also to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, a scientific and technological talent growth data analysis method 100 provided by the present invention is used for analyzing the growth data of the scientific and technological talents. The scientific talent growth data analysis method 100 includes the following steps:
s1, establishing a scientific and technological talent growth data model TG (i, j), acquiring scientific and technological talent growth data according to years, classifying and sorting the scientific and technological talent growth data according to scientific research indexes, teaching indexes and job title honor indexes, and establishing the scientific and technological talent growth data model TG (i, j);
s2, dividing the types of the scientific and technological talents, acquiring index indexes corresponding to scientific research indexes, teaching indexes and job title honor indexes, and dividing the types of the scientific and technological talents in a scientific and technological talent growth data model TG (i, j);
s3, performing weight analysis calculation on the scientific and technological talent growth data model TG (i, j) based on the scientific and technological talent type to obtain a weight factor WFi matched with the scientific and technological talent type;
s4, acquiring the scientific and technological talent growth index I, calculating the scientific and technological talent growth index I according to the types of the scientific and technological talents, drawing a scientific and technological talent growth index-time relation graph, and completing analysis of scientific and technological talent growth data.
The following description section will be described in detail with respect to steps S1 to S4.
Step S1 specifically includes:
s11, acquiring basic information of the scientific and technological talents, and acquiring scientific and technological talent growth data corresponding to the scientific and technological talents according to the working years of the scientific and technological talents;
s12, dividing achievement indexes of the scientific research indexes, the teaching indexes and the job title honor indexes, and classifying the scientific talent growth data according to the achievement indexes;
and S13, matching corresponding quantitative scores according to the achievement indexes, carrying out quantitative calculation on the scientific and technological talent growth data corresponding to each achievement index, and establishing a scientific and technological talent growth data model TG (i, j).
Specifically, in step S11, the basic information of the scientific and technological talents includes information for showing the personal condition of the scientific and technological talents, such as name, age, and working age; the scientific and technological talent growth data is the division of corresponding scientific research index, teaching index and job title honor index based on the score data obtained by scientific personnel in scientific research, teaching and job title honor, and in step S11, the scientific and technological talent growth data is acquired according to the working years of the scientific and technological talents, and the score data of each year is classified according to the scientific research index, teaching index and job title honor index. It should be noted that, the present invention is exemplified by only scientific research indexes, teaching indexes and job title honor indexes, which are examples of the growth data of the scientific talents, and should not be limited thereto.
Step S12 is to further refine the achievements of the scientific research index, the teaching index, and the job title honor index, specifically, the division of the achievement index of the scientific research index includes: paper Pap, representative Mas, patent Pat, other results Oth and scientific research project Pro; the division of the achievement indexes of the teaching indexes comprises the following steps: culturing student Stu and teaching achievement Ach; the division of the achievement indexes of the job title honor indexes comprises the following steps: title Tit, honor reward Hon.
Further, step S12 includes performing data statistics on the scientific research index, the teaching index, the job title honor index and the subdivided achievement index according to the year, in a preferred embodiment of the present invention, the data statistics on the achievement index according to the year is not performed through a table, and the statistical table is shown as follows:
Figure BDA0003417117990000061
Figure BDA0003417117990000071
of course, in other embodiments of the present invention, the year statistics of the achievement index can also be counted in other forms, that is, in the present invention, the statistics of the achievement index is only exemplary and should not be limited thereto.
Step S13 is to match the achievement indicators of the scientific research indicator, the teaching indicator, and the job title honor indicator with corresponding quantitative scores sco (j), and the quantitative scores sco (j) are set in one-to-one correspondence with the achievement indicators. In this embodiment, the quantitative score sco (j) ═ of the achievement indicator (Pap)j、Masj、Patj、Othj、Proj、Stuj、Achj、Titj、Honj) Wherein j [1, M ]]And M is 8. Quantitative score Sco (j) E [0,10]The middle is divided into 10 levels.
Further, step S13 further includes performing quantitative calculation on the scientific and technological talent growth data corresponding to each achievement index based on the quantitative score sco (j), and establishing a scientific and technological talent growth data model TG (i, j):
Figure BDA0003417117990000072
preferably, the matrix model of the scientific talent growth data model TG (i, j) can be further expressed as:
TG(i,j)＝{Yi,Scoi,j},i[1,N],j[1,M]。
step S2 specifically includes:
s21, acquiring index indexes based on the scientific talent growth data model TG (i, j), and calculating the index indexes according to the scientific research indexes, the teaching indexes and the achievement indexes of the job title honor indexes;
s22, fitting the index indexes of the scientific research indexes and the teaching indexes obtained by calculation by using a least square method to obtain division parameters for dividing the types of the scientific and technological talents;
and S23, comparing the division parameters to finish the division of the scientific and technological talent types of the scientific and technological talents.
In fact, the technical talents are different in the skilled areas due to their own characteristics, for example, some technical talents are good at scientific research, some are good at teaching, and some both. Based on this, when the tracking evaluation of the scientific and technological talents is carried out, the scientific and technological talents are evaluated by adopting the unified standard, and the evaluation result is definitely not objective.
Therefore, in step S2, by obtaining the index and the algorithm strategy, the achievement indexes of the scientific research index and the teaching index of the scientific talents are added in a weighted manner, and a least square method is used for fitting, so as to finally complete the division of the scientific talents.
Specifically, in step S21, an index is obtained based on the technical talent growth data model TG (i, j), and in the present invention, the index includes a scientific research index Res _ index (i) obtained corresponding to the scientific research index, a teaching index Tea _ index (i) obtained corresponding to the teaching index, and a job title honor index Tit _ Hon _ index obtained corresponding to the job title honor index.
In a preferred embodiment of the present invention, the scientific index
Figure BDA0003417117990000081
Wherein alpha isk>0, k is an adjustable parameter and is the number of achievement indexes corresponding to scientific research indexes, k is more than or equal to 1 and less than or equal to 4, and alpha is1+...+αk＝1。
Teaching index
Figure BDA0003417117990000082
Wherein, betah>0, h is an adjustable parameter and is the number of achievement indexes corresponding to the teaching indexes, h is more than or equal to 1 and less than or equal to 2, beta1+β2＝1。
Honor index of job title
Figure BDA0003417117990000083
In fact, the scientific talents focus on the evaluation of achievement indexes such as a paper Pap, masa, a patent Pat, a scientific achievement Oth, a scientific research project Pro and the like in scientific indexes; the teaching talents focus on the evaluation of achievement indexes such as student Stu culture, achievement Ach of teaching and the like in teaching indexes; the research-and-education talents pay attention to all achievement indexes in scientific research indexes, teaching indexes and job title honor indexes so as to carry out all-around evaluation on the scientific talents.
Step S22 specifically includes:
s221, fitting the growth years and the scientific research indexes by using a least square method based on the growth years and the scientific research indexes of the scientific talents to obtain a scientific research index-growth year fitting equation Res _ index (Y)i)＝A1·Yi+B1；
S222, fitting the growth years and the teaching indexes by using a least square method based on the growth years and the teaching indexes of the scientific talents to obtain a teaching index-growth year fitting equation, namely Tea _ index (Y)i)＝A2·Yi+B2；
S223, extracting slope parameters A of the scientific research index-growth year fitting equation and the teaching index-growth year fitting equation1、A2Slope parameter A1、A2I.e. the partitioning parameter.
Step S23 specifically includes: dividing parameter A according to preset strategy1、A2Comparing, the predetermined strategy is specifically if A1-A2If the number is more than or equal to 0.2, the scientific talent belongs to a scientific talent; if A2-A1If the number is less than or equal to 0.2, the scientific talent belongs to a teaching talent; if | A1-A2|<0.2, the scientific and technological talents belong to the teaching and research talents; the division of the types of the scientific and technological talents is completed.
Step S3 specifically includes: obtaining different weight factors WF according to the classification of the scientific talents in step S2 for scientific research indexes, teaching indexes and job title honor indexes of different types of scientific talentsi，WFi(Res _ index, Tea _ index, Tit _ Hon _ index); in a preferred embodiment of the present invention, the weighting factor WF corresponding to the type of the scientific and technological talentiAs shown in the following table:
types of scientific talents Res_index Tea_index Tit_Hon_index
Talents for scientific research 0.5 0.2 0.3
Educational talent 0.2 0.5 0.3
Talent of teaching and research type 0.35 0.35 0.3
Step S4 specifically includes:
s41, matching the weight factor WFi matched with the scientific and technological talent type with the index of the scientific and technological talent according to the scientific and technological talent type;
s42, obtaining the scientific talent growth index I including scientific talent index IresEducational talent index IteaAnd educational talent index Itie_HonThe scientific talent growth index I is related to the weight factor WFi and the index;
s43, drawing a relationship graph of the scientific talent growth index-time according to the scientific talent growth index I of different growth years, and completing analysis of the scientific talent growth data.
Specifically, in step S42, a scientific talent growth index I corresponding to the scientific talent type is obtained according to the index and the weighting factor WFi. In a preferred embodiment of the present invention,scientific research talent index I obtained in step S42resScientific research talent index IresAnd educational talent index Itie_HonThe method specifically comprises the following steps:
index of talents of scientific research type Ires＝(Res_index+Tea_index+Tit_Hon_index)·WFires；
Educational talent index Itea＝(Res_index+Tea_index+Tit_Hon_index)·WFitea；
Educational and research type talent index Itie_Hon＝(Res_index+Tea_index+Tit_Hon_index)·WFitie_Hon。
Further, in step S43, the scientific talent growth index I (y) of different years is calculated according to the scientific talent growth index I in step S42, and a scientific talent growth index-time relationship graph is drawn based on the scientific talent growth index I (y), so as to complete the analysis of the scientific talent growth data and realize the continuous tracking rating of the scientific talent growth.
Referring to fig. 2, the present invention further provides a system 200 for analyzing the growth data of the scientific and technological talents, for continuously tracking and reviewing the growth of the scientific and technological talents. The scientific talent growth data analysis system 200 includes an external processing device 201 and an internal analysis device 202.
The external processing device 201 is used for inputting scientific and technological talent information; in a preferred embodiment of the present invention, the external processing device 201 includes a data exchange unit, which includes an external collecting component, such as a keyboard, a microphone, etc., for inputting basic information of the scientific and technological talent and growth data of the scientific and technological talent; the data exchange unit further comprises a data exchange component for performing data exchange processing on electronic data including the basic scientific and technological talent information, the growth data of the scientific and technological talents and the like to obtain the basic scientific and technological talent information and the growth data of the scientific and technological talents after integrated processing. Further, the external processing device 201 of the present invention further includes a display unit for displaying the basic information of the scientific and technological talent and the growth data of the scientific and technological talent.
The internal analysis device 202 is housed in the external processing device 201 and configured to execute the scientific and technological talent growth data analysis method 100, and further, the internal analysis device 202 at least includes a data processing module, which is configured to perform processing according to the scientific and technological talent growth data analysis method 100 based on the scientific and technological talent basic information and the scientific and technological talent growth data entered and collected by the external processing device 201, so as to obtain a scientific and technological talent growth data model TG (i, j), a scientific and technological talent type, and calculation and acquisition of an index; the internal analysis device 202 further includes an analysis module, which is configured to perform matching of the weight factor WFi and obtaining and calculating of the scientific and technological talent growth index I according to the obtained scientific and technological talent growth data model TG (I, j), the scientific and technological talent type and the index, to finally complete continuous tracking and rating of the scientific and technological talent growth, and to perform display/printing of an analysis result through the external processing device 201.
It should be noted that the structures of the external processing device 201 and the internal analysis device 202 in the present invention are only exemplary, in other embodiments of the present invention, the external processing device 201 and the internal analysis device 202 may further include other structures for performing entry, presentation, storage, and processing of data, and the specific arrangement form of the external processing device 201 and the internal analysis device 202 may be selected according to actual needs, which is not limited thereto.
In summary, the scientific and technological talent growth data analysis method 100 of the present invention can objectively analyze the growth characteristics of the scientific and technological talent data by establishing the scientific and technological talent growth data model TG (i, j) and classifying and analyzing the scientific and technological talents based on the scientific and technological talent growth data model TG (i, j), and mine and accurately reflect the association relationship between the scientific and technological data and the time data, so as to provide a data reference basis for improving talent culture and the like; the scientific and technological talent growth data analysis system using the scientific and technological talent growth data analysis method can quickly and accurately complete the growth characteristic analysis of the scientific and technological talent related data.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.

Claims (10)

1. A scientific and technological talent growth data analysis method is characterized by comprising the following steps:
s1, establishing a scientific and technological talent growth data model TG (i, j), acquiring scientific and technological talent growth data according to years, classifying and sorting the scientific and technological talent growth data according to scientific research indexes, teaching indexes and job title honor indexes, and establishing the scientific and technological talent growth data model TG (i, j);
s2, dividing the types of the scientific talents, acquiring index indexes corresponding to the scientific research index, the teaching index and the job title honor index, and dividing the types of the scientific talents in the scientific talent growth data model TG (i, j);
s3, performing weight analysis calculation on the scientific and technological talent growth data model TG (i, j) based on the scientific and technological talent type to obtain a weight factor WFi matched with the scientific and technological talent type;
s4, acquiring a scientific and technological talent growth index I, calculating the scientific and technological talent growth index I according to the type of the scientific and technological talents, drawing a scientific and technological talent growth index-time relation graph, and completing analysis of scientific and technological talent growth data.
2. The method for analyzing scientific and technological talent growth data according to claim 1, wherein the step S1 specifically includes:
s11, acquiring basic information of the scientific and technological talents, and acquiring the scientific and technological talent growth data corresponding to the scientific and technological talents according to the working years of the scientific and technological talents;
s12, dividing achievement indexes of the scientific research indexes, the teaching indexes and the job title honor indexes, and classifying the scientific talent growth data according to the achievement indexes;
and S13, matching corresponding quantitative scores according to the achievement indexes, carrying out quantitative calculation on the scientific and technological talent growth data corresponding to each achievement index, and establishing the scientific and technological talent growth data model TG (i, j).
3. The method for analyzing scientific and technological talent growth data according to claim 1, wherein the step S2 is specifically:
s21, acquiring index indexes based on the scientific talent growth data model TG (i, j), and calculating the index indexes according to the scientific research indexes, the teaching indexes and the achievement indexes of the job title honor indexes;
s22, fitting the index indexes which are obtained by calculation and correspond to the scientific research indexes and the teaching indexes by using a least square method to obtain division parameters for dividing the types of the scientific and technological talents;
and S23, comparing the division parameters to finish the division of the scientific and technological talent types of the scientific and technological talents.
4. The method according to claim 3, wherein the scientific talent growth data analysis method comprises: the index indexes in step S21 include a scientific research index obtained corresponding to the scientific research index, a teaching index obtained corresponding to the teaching index, and a job title honor index obtained corresponding to the job title honor index.
5. The method according to claim 4, wherein the scientific talent growth data analysis method comprises: the scientific research index
Figure FDA0003417117980000021
Wherein alpha isk>0, k is an adjustable parameter and is the number of the achievement indexes corresponding to the scientific research indexes, k is more than or equal to 1 and less than or equal to 4, and alpha is1+...+αk＝1；
The teaching index
Figure FDA0003417117980000022
Wherein, betah>0, h is an adjustable parameter, h is more than or equal to 1 and less than or equal to 2, beta is the number of the achievement indexes corresponding to the teaching indexes1+β2＝1；
Reputation index of the job title
Figure FDA0003417117980000023
6. The method according to claim 3, wherein the scientific talent growth data analysis method comprises: the step S22 specifically includes:
s221, fitting the growth years and the scientific research indexes by using a least square method based on the growth years of the scientific talents and the scientific research indexes to obtain a scientific research index-growth year fitting equation, Res _ index (Y)i)＝A1·Yi+B1；
S222, fitting the growth years and the teaching indexes by using a least square method based on the growth years of the scientific and technical talents and the teaching indexes to obtain a teaching index-growth year fitting equation, namely Tea _ index (Y)i)＝A2·Yi+B2；
S223, extracting the scientific research index-growth year fitting equation and the slope parameter A of the teaching index-growth year fitting equation1、A2Said slope parameter A1、A2I.e. the partitioning parameter.
7. The method according to claim 3, wherein the scientific talent growth data analysis method comprises: the step S23 specifically includes: dividing the parameter A according to a preset strategy1、A2Comparing, wherein the predetermined strategy is specifically if A1-A2If the number is more than or equal to 0.2, the scientific talent belongs to a scientific talent; if A2-A1If the number is less than or equal to 0.2, the scientific talent belongs to a teaching talent; if | A1-A2|<0.2, the scientific and technological talents belong to the teaching and research talents; and finishing the division of the types of the scientific and technological talents.
8. The method according to claim 1, wherein the scientific talent growth data analysis method comprises: the step S4 specifically includes:
s41, matching the weight factor WFi matched with the scientific and technological talent type with the index of the scientific and technological talent according to the scientific and technological talent type;
s42, acquiring scientific talent growth index I, wherein the scientific talent growth index I comprises a scientific talent index IresEducational talent index IteaAnd educational talent index Itie_HonThe scientific talent growth index I is related to the weighting factor WFi and the index;
s43, drawing a scientific talent growth index-time relation graph according to the scientific talent growth index I of different growth years, and completing analysis of the scientific talent growth data.
9. The method according to claim 8, wherein the scientific talent growth data analysis method comprises: in the step S42, in the above step,
the scientific research talent index Ires＝(Res_index+Tea_index+Tit_Hon_index)·WFires；
The teaching talent index Itea＝(Res_index+Tea_index+Tit_Hon_index)·WFitea
The teaching and research type talent index Itie_Hon＝(Res_index+Tea_index+Tit_Hon_index)·WFitie_Hon。
10. The utility model provides a science and technology talent growth data analysis system, includes external processing apparatus and internal analysis device, its characterized in that: the external processing device is used for inputting scientific and technological talent information; the internal analysis device is used for executing the scientific and technological talent growth data analysis method according to any one of claims 1 to 9, and the external processing device is used for displaying/printing analysis results.
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