CN104834702B - For the subject selection method of science research programs - Google Patents
For the subject selection method of science research programs Download PDFInfo
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- CN104834702B CN104834702B CN201510208569.1A CN201510208569A CN104834702B CN 104834702 B CN104834702 B CN 104834702B CN 201510208569 A CN201510208569 A CN 201510208569A CN 104834702 B CN104834702 B CN 104834702B
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Abstract
The invention discloses a kind of subject selection methods for science research programs, and the researcher to save all kinds of scientific research projects to have lofty ideals for application country provides simple easily execution while subject selection method having theories integration again, optimizing.The method of the present invention specifically includes following steps：Calculate individual research direction, research contents with listed each subject in project application guide the degree of correlation；The data declared in each subject in recent years unit one belongs to employee are for statistical analysis, calculate data feature values；Relevance data and data feature values are subjected to fusion calculation, obtain the recommendation weights of each subject；Selection recommends the maximum subject of weights to be declared.The present invention provides a kind of effective subject selection method for science research programs process, can be used for that individual event is declared or Scientific research management department proposes planning to the unit one belongs to project application and suggests.
Description
Technical field
It is especially a kind of to provide subject selection for personal science research programs the present invention relates to a kind of subject selection method
Reference frame, or the method that provides the unit project application relevant plan in scientific research and suggestion for Scientific research management department, belong to calculating
Machine science and technology field.
Background technology
China has numerous studies personnel that can declare country, save ground all types scientific research project every year at present, when the project application
It first has to selection one and declares subject, the accuracy of subject selection is directly related to the success rate of project application.Scientific research personnel selects
It selects before declaring subject, usually first carefully consults the file that national correlation department is promulgated, understand Subject division and classification situation.Due to
The branches of learning and subjects divided are complicated, have prodigious intercrossing between the branches of learning and subjects again, and current international academic community encourages subject to hand over
Fork researchs and solves important scientific problems, this declares scientific research project to scientific research personnel and brings puzzlement, because before declaring, needs head
Choosing solves:A suitable subject how is selected to be declared.
Scientific research personnel's selection at present is essentially all the subjective judgement by oneself when declaring subject, lacks scientific method and refers to
It leads, this, which just frequently can lead to numerous items, declares causes to declare unsuccessful because of the selection error of subject, and declares time-consuming
Length declares inefficiency.
Invention content
To solve the above-mentioned problems, the present invention provides a kind of subject selection methods for science research programs, to have
Will is saved in application country all kinds of scientific research projects researcher provide simple easy execution and meanwhile have again theories integration, most
The subject selection method of optimization, the method for the present invention specifically comprise the following steps：
S1：Calculate the degree of association of the individual research direction with listed each related discipline in project application guide of user；
S2：It is for statistical analysis to request for data of the user unit one belongs in recent years in each correlation declares ambit,
Calculate data feature values；
S3：Fusion calculation is carried out to the association degrees of data and the data feature values of S2 steps output that are exported in S1 steps, is obtained
To the recommendation weights of each subject；
S4：According to the recommendation weights of subject, to being suitble to the subject declared to be ranked up, one is selected to carry out Shen for user
Report.
Further, in above-mentioned steps S1, the computational methods of the degree of association are：First obtain the pass in the individual research direction of user
Then keyword calculates the keyword and declares matching between the corresponding keyword of subject with projects listed in project application guide
Degree obtains the degree of association of the two with this.
Further, the source of the keyword in individual research direction is：It can be by collecting the personal middle institute that has a learned dissertation published
Row keyword obtains；Or carry out text statistical analysis acquisition, statistical analysis process by haveing a learned dissertation published to collect to individual
It can be based on text mining and theme modeling method carries out；Or it is obtained by seeking advice from expert of the art or inquiry expert knowledge library.
Further, in above-mentioned steps S2, in the calculating process of data feature values, not only to consider that user unit one belongs to exists
Success rate is declared on every subjects, also needs to consider simultaneously to declare number on each subject.
Further, the computational methods of above-mentioned data feature values are specially：Work unit where user is applied in subject p
Project sum be denoted as Np, apply for that successful project number is denoted as N in subject pp,s, this work unit is in the fields subject p
Project application success rate is Rp=Np,s/Np, then project application data feature values of this work unit in the fields subject p
Further, in above-mentioned steps S4, the recommendation weights of subject can also be integrated with individual's intention of declaring, i.e.,
The intention of declaring by individual subscriber for each subject quantifies, and obtains quantization and declares intention value, then corresponds to each subject
Quantization declare intention value and be multiplied with the recommendation weights of the subject, finally will be each using product as the selection criteria value of the subject
Subject is ranked up according to corresponding selection criteria value, and the corresponding subject of maximum standard value is selected to be declared.
Further, in above-mentioned steps S3, the fusion calculation process for being associated with degrees of data and data feature values is：For each
Subject, by the degree of association of individual research direction and the subject with based on work unit where user on the subject direction in recent years
The counted request for data characteristic value of request for data institute be multiplied, product is referred to as to the recommendation weights of the subject, the i.e. recommendation of subject p
Weights Wp=Cp × Fp, wherein Cp are association degrees of data, and Fp is data feature values.
By using above-mentioned technical proposal, artificial subjective subject selection course during the project application is converted into one
Scientific quantum chemical method process, the process synthesis consider individual research direction with correlation degree interdisciplinary, and fully
Historical data information of the work unit in each ambit application situation where being utilized.Numerous and complicated is being faced for scientific research personnel
Subject improving eyesight when provide science, simple and practicable subject selection method.
Description of the drawings
Fig. 1 is the method flow diagram of present pre-ferred embodiments.
Specific implementation mode
Invention is further described in detail with reference to the accompanying drawings and examples.
The main-process stream of the present embodiment is mainly included the following steps that referring to Fig. 1：
S1：Calculate the degree of association of the individual research direction with listed each related discipline in project application guide；
In step sl, individual research direction keyword is obtained first, then it is declared with projects to subject and its right
The matching degree between keyword is answered to obtain.Individual research direction keyword can be listed in personal have a learned dissertation published by collecting
Keyword obtains, and can also be obtained by seeking advice from expert of the art or inquiry expert knowledge library；It also can be by delivering to individual
Art collection of thesis carries out text statistical analysis acquisition, and statistical analysis process can be based on text mining and theme modeling method carries out.Institute
The obtained degree of association indicates that p=1,2 ..., P, wherein p indicate that subject index, P indicate subject sum with Cp.
S2：It is for statistical analysis to request for data of the place work unit in recent years in each correlation declares ambit,
Calculate data feature values；
Our unit is retrieved according to subject code in recent years, and how many project applied for altogether in every subjects class, it will be
The project sum applied in subject p is denoted as Np；Then it retrieves again and applies for successful project number in subject p, use Np,sCarry out table
Show；Then project application success rate of this work unit in the fields subject p is Rp=Np,s/Np.Finally calculate this work unit
Project application data feature values in the fields subject p, use FpIt indicates, calculation formula is：
S3：Fusion calculation is carried out to the relevance data and the characteristic value data of S2 steps output that are exported in S1 steps, is obtained
To the recommendation weights of each subject.
Weights will be recommended to be indicated with Wp, fusion calculation equation is Wp=Cp × Fp.
S4：Directly according to the recommendation weights calculated in S3 steps, each subject is ranked up, selects the maximum subject of weights
It is declared；
Or first quantify the personal intention of declaring for each subject, it then will be for the individual of each subject
Intention quantized value recommends weights to be multiplied with the corresponding subject, is ranked up to each subject according to product.Select product
Maximum subject is declared.
The present invention is not limited to the above embodiments, all are belonged to using the technical solution that equivalent replacement or equivalence replacement are formed
The scope of protection of present invention.
Claims (6)
1. a kind of subject selection method for science research programs, which is characterized in that include the following steps：
S1：The degree of association of subject is declared with each correlation in the individual research direction for calculating user；
S2：It is for statistical analysis to request for data of the user unit one belongs in each correlation declares ambit, it is special to calculate data
Value indicative；
S3：Fusion calculation is carried out to the association degrees of data and the data feature values of S2 steps output that are exported in S1 steps, is obtained every
The recommendation weights of a subject；
S4：According to the recommendation weights of subject, to being suitble to the subject declared to be ranked up, one is selected to declare for user；
Or integrate the recommendation weights of subject with individual's intention of declaring, to being suitble to the subject declared to be ranked up, for
Family selection one is declared；
The computational methods of the data feature values are：The project sum that work unit where user applies in subject p is denoted as
Np, apply for that successful project number is denoted as N in subject pp,s, project application success rate of this work unit in the fields subject p
For Rp=Np,s/Np, then project application data feature values of this work unit in the fields subject p
2. according to the method described in claim 1, it is characterized in that, in the step S1, the computational methods of the degree of association are：First obtain
Then the keyword for taking the individual research direction at family calculates between the corresponding keyword that the keyword declares subject with projects
Matching degree obtains the degree of association of the two with this.
3. according to the method described in claim 2, it is characterized in that, the source of the keyword in the individual research direction is：It is personal
In haveing a learned dissertation published listed keyword and/or to individual have a learned dissertation published collection progress text statistical analysis and/or
Person seeks advice from expert of the art and/or inquiry expert knowledge library.
4. method according to claim 1 or 2, characterized in that in above-mentioned steps S2, the meter of the data feature values
It calculates, it be according to user unit one belongs to declaring success rate and declare number on each subject on every subjects.
5. method according to claim 1 or 2, characterized in that in above-mentioned steps S4, by the recommendation weights of subject with
People's intention of declaring carries out the comprehensive detailed process consolidated reporting：Individual subscriber is declared into the intention amount of progress for each subject
Change, obtains quantization and declare intention value, intention value then is declared in the corresponding quantization of each subject is multiplied with the recommendation weights of the subject,
Using product as the selection criteria value of the subject, finally each subject is ranked up according to corresponding selection criteria value, is selected
The corresponding subject of maximum standard value is declared.
6. method according to claim 1 or 2, characterized in that in above-mentioned steps S3, the recommendation weights Wp=of subject p
Cp × Fp, wherein Cp are association degrees of data, and Fp is data feature values.
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| CN107506493A (en) * | 2017-09-27 | 2017-12-22 | 合肥博力生产力促进中心有限公司 | A kind of Enterprise Project declares information on services screening the matching analysis regulator control system |
| CN110349679A (en) * | 2019-05-23 | 2019-10-18 | 北京新研汇医药研发有限公司 | A kind of clinical test resource statistics method, sponsor's selection method and system |
| CN110728496A (en) * | 2019-10-14 | 2020-01-24 | 浙江农林大学暨阳学院 | Scientific research project application amount dispatching method based on linear regression |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101833552A (en) * | 2009-03-10 | 2010-09-15 | 郝瑞林 | Method for marking and recommending streaming media |
| CN103823896A (en) * | 2014-03-13 | 2014-05-28 | 蚌埠医学院 | Subject characteristic value algorithm and subject characteristic value algorithm-based project evaluation expert recommendation algorithm |
| CN104021153A (en) * | 2014-05-19 | 2014-09-03 | 江苏金智教育信息技术有限公司 | Personalized recommendation method for campus books |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US9171068B2 (en) * | 2012-03-07 | 2015-10-27 | Ut-Battelle, Llc | Recommending personally interested contents by text mining, filtering, and interfaces |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101833552A (en) * | 2009-03-10 | 2010-09-15 | 郝瑞林 | Method for marking and recommending streaming media |
| CN103823896A (en) * | 2014-03-13 | 2014-05-28 | 蚌埠医学院 | Subject characteristic value algorithm and subject characteristic value algorithm-based project evaluation expert recommendation algorithm |
| CN104021153A (en) * | 2014-05-19 | 2014-09-03 | 江苏金智教育信息技术有限公司 | Personalized recommendation method for campus books |
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