CN115879829A - Evaluation expert screening method applied to platform innovation capability examination and verification - Google Patents
Evaluation expert screening method applied to platform innovation capability examination and verification Download PDFInfo
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Abstract
The application provides a screening method of review experts applied to platform innovation capability auditing, and relates to the technical field of data processing. The method comprises the steps of obtaining historical review data, and establishing a category review screening word bank; acquiring technical field information of all experts in an expert database, and performing first classification screening to form classification expert sub-database data; acquiring historical review data, and establishing a technical review screening word bank and a management review screening word bank; acquiring review screening data information of all experts in the classified expert sub-library data, and performing first qualification evaluation by combining a technical review screening word library and a management review screening word library to form qualification review expert sub-library data; and obtaining the evaluation matching degree data of all experts by combining the historical audit data, and carrying out matching degree analysis to form screening reference data. The method can be used for accurately and objectively screening the evaluation experts aiming at different scientific and technological innovation platform categories so as to ensure the specialty, objectivity and fairness of the expert evaluation.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a screening method of review experts applied to platform innovation capability audit.
Background
The scientific and technological innovation platform is an important content of scientific and technological infrastructure construction, has the functions of technology transfer, technology research and development, resource sharing, incubation enterprises and the like, is an important carrier for culturing and developing high and new technology industries, is an important support of a scientific and technological innovation system, and is an accelerator for scientific and technological progress, social development and economic growth. For the evaluation of the innovation capability of the science and technology innovation platform, the evaluation method not only provides the reference of innovation development for the science and technology innovation platform, but also is an important basis for evaluating the innovation capability of the science and technology innovation platform.
At present, the innovation ability of a scientific and technological innovation platform is evaluated mostly through matching of key technical field keywords of an evaluated object and the technical field of experts, the system randomly extracts and simultaneously has the condition of manually screening matching results, on one hand, screening standards are inconsistent due to the influence of human factors, subjective screening influence is doped, the objectivity of screening is reduced, on the other hand, due to the fact that effective summarization and wide comparison matching cannot be carried out on the information of the experts, matching precision is low, and scientific evaluation results are difficult to form.
Meanwhile, considering the different categories of the scientific and technological innovation platform, the review contents are greatly different, and proper experts cannot be selected according to the categories of the scientific and technological innovation platform to conduct targeted innovation capability review, so that fair and objective evaluation on the innovation capability of the scientific and technological innovation platform cannot be achieved.
Therefore, designing a screening method for review experts applied to platform innovation capability review can accurately and objectively screen review experts aiming at different science and technology innovation platform categories so as to ensure the specialty, objectivity and fairness of expert review, which is a problem to be solved urgently at present.
Disclosure of Invention
The embodiment of the application aims to provide a review expert screening method applied to platform innovation capability review, which is characterized in that a targeted word bank related to expert screening is established for different types of scientific and technological innovation platforms by referring to historical review data so as to more accurately classify experts according to categories of the scientific and technological innovation platforms, the specialty of the selected experts on the categories is ensured, and the fairness and the objectivity of review are improved. Meanwhile, a qualification screening mechanism is established based on the technology and management, and the selected experts are guaranteed to have comprehensive, accurate and fair evaluation on the evaluation of the innovation capability of the scientific and technological innovation platform related to the technology and management. In addition, through the analysis of the evaluation matching degree, the screening can be carried out according to the experience and the qualification of the experts, so that the selected evaluation experts have rich experience and high energy level, and the objective, accurate and fair evaluation result is ensured.
In a first aspect, an embodiment of the present application provides a review expert screening method applied to platform innovation capability review, including obtaining historical review data, and establishing a category review screening lexicon according to a category of a scientific and technological innovation platform; acquiring technical field information of all experts in an expert database, and performing first classification screening according to a classification review screening word bank to form classification expert sub-database data; acquiring historical review data, and establishing a technical review screening word bank and a management review screening word bank according to the category of a scientific and technological innovation platform; acquiring review screening data information of all experts in the classified expert sub-database data, and performing first qualification by combining a technical review screening word bank and a management review screening word bank to form qualification review expert sub-database data; and obtaining evaluation matching degree data of all experts in the qualification evaluation expert sub-database data by combining with historical evaluation data, and performing matching degree analysis to form screening reference data.
In the embodiment of the application, the method establishes the pertinence lexicon about expert screening aiming at the scientific and technological innovation platforms of different types by referring to the historical review data so as to more accurately classify the experts according to the categories of the scientific and technological innovation platforms, ensure the specialty of the selected experts on the categories and improve the fairness and objectivity of the review. Meanwhile, a qualification screening mechanism is established based on the technology and management, and the selected experts are guaranteed to have comprehensive, accurate and fair evaluation on the evaluation of the innovation capability of the scientific and technological innovation platform related to the technology and management. In addition, through the analysis of the evaluation matching degree, screening can be carried out according to the experience and qualification of experts, so that the selected evaluation experts have rich experience and high-capacity level, and the objectivity, accuracy and fairness of evaluation results are ensured. Certainly, the more information the acquired historical review data contains, the more objective and comprehensive matching is performed, and in the scheme, the historical review data further includes, but is not limited to, relevance of the reviewed project field of the concerned expert, credit of the expert in project review, and contribution of the submitted opinion.
As a possible implementation mode, acquiring historical review data, and establishing a category review screening word bank according to the category of a scientific and technological innovation platform comprises the following steps: acquiring historical audit data, extracting word class information of a basic technical field based on a subject engineering class, and forming a subject engineering class basic review screening word bank and a professional review screening word bank aiming at the subject engineering class basic review screening word bank; acquiring historical review data, extracting word class information of the technical basic field based on technical innovation and result conversion classes, and forming a technical innovation and result conversion class basic review screening word bank and a professional review screening word bank aiming at the technical innovation and result conversion class basic review screening word bank; obtaining historical review data, extracting word class information of the technical basic field based on the basic support and the condition guarantee class, and forming a basic support and condition guarantee class basic review screening word bank and a professional review screening word bank aiming at the basic support and condition guarantee class basic review screening word bank.
In the embodiment of the application, in order to ensure that the screened review experts have a high matching degree, the matched target data is firstly sorted and analyzed. And respectively extracting the word classes of basic large-class technical fields related under each class by referring to historical audit data for the three classes of scientific and technological innovation platforms, and establishing a technical field judgment word bank which belongs to each class. Considering that the segmentation technical field which is good at or special for the user can be found under the basic large-class technical field related to each scientific and technological innovation platform, the professional screening word bank of the corresponding segmentation technical field is established for the basic large-class technical field of each class, so that experts in the segmentation technical field can be selected and researched in a targeted manner during matching, the matching degree of screening of the experts is improved, and the accuracy and the fairness of expert review are further improved.
As a possible implementation manner, acquiring technical field information of all experts in an expert database, performing first classification screening according to a category review screening word bank, and forming classification expert sub-database data, including: acquiring technical data of all experts in an expert database, extracting subject field part of speech information and technical field part of speech information, and forming expert technical field information data; according to the information data of the technical field of experts, respectively performing word class matching of a subject engineering class basic review screening word bank, a technical innovation and achievement transformation class basic review screening word bank and a basic support and condition guarantee class basic review screening word bank, and determining the scientific and technological innovation platform class corresponding to the word bank with the highest matching degree as the scientific and technological innovation platform class to which the experts belong; according to the expert technical field information data, matching of the professional review screening word bank corresponding to the basic review screening word bank with the highest matching degree is carried out, and the subdivision professional technical field corresponding to the professional review screening word bank with the highest matching degree is determined; according to the matching result of each expert, determining the scientific and technological innovation platform category corresponding to the basic review screening word bank with the highest matching degree determined by each expert as the scientific and technological innovation platform category to which the expert belongs, and determining the sub-technical field corresponding to the professional review screening word bank with the highest matching degree as the sub-technical field of expert research to form the classified expert sub-bank data aiming at different categories of scientific and technological innovation platforms.
In the embodiment of the application, the first classification and screening mainly classifies experts according to the categories of the scientific and technological innovation platforms so as to ensure that the experts selected by the review of each category of the scientific and technological innovation platforms are experts in the category field, and improve the matching degree of screening of the review experts. Meanwhile, a basic field word bank and a professional word bank aiming at the sub-fields subdivided by the basic field are established through historical data, the research direction of each expert can be better determined, and reference can be provided for more pertinently selecting the experts during screening. It is understood that when matching a thesaurus, especially in matching of a basic review screening thesaurus, there may occur a pairing of the expert technical field information data with the same technical words of several basic review screening thesaurus, which is common place of technical words and is not inaccurate in matching. However, after entering the professional review screening word bank, the uniqueness of matching can be gradually shown, even the professional technical terms in some independent sub-disciplines are not universal, the unique professional technical field matching can be directly obtained, and the subdivision technical field related to the expert can be quickly determined. Of course, in order to ensure the matching accuracy, the professional review screening word banks corresponding to all the basic review screening word banks with a certain matching degree may be matched, so as to ensure that the finally determined subdivision technical field is not subjected to the matching deviation caused by the common technical terms used in the basic technical field.
As a possible implementation mode, the method for acquiring historical review data, establishing a technical review screening word bank and a management review screening word bank according to the category of a scientific and technological innovation platform comprises the following steps: acquiring historical audit data, and respectively forming a basic technical review screening word bank and a professional technical review screening word bank aiming at three types of scientific and technological innovation platforms, namely a subject engineering type, a technical innovation and result conversion type and a basic support and condition guarantee type; and obtaining historical auditing data, and respectively forming a descriptive management review screening word bank and a professional term management review screening word bank aiming at three types of scientific and technological innovation platforms, namely a subject engineering class, a technical innovation and result conversion class and a basic support and condition guarantee class.
In the embodiment of the application, the innovation capability review of the scientific and technological innovation platform not only relates to professional review in the technical aspect, but also relates to review in the management aspect, and belongs to comprehensive review. Therefore, the selected review experts also need to have comprehensive review capability in both technical and administrative directions. Therefore, when screening the review experts, the requirements on the required technology and management can be determined by respectively establishing the technical and management word banks for targeted matching in the two aspects, so that the experts with high matching degree can be accurately screened, and the review is more objective, fair and professional.
As a possible implementation manner, obtaining review screening data information of all experts in the classified expert sub-library data, and performing first qualification review by combining the technical review screening word library and the management review screening word library to form qualification review expert sub-library data, including: acquiring technical word information data and management word information data of all experts in the classified expert sub-database data; matching the technical word class information data with a basic technical review screening word bank and a professional technical review screening word bank respectively to obtain technical qualification review result data; respectively matching the management word class information data with a descriptive management review screening word library and a professional term management review screening word library to obtain management qualification review result data; and analyzing the technical qualification evaluation result data and the management qualification evaluation result data to form qualification evaluation expert sub-database data.
In the embodiment of the application, the screening of the review experts comprises two directions of technology and management. Different screening and matching modes are set for the two directions, the technical matching mainly comprises basic technology review screening word bank matching and professional technology review screening word bank matching, the basic technology review word bank matching can show the research breadth of the review experts in the technical field, the technical knowledge of the review experts is known, the research depth of the review experts in the technical field can be known for the professional technology review screening word bank matching, and reliable reference is provided for technical screening. For management matching, the descriptive management reviews the matching of the screening word bank and the professional term management reviews the matching of the screening word bank. For the matching of the descriptive management review screening word bank, the understanding depth of the review expert on management can be known, for the professional term management review screening word bank, the knowledge accumulation condition of the review expert on the management theory can be known, and the two can comprehensively reflect the technical level of the review expert on management.
As a possible implementation, analyzing the technical qualification result data and the management qualification result data to form qualification expert sub-library data, including: setting a basic technology matching threshold, a professional technology matching threshold and a technology matching difference threshold; comparing the matching degree of the basic technology review screening word bank with a basic technology matching threshold, comparing the matching degree of the professional technology review screening word bank with a professional technology matching threshold, comparing the matching degree difference between the basic technology review screening word bank and the professional technology review screening word bank with a technical matching difference threshold, screening out experts meeting conditions, and forming a technical qualification review expert bank; setting a descriptive management matching threshold and a professional term management matching threshold; comparing the matching degree of the screening word bank according to the descriptive management with a descriptive management matching threshold value, screening out experts meeting conditions, and forming a management qualification review expert bank; and determining experts which are overhauled on the aspects of technology and management according to the technical qualification expert library and the management qualification expert library to form qualification expert sublibrary data.
In the embodiment of the application, the screening of technical aspects and the screening of management aspects are different in the aspects of evaluation, so that the related screening standards are different. In the process of matching and screening in the technical aspect, the term word stock of the technical class is considered for matching, and it can be understood that the more frequently the basic technical review screening word stock and the professional technical review screening word stock appear in the technical information of experts, the deeper the depth and the wider the breadth of the research of the review experts are. In addition, if the matching degree of the list for the basic technology review screening word bank is high, only the research breadth of the review experts can be explained, and if the matching degree of the list for the professional technology review screening word bank is high, only the deep research depth of the review experts can be simply indicated. The lack of either may affect the objectivity of the review. Therefore, after the basic technology review screening word bank and the professional technology review screening word bank are independently matched, the difference between the basic technology review screening word bank and the professional technology review screening word bank needs to be considered, and the matching degree of the basic technology review screening word bank and the professional technology review screening word bank is balanced to improve the overall objectivity of the review. However, for the matching in management, the understanding depth of management and the knowledge plane of management theory are not particularly closely related, and the management level with higher capability can be obtained by experience or the influence of the management theory level, so that the matching in management only needs to be carried out by matching the descriptive management review screening word bank and the professional term management review word bank separately.
As a possible implementation manner, the method, in combination with historical review data, obtains the review matching degree data of all experts in the qualification review expert sub-library data, performs matching degree analysis, and forms screening reference data, including: analyzing the basic matching degree data of all experts in the qualification evaluation expert sub-database data according to historical evaluation data to form evaluation screening basic requirement data; analyzing the evaluation matching degree data of all experts in the qualification evaluation expert sub-database data according to historical evaluation data to form evaluation experience requirement data; acquiring academic contact information and relationship information of all experts in the qualification assessment expert sub-database data to form social relationship requirement data; and establishing screening reference data according to the evaluation screening basic requirement data, the evaluation experience requirement data and the social relationship requirement data.
In the embodiment of the application, the qualification evaluation mainly considers the matching degree of the evaluation experts in the evaluation capability. The accurate, objective and fair evaluation also requires that the evaluation experts have certain self-ability, so that the evaluation experience and the evaluation quality of the evaluation experts are considered to a certain extent. In the embodiment, the evaluation screening basic requirement data of the self-capability of the design, the evaluation experience requirement data related to the evaluation experience and the social relationship requirement data influencing the evaluation fairness are mainly considered.
As a possible implementation manner, according to the historical review data, the basic matching degree data of all experts in the qualification review expert sub-library data is analyzed to form review screening basic requirement data, which includes: setting job title requirements, and sequentially performing primary basic sorting on the job titles of all experts in the qualification review expert sub-database data to form primary basic sorting requirement data; setting an age requirement, and carrying out secondary basic sorting arrangement on the ages of all experts in the qualification evaluation expert subbase data according to the primary basic sorting requirement data to form secondary basic sorting requirement data; and defining the academic active period rate, and sequentially carrying out third basic sorting and sorting on the academic active period rates of all experts in the qualification review expert sub-database data according to the second basic sorting requirement data to form a review screening basic requirement.
In the embodiment of the present application, the review screening base requirement data mainly considers the title, age and academic activity of the review expert. The job title is a capability requirement of the review experts, and the review experts above the secondary job title can ensure the matching of the self capability of the review. For the age, the influence of the review workload on the review experts is mainly considered, and the condition is also the condition for ensuring the smooth progress of the review work. For academic liveness, the familiarity of the review specialist with the technical field is considered. It can be understood that the academic activity level can be determined by a certain academic time, and in the embodiment, the academic activity level is determined in the form of paper publication cycle frequency, that is, the number of published papers in the evaluation period is determined. Of course, in order to improve the overall objectivity of the evaluation, comprehensive consideration may be given to academic reports, patent applications, and results conversion.
As a possible implementation manner, according to the historical review data, the review matching degree data of all experts in the qualification review expert sub-library data is analyzed to form review experience requirement data, which includes: counting the number of times that all experts in the qualification evaluation expert subbase data participate in evaluation, and carrying out first experience sorting according to the sequence to form first experience sorting requirement data; and analyzing the credit degrees of all experts in the qualification evaluation expert sub-database data according to the first experience sorting requirement data, and performing second experience sorting according to the analysis result to form evaluation experience requirement data.
In the embodiment of the application, for the review experience requirement data, the number of times that the review experts take part in the review and the credibility of the review are mainly considered. Particularly, as for the evaluation credibility, comprehensive evaluation can be performed according to evaluation and invited conditions of each evaluation expert.
As a possible implementation manner, establishing screening reference data according to the review screening basic requirement data and the review experience requirement data includes: and performing weight distribution on each item ranked in the review screening basic requirement data and the review experience requirement data, establishing a weight analysis model, and determining the screening grade of each expert according to the weight analysis model to form a screening reference.
In the embodiment of the application, the screening scheme is considered to provide guidance for screening of review experts, so that after comprehensive screening and matching are performed, comprehensive statistics can be performed on the evaluation based on various factors as required to form a referable evaluation index. The present embodiment provides a comprehensive evaluation method, i.e., using a form of weight. In this embodiment, the matching degree is confirmed technically, and the matching factor is defined as P Technique of The matching degree is also confirmed in management, and the matching factor is defined as P Administration Meanwhile, corresponding review matching degree analysis is established, and the title factor, the age factor, the social relation factor, the historical evaluation factor and the credit factor are respectively defined as P Job scale 、P Age(s) 、P Relationships between 、P Ginseng review And P Degree of credit . Giving weight to the target according to the requirement, calculating the weight, and calculating the final result P General assembly Screening and sorting:
wherein
Respectively, the weight of each factor.
The review expert screening method applied to platform innovation capability review provided by the embodiment has the following beneficial effects:
according to the method, through referring to historical evaluation data, a pertinence word bank related to expert screening is established for different types of scientific and technological innovation platforms, so that the experts are accurately classified according to the types of the scientific and technological innovation platforms, the specialty of the selected experts on the types is guaranteed, and the fairness and the objectivity of evaluation are improved. Meanwhile, a qualification screening mechanism is established on the basis of the technology and management, and the selected experts are guaranteed to have comprehensive and accurate and fair evaluation on the evaluation of the innovative capability of the scientific and technological innovation platform relating to the technology and management. In addition, through the analysis of the evaluation matching degree, screening can be carried out according to the experience and qualification of experts, so that the selected evaluation experts have rich experience and high-capacity level, and the objectivity, accuracy and fairness of evaluation results are ensured.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a step diagram of a review expert screening method applied to platform innovation capability review according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
The scientific and technological innovation platform is an important content of scientific and technological infrastructure construction, has the functions of technology transfer, technology research and development, resource sharing, incubation enterprises and the like, is an important carrier for culturing and developing high and new technology industries, is an important support of a scientific and technological innovation system, and is an accelerator for scientific and technological progress, social development and economic growth. For the evaluation of the innovation capability of the science and technology innovation platform, the evaluation method not only provides the reference of innovation development for the science and technology innovation platform, but also is an important basis for evaluating the innovation capability of the science and technology innovation platform.
At present, the innovation ability of a scientific and technological innovation platform is mostly screened and extracted manually through subjects and technical fields, and due to manual matching, on one hand, the screening standards are inconsistent due to the influence of human factors, subjective screening influence is doped, the screening objectivity is reduced, on the other hand, the matching precision is low due to the fact that effective summarization and wide comparison matching cannot be conducted on expert information, and scientific evaluation results are difficult to form.
Meanwhile, considering the different categories of the scientific and technological innovation platform, the review contents are greatly different, and proper experts cannot be selected according to the categories of the scientific and technological innovation platform to conduct targeted innovation capability review, so that fair and objective evaluation on the innovation capability of the scientific and technological innovation platform cannot be achieved.
Referring to fig. 1, an embodiment of the present application provides a review expert screening method applied to platform innovation capability review. According to the method, through referring to historical evaluation data, a pertinence word bank related to expert screening is established for different types of scientific and technological innovation platforms, so that the experts are accurately classified according to the types of the scientific and technological innovation platforms, the specialty of the selected experts on the types is guaranteed, and the fairness and the objectivity of evaluation are improved. Meanwhile, a qualification screening mechanism is established based on the technology and management, and the selected experts are guaranteed to have comprehensive, accurate and fair evaluation on the evaluation of the innovation capability of the scientific and technological innovation platform related to the technology and management. In addition, through the analysis of the evaluation matching degree, the screening can be carried out according to the experience and the qualification of the experts, so that the selected evaluation experts have rich experience and high energy level, and the objective, accurate and fair evaluation result is ensured.
The method for screening the review experts applied to the platform innovation capability review comprises the following main steps:
s1: and acquiring historical audit data, and establishing a category review screening word bank according to the category of the scientific and technological innovation platform.
The method mainly comprises the step of establishing target parameters for classification and screening of review experts. The method mainly comprises the steps of obtaining historical review data, extracting word class information of basic technical fields based on subject engineering classes, and forming a subject engineering class basic review screening word bank and a professional review screening word bank aiming at the subject engineering class basic review screening word bank; acquiring historical review data, extracting word class information of the technical basic field based on technical innovation and result conversion classes, and forming a technical innovation and result conversion class basic review screening word bank and a professional review screening word bank aiming at the technical innovation and result conversion class basic review screening word bank; obtaining historical review data, extracting word class information of the technical basic field based on the basic support and the condition guarantee class, and forming a basic support and condition guarantee class basic review screening word bank and a professional review screening word bank aiming at the basic support and condition guarantee class basic review screening word bank.
In order to ensure that the screened review experts have a high matching degree, matched target data are firstly sorted and analyzed. And (4) respectively extracting the word class of the basic major technical field related under each class of the three classes of science and technology innovation platforms by referring to historical audit data, and establishing a technical field judgment word bank which belongs to each class. Considering that the segmentation technical field which is good at or special for the user can be found under the basic large-class technical field related to each scientific and technological innovation platform, the professional screening word bank of the corresponding segmentation technical field is established for the basic large-class technical field of each class, so that experts in the segmentation technical field can be selected and researched in a targeted manner during matching, the matching degree of screening of the experts is improved, and the accuracy and the fairness of expert review are further improved.
S2: acquiring technical field information of all experts in the expert database, and performing first classification screening according to the classification review screening word bank to form classification expert sub-database data.
The step realizes the classified screening of the evaluation experts. The method comprises the following steps of obtaining the technical field information of all experts in an expert database, carrying out first-time classification screening according to a category review screening word bank, and forming classification expert sub-database data, wherein the method comprises the following steps: acquiring technical data of all experts in an expert database, and extracting part-of-speech information in subject fields and part-of-speech information in technical fields to form expert technical field information data; according to the information data of the technical field of experts, respectively performing word class matching of a subject engineering class basic review screening word bank, a technical innovation and achievement transformation class basic review screening word bank and a basic support and condition guarantee class basic review screening word bank, and determining the scientific and technological innovation platform class corresponding to the word bank with the highest matching degree as the scientific and technological innovation platform class to which the experts belong; according to the expert technical field information data, matching of the professional review screening word bank corresponding to the basic review screening word bank with the highest matching degree is carried out, and the subdivision professional technical field corresponding to the professional review screening word bank with the highest matching degree is determined; according to the matching result of each expert, determining the scientific and technological innovation platform category corresponding to the basic review screening word bank with the highest matching degree determined by each expert as the scientific and technological innovation platform category to which the expert belongs, and determining the sub-technical field corresponding to the professional review screening word bank with the highest matching degree as the sub-technical field of expert research to form the classified expert sub-bank data aiming at different categories of scientific and technological innovation platforms.
The first classification screening is mainly to classify the experts according to the classes of the scientific and technological innovation platforms so as to ensure that the experts selected by the review of each class of the scientific and technological innovation platforms are the experts in the field of the class, and the matching degree of screening of the review experts is improved. Meanwhile, a basic field word bank and a professional word bank aiming at the sub-fields subdivided by the basic field are established through historical data, the research direction of each expert can be better determined, and reference can be provided for more pertinently selecting the experts during screening. It is understood that when matching a thesaurus, especially in matching of a basic review screening thesaurus, there may occur a pairing of the expert technical field information data with the same technical words as several basic review screening thesaurus, which is a common place for technology and is not an imprecision of matching. However, after entering the professional review screening word bank, the uniqueness of matching can be gradually shown, even the professional technical terms in some independent sub-disciplines are not universal, the unique professional technical field matching can be directly obtained, and the subdivision technical field related to the expert can be quickly determined. Of course, in order to ensure the matching accuracy, the professional review screening word banks corresponding to all the basic review screening word banks with a certain matching degree may be matched, so as to ensure that the finally determined subdivision technical field is not subjected to the matching deviation caused by the common technical terms used in the basic technical field.
S3: and acquiring historical audit data, and establishing a technical review screening word bank and a management review screening word bank according to the category of the scientific and technological innovation platform.
In order to ensure that the screened review experts have professional and objective review capability, screening based on technical and management aspects is an important screening content. The method comprises the steps of obtaining historical audit data, and forming a basic technology review screening word bank and a professional technology review screening word bank respectively according to three types of scientific and technological innovation platforms, namely a subject engineering type, a technical innovation and achievement transformation type and a basic support and condition guarantee type; obtaining historical audit data, and respectively forming a descriptive management review screening word bank and a professional term management review screening word bank aiming at three types of scientific and technological innovation platforms, namely a subject engineering type, a technical innovation and achievement transformation type and a basic support and condition guarantee type.
The innovation capability evaluation of the scientific and technological innovation platform not only relates to professional evaluation in the technical aspect, but also relates to evaluation in the management aspect, and belongs to comprehensive evaluation. Therefore, the selected review experts also need to have comprehensive review capabilities in both the technical and administrative directions. Therefore, when screening the evaluation experts, the requirements on the required technology and management can be determined by respectively establishing the technical and management word banks for targeted matching in the two aspects, so that the experts with high matching degree can be accurately screened out, and the evaluation is more objective, fair and professional.
S4: and acquiring review screening data information of all experts in the classified expert sub-library data, and performing first qualification evaluation by combining the technical review screening word library and the management review screening word library to form qualification review expert sub-library data.
Qualification is an important screening process that enables screening based on technical and administrative aspects. Acquiring review screening data information of all experts in the classified expert sub-library data, and performing first qualification review by combining the technical review screening word library and the management review screening word library to form qualification review expert sub-library data, wherein the qualification review screening data information comprises: acquiring technical word information data and management word information data of all experts in the classified expert sub-database data; matching the technical word information data with a basic technical review screening word library and a professional technical review screening word library respectively to obtain technical qualification review result data; respectively matching the management word class information data with a descriptive management review screening word bank and a professional term management review screening word bank to obtain management qualification review result data; and analyzing the technical qualification evaluation result data and the management qualification evaluation result data to form qualification evaluation expert sub-database data.
The screening of the review experts includes both technical and administrative directions. Different screening and matching modes are set for the two directions, the technical matching mainly comprises basic technology review screening word bank matching and professional technology review screening word bank matching, the basic technology review word bank matching can show the research breadth of the review experts in the technical field, the technical knowledge of the review experts is known, the research depth of the review experts in the technical field can be known for the professional technology review screening word bank matching, and reliable reference is provided for technical screening. For management matching, the descriptive management reviews the matching of the screening word bank and the professional term management reviews the matching of the screening word bank. For the matching of the descriptive management evaluation screening word bank, the understanding depth of the evaluation expert on the management can be known, for the professional term management evaluation screening word bank, the knowledge accumulation condition of the evaluation expert on the management theory can be known, and the two can comprehensively reflect the technical level of the evaluation expert on the management.
The method for analyzing the technical qualification evaluation result data and the management qualification evaluation result data to form qualification evaluation expert sub-library data comprises the following steps: setting a basic technology matching threshold, a professional technology matching threshold and a technology matching difference threshold; comparing the matching degree of the basic technology review screening word bank with a basic technology matching threshold, comparing the matching degree of the professional technology review screening word bank with a professional technology matching threshold, comparing the matching degree difference between the basic technology review screening word bank and the professional technology review screening word bank with a technical matching difference threshold, screening out experts meeting conditions, and forming a technical qualification review expert bank; setting a descriptive management matching threshold and a professional term management matching threshold; comparing the matching degree of the screening word bank according to the descriptive management with a descriptive management matching threshold value, screening out experts meeting conditions, and forming a management qualification screening expert bank; and determining experts which are overhauled on the aspects of technology and management according to the technical qualification expert library and the management qualification expert library to form qualification expert sublibrary data.
The screening criteria involved differ from the review aspects, both technical and administrative. In the technical matching screening, the term lexicon of the technical class is considered for matching, and it can be understood that the more frequently the term lexicon appears in the technical information of the expert, the deeper and wider the depth and breadth of the research of the expert are. In addition, if the matching degree of the list for the basic technology review screening word bank is high, only the research breadth of the review experts can be explained, and if the matching degree of the list for the professional technology review screening word bank is high, only the deep research depth of the review experts can be simply indicated. The lack of either may affect the objectivity of the review. Therefore, after the basic technology review screening word bank and the professional technology review screening word bank are independently matched, the difference between the basic technology review screening word bank and the professional technology review screening word bank needs to be considered, and the matching degree of the basic technology review screening word bank and the professional technology review screening word bank is balanced to improve the overall objectivity of the review. However, for the matching in management, the understanding depth of management and the knowledge plane of management theory are not particularly closely related, and the management level with higher capability can be obtained by experience or the influence of the management theory level, so that the matching in management only needs to be performed by matching the descriptive management review screening word bank and the professional term management review word bank separately.
S5: and obtaining evaluation matching degree data of all experts in the qualification evaluation expert sub-database data by combining historical evaluation data, and performing matching degree analysis to form screening reference data.
The method comprises the following steps: analyzing the basic matching degree data of all experts in the qualification evaluation expert sub-database data according to historical evaluation data to form evaluation screening basic requirement data; analyzing the evaluation matching degree data of all experts in the qualification evaluation expert sub-database data according to historical evaluation data to form evaluation experience requirement data; acquiring academic contact information and relationship information of relatives and friends of all experts in the qualification expert sub-database data to form social relationship requirement data; and establishing screening reference data according to the evaluation screening basic requirement data, the evaluation experience requirement data and the social relationship requirement data.
The qualification evaluation mainly considers the matching degree of evaluation experts in evaluation capability. The accurate, objective and fair evaluation also requires that the evaluation experts have certain self-ability, so that the evaluation experience and the evaluation quality of the evaluation experts are considered to a certain extent. In the embodiment, basic requirement data for review and screening of the self-capability of the design, requirement data of review experience related to review experience and requirement data of social relationship affecting the fairness of the review are mainly considered.
The method comprises the following steps of analyzing basic matching degree data of all experts in the qualification review expert sub-library data according to historical review data to form review screening basic requirement data, wherein the basic matching degree data comprises the following steps: setting job title requirements, and sequentially performing primary basic sorting on the job titles of all experts in the qualification review expert sub-database data to form primary basic sorting requirement data; setting an age requirement, and carrying out secondary basic sorting arrangement on the ages of all experts in the qualification evaluation expert subbase data according to the primary basic sorting requirement data to form secondary basic sorting requirement data; and defining academic active cycle rates, and sequentially carrying out third basic sorting and arrangement on the academic active cycle rates of all experts in the qualification review expert sub-database data according to the second basic sorting requirement data to form a review screening basic requirement.
The review screening base requires data mainly considering the title, age and academic activeness of the review expert. The job title is a capability requirement of the review experts, and the review experts above the secondary job title can ensure the matching of the self capability of the review. For the age, the influence of the review workload on the review experts is mainly considered, and the condition is also the condition for ensuring the smooth progress of the review work. For academic liveness, the familiarity of the review expert with the technical field is considered. It can be understood that the academic activity level can be determined by a certain academic time, and in the embodiment, the academic activity level is determined in the form of paper publication cycle frequency, that is, the number of published papers in the evaluation period is determined. Of course, in order to improve the overall objectivity of the evaluation, comprehensive consideration may be given to academic reports, patent applications, and results conversion.
Analyzing the evaluation matching degree data of all experts in the qualification evaluation expert sub-database data according to the historical evaluation data to form evaluation experience requirement data, wherein the evaluation experience requirement data comprises the following steps: counting the number of times that all experts in the qualification evaluation expert subbase data participate in evaluation, and carrying out first experience sorting according to the sequence to form first experience sorting requirement data; and analyzing the credit degrees of all experts in the qualification evaluation expert sub-database data according to the first experience ordering requirement data, and performing second experience ordering according to the analysis result to form evaluation experience requirement data.
For the evaluation experience requirement data, the evaluation times of evaluation experts and the credit of evaluation are mainly considered. Particularly, as for the evaluation credibility, comprehensive evaluation can be performed according to evaluation and invited conditions of each evaluation expert.
In addition, according to the review screening basic requirement data and the review experience requirement data, screening reference data are established, and the screening reference data comprise: and performing weight distribution on each item ranked in the review screening basic requirement data and the review experience requirement data, establishing a weight analysis model, and determining the screening grade of each expert according to the weight analysis model to form a screening reference.
The screening scheme is considered to provide guidance for screening of review experts, so that comprehensive statistics can be carried out on the evaluation based on various factors as required after comprehensive screening matching is carried out, and evaluation indexes for reference are formed. The present embodiment provides a comprehensive evaluation method, i.e., using a form of weight. In this embodiment, the matching degree is confirmed and defined in the technical aspectThe matching factor is P Technique of The matching degree is also confirmed in management, and the matching factor is defined as P Administration Meanwhile, corresponding review matching degree analysis is established, and the title factor, the age factor, the social relation factor, the historical evaluation factor and the credit factor are respectively defined as P Job scale 、P Age (age) 、P Relationships between 、P Ginseng review And P Degree of credit . Giving weight to the target according to the requirement, calculating the weight, and calculating the final result P General assembly Screening and sorting:
wherein
Respectively, the weight of each factor.
To sum up, the review expert screening method applied to the platform innovation capability review provided by the embodiment of the application has the following beneficial effects:
according to the method, through referring to historical evaluation data, a pertinence word bank related to expert screening is established for different types of scientific and technological innovation platforms, so that the experts are accurately classified according to the types of the scientific and technological innovation platforms, the specialty of the selected experts on the types is guaranteed, and the fairness and the objectivity of evaluation are improved. Meanwhile, a qualification screening mechanism is established based on the technology and management, and the selected experts are guaranteed to have comprehensive, accurate and fair evaluation on the evaluation of the innovation capability of the scientific and technological innovation platform related to the technology and management. In addition, through the analysis of the evaluation matching degree, screening can be carried out according to the experience and qualification of experts, so that the selected evaluation experts have rich experience and high-capacity level, and the objectivity, accuracy and fairness of evaluation results are ensured.
In the present application, "at least one" means one or more, "a plurality" means two or more. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A review expert screening method applied to platform innovation capability review is characterized by comprising the following steps:
acquiring historical audit data, and establishing a category review screening word bank according to the category of a scientific and technological innovation platform;
acquiring technical field information of all experts in an expert database, and performing first classification screening according to the class review screening word bank to form classification expert sub-database data;
acquiring historical audit data, and establishing a technical review screening word bank and a management review screening word bank according to the category of a scientific and technological innovation platform;
acquiring review screening data information of all experts in the classified expert sub-database data, and performing first qualification by combining the technical review screening word bank and the management review screening word bank to form qualification review expert sub-database data;
and obtaining evaluation matching degree data of all experts in the qualification evaluation expert sub-database data by combining with historical review data, and performing matching degree analysis to form screening reference data.
2. The method for screening review experts applied to platform innovation capability review as claimed in claim 1, wherein the step of obtaining historical review data and establishing a category review screening lexicon according to the category of the scientific and technological innovation platform comprises:
acquiring historical audit data, extracting word class information of a basic technical field based on a subject engineering class, and forming a subject engineering class basic review screening word bank and a professional review screening word bank aiming at the subject engineering class basic review screening word bank;
acquiring historical review data, extracting word class information of the technical basic field based on technical innovation and result conversion classes, and forming a technical innovation and result conversion class basic review screening word bank and a professional review screening word bank aiming at the technical innovation and result conversion class basic review screening word bank;
obtaining historical review data, extracting word class information of the technical basic field based on basic support and condition guarantee class, and forming a basic support and condition guarantee class basic review screening word bank and a professional review screening word bank aiming at the basic support and condition guarantee class basic review screening word bank.
3. The method for screening review experts applied to platform innovation capability review as claimed in claim 2, wherein the step of obtaining technical field information of all experts in an expert database and performing a first classification screening according to the class review screening word library to form a classification expert sub-library data comprises:
acquiring technical data of all experts in an expert database, extracting subject field part of speech information and technical field part of speech information, and forming expert technical field information data;
according to the expert technical field information data, respectively performing word class matching of a subject engineering class basic review screening word bank, a technical innovation and result conversion class basic review screening word bank and a basic support and condition guarantee class basic review screening word bank, and determining the scientific and technological innovation platform class corresponding to the word bank with the highest matching degree as the scientific and technological innovation platform class to which the expert belongs;
according to the expert technical field information data, matching of the professional review screening word bank corresponding to the basic review screening word bank with the highest matching degree is carried out, and the subdivision professional technical field corresponding to the professional review screening word bank with the highest matching degree is determined;
according to the matching result of each expert, determining the scientific and technological innovation platform category corresponding to the basic review screening word bank with the highest matching degree determined by each expert as the scientific and technological innovation platform category to which the expert belongs, and determining the sub-technical field corresponding to the professional review screening word bank with the highest matching degree as the sub-technical field of expert research to form the classified expert sub-bank data aiming at different categories of scientific and technological innovation platforms.
4. The method for screening review experts applied to platform innovation capability review as claimed in claim 1, wherein the step of obtaining historical review data, establishing a technical review screening lexicon and managing the review screening lexicon according to the category of the scientific and technological innovation platform comprises:
acquiring the historical audit data, and respectively forming a basic technical review screening word bank and a professional technical review screening word bank aiming at three types of scientific and technological innovation platforms, namely a subject engineering type, a technical innovation and result conversion type and a basic support and condition guarantee type;
and acquiring the historical auditing data, and respectively forming a descriptive management review screening word bank and a professional term management review screening word bank aiming at three types of scientific and technological innovation platforms, namely a subject engineering class, a technical innovation and result conversion class and a basic support and condition guarantee class.
5. The method as claimed in claim 4, wherein the step of obtaining the review screening data information of all experts in the sub-database data of the classified experts, and combining the technical review screening word bank and the management review screening word bank to perform the first qualification review to form the sub-database data of the qualification experts comprises:
acquiring technical word information data and management word information data of all experts in the classified expert sub-database data;
matching the technical word information data with the basic technical review screening word bank and the professional technical review screening word bank respectively to obtain technical qualification review result data;
matching the management word class information data with the descriptive management review screening word library and the professional term management review screening word library respectively to obtain management qualification review result data;
and analyzing the technical qualification evaluation result data and the management qualification evaluation result data to form qualification evaluation expert sub-library data.
6. The method for screening review experts for platform innovation capability review as claimed in claim 5, wherein the analyzing the technical qualification result data and the management qualification result data to form the qualification expert sub-library data comprises:
setting a basic technology matching threshold, a professional technology matching threshold and a technology matching difference threshold;
comparing the matching degree of the basic technology review screening word bank with the basic technology matching threshold, comparing the matching degree of the professional technology review screening word bank with the professional technology matching threshold, comparing the matching degree difference value of the basic technology review screening word bank and the professional technology review screening word bank with the technology matching difference threshold, screening out experts meeting conditions, and forming a technical qualification review expert bank;
setting a descriptive management matching threshold and a professional term management matching threshold;
comparing the matching degree of the screening word bank according to the descriptive management evaluation with the descriptive management matching threshold value, screening out experts meeting conditions, and forming a management qualification review expert bank;
and determining experts which are overhauled on the aspects of technology and management according to the technical qualification expert database and the management qualification expert database to form the qualification expert subbase data.
7. The method for screening experts for auditing of platform innovation capabilities according to claim 1, wherein the step of obtaining the review matching degree data of all experts in the sub-base data of the qualification review experts by combining historical audit data, performing matching degree analysis, and forming screening reference data comprises the steps of:
analyzing the basic matching degree data of all experts in the qualification evaluation expert sub-database data according to historical evaluation data to form evaluation screening basic requirement data;
analyzing the evaluation matching degree data of all experts in the qualification evaluation expert sub-database data according to historical evaluation data to form evaluation experience requirement data;
acquiring academic contact information and relationship information of all experts in the qualification assessment expert sub-database data to form social relationship requirement data;
and establishing the screening reference data according to the evaluation screening basic requirement data, the evaluation experience requirement data and the social relationship requirement data.
8. The method for screening review experts applied to platform innovation capability review as claimed in claim 7, wherein the step of analyzing the basic matching degree data of all experts in the sub-library data of the qualification review experts according to historical review data to form the basic requirement data for review screening comprises the following steps:
setting job title requirements, and sequentially performing primary basic sorting on the job titles of all experts in the qualification review expert sub-database data to form primary basic sorting requirement data;
setting an age requirement, and carrying out secondary basic sorting arrangement on the ages of all experts in the qualification evaluation expert subbase data according to the primary basic sorting requirement data to form secondary basic sorting requirement data;
and defining academic active period rates, and sequentially carrying out third basic sorting and arrangement on the academic active period rates of all experts in the qualification review expert sub-library data according to the second basic sorting requirement data to form the review screening basic requirement data.
9. The method for screening review experts applied to platform innovation capability review as claimed in claim 8, wherein the step of analyzing the review matching degree data of all experts in the sub-base data of the qualification review experts according to the historical review data to form the review experience requirement data comprises the following steps:
counting the number of times that all experts in the qualification expert sub-database take part in the evaluation, and carrying out first experience sorting according to the sequence to form first experience sorting requirement data;
and analyzing the credit degrees of all experts in the qualification evaluation expert sub-database data according to the first experience sorting requirement data, and performing second experience sorting according to the analysis result to form the evaluation experience requirement data.
10. The method for screening experts for auditing of platform innovation capabilities according to claim 1, wherein said creating the screening reference data according to the screening basic requirement data and the review experience requirement data comprises:
and performing weight distribution on each item in the ranking of the review screening basic requirement data and the review experience requirement data, establishing a weight analysis model, and determining the screening grade of each expert according to the weight analysis model to form the screening reference data.
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