Academic talent flow rule dynamic monitoring method and model based on resume analysis method

Abstract

The invention relates to the field of information and communication technologies, in particular to an academic talent flow rule dynamic monitoring method and model based on a resume analysis method.The monitoring method comprises the following steps that resume data are obtained, and keyword data are extracted from the resume data through keywords; screening the keyword data according to the age limit and the region, retaining the screening result of the required age limit and the region, further carrying out education background screening on the screening result, storing resume data of different education background in different education background databases, and analyzing the resume data in the education background databases; screening the doctor resumes of which the educational background is the doctor, analyzing the academic achievements in the doctor resumes, and reserving the talent resumes meeting the screening conditions and storing the talent resumes in the talent database; extracting national flow information in the talent resume; and extracting employment information in the resume data. The method has the effects of performing multi-aspect screening and analysis on the resumes and improving the reference of the screening result.

Description

Technical Field

[0001] The present invention relates to the field of information and communication technology, and in particular to a method and model for dynamically monitoring the flow of academic talents based on resume analysis. Background Art

[0002] When conducting research on academic talents with different academic qualifications, it involves the analysis of the employment areas and mobility of academic talents, and it is necessary to screen and monitor the academic qualifications, working hours, study time, study place, work place, reasons for location change, etc. However, the current screening of academic talents can generally only screen academic qualifications or mobility separately. When faced with the more detailed screening requirements required by academic talent research, the screening results are difficult to meet the requirements of academic talent research, resulting in the screening results being not referenceable or increasing the workload of academic talent research. Summary of the invention

[0003] In order to solve the problem that when screening academic talents, the single screening condition leads to the poor reference value of the screening results when applied to academic talent research, the present invention provides a dynamic monitoring method and model for the flow law of academic talents based on resume analysis.

[0004] The present invention provides a method for dynamically monitoring the flow of academic talents based on resume analysis, which adopts the following technical solutions:

[0005] A method for dynamically monitoring the flow of academic talents based on resume analysis includes the following steps:

[0006] Obtain resume data, and extract keyword data from the resume data by using keywords;

[0007] Filter the keyword data according to years and regions, retain the screening results of required years and regions, further screen the screening results by academic qualifications, save the resume data of different academic qualifications in different academic qualification databases, and analyze the resume data in the academic qualification databases;

[0008] Screen the resumes of PhD candidates, analyze the academic achievements in the resumes, and based on the preset screening conditions, retain the resumes of talents that meet the screening conditions and store them in the talent database;

[0009] Extract the country flow information from the resumes of talents, generate charts and output them;

[0010] Extract employment information from resume data, generate charts and output them.

[0011] In a specific possible implementation scheme, when extracting keyword data, if the keyword cannot be identified for extraction, the resume data is sent to a manual database.

[0012] In a specific implementation scheme, the academic degree database includes an undergraduate degree database, a master's degree database, a doctoral degree database and a postdoctoral database.

[0013] The present invention provides a dynamic monitoring model of academic talent flow law based on resume analysis method, which adopts the following technical scheme:

[0014] A dynamic monitoring model for the flow of academic talents based on resume analysis, including:

[0015] A keyword grabbing unit extracts keyword data from the acquired resume data based on keywords;

[0016] A table arrangement unit obtains the keyword data extracted by the keyword capture unit and generates a table;

[0017] The resume analysis unit obtains the table of the table sorting unit, filters the keyword data according to years and regions, and then further filters the keyword data according to education and work status, and stores the screening results by category according to education;

[0018] Obtain a database of workers with doctoral degrees and save the resume data of those with doctoral degrees;

[0019] The global academic talent extraction unit analyzes the academic achievements in the resume data of the PhD students in the resume analysis unit, and retains and outputs the resumes of talents that meet the preset screening conditions;

[0020] A flow pattern extraction unit is used to obtain and store the talent resume output by the global academic talent extraction unit, extract the country flow information in the talent resume and generate a first chart, and output the first chart;

[0021] The dynamic monitoring mechanism extraction unit extracts the employment information in the resume data and generates a second chart, and outputs the second chart.

[0022] In a specific possible implementation scheme, a manual sorting unit is also included. If the keyword capture unit cannot identify the keywords in the establishment data, the resume data is stored in a manual database.

[0023] In a specific implementation scheme, the resume analysis unit includes:

[0024] Online resume grabbing module to obtain the table generated by the table sorting unit;

[0025] The years of service screening module filters resume data based on years of service;

[0026] Regional screening module, to screen resume data based on region;

[0027] The education screening module obtains the resume data filtered by the region screening module and the years screening module, and classifies the resume data according to education and work status;

[0028] The undergraduate education database module stores the resume data of undergraduate students;

[0029] The master's degree database module stores the resume data of those with a master's degree;

[0030] PhD degree database module, which stores resume data of PhD degree holders;

[0031] Postdoctoral database module, which stores the resume data of postdoctoral students.

[0032] In a specific implementation scheme, the resume analysis unit further includes:

[0033] The undergraduate education experience analysis module obtains the resume data in the undergraduate education database module and analyzes the education experience, education flow pattern, and education level span;

[0034] The master's education experience analysis module obtains the resume data in the master's degree database module and analyzes the education experience, education flow pattern, and education level span;

[0035] Doctoral education experience analysis module, which obtains resume data from the doctoral degree database module and analyzes educational experience, educational mobility patterns, and educational level span;

[0036] The postdoctoral education experience analysis module obtains the resume data in the postdoctoral database module and analyzes the education experience, education mobility pattern, and education level span.

[0037] In a specific possible implementation plan, the global academic talent extraction unit includes:

[0038] The collection module obtains the resume data of the workers who have obtained doctoral degrees in the database, and the resume data of the workers who have doctoral degrees in the resume analysis unit;

[0039] The doctoral screening module screens out the resume data of those with a doctoral degree from the database of doctoral degree workers and the resume analysis unit;

[0040] The comprehensive assessment module obtains the resume data screened by the doctoral screening module, extracts the academic achievements and academic outputs in the resume data for analysis;

[0041] The talent data extraction module obtains the analysis results of the comprehensive assessment module, compares them with the preset screening conditions, retains the resume data that meets the screening conditions and outputs them.

[0042] In a specific embodiment, the flow pattern extraction unit includes:

[0043] Talent database module, which stores the resumes of talents output by the global academic talent extraction unit;

[0044] The module for extracting destination information based on countries analyzes the destination countries, reasons for going to the countries, and the countries where they teach in the resumes of talents in the talent database module;

[0045] A sorting module obtains the analysis results output by the terminal flow information extraction module based on countries and sorts them into charts;

[0046] Output module, outputs the charts generated by the sorting module.

[0047] In a specific implementation scheme, the dynamic monitoring model of academic talent flow rules based on resume analysis is trained through a neural network.

[0048] In summary, the present invention includes at least one of the following beneficial technical effects:

[0049] 1. Through the cooperation between the online resume capture module, years of experience screening module, region screening module and academic qualification screening module, the required talents can be screened out according to academic qualifications, years of experience and regional restrictions, and then targeted dynamic monitoring of flow patterns can be carried out to improve the reference value of the dynamic monitoring results of talent flow patterns.

[0050] 2. Through the cooperation among the collection module, doctoral screening module, comprehensive assessment module and talent data extraction module, targeted information extraction can be carried out on workers who have obtained doctoral degrees, making the monitored data more specific.

[0051] 3. Through the cooperation between the talent database module, the country-based destination flow information extraction module, the sorting module and the output module, the information of talents going to different countries can be extracted and sorted, which is convenient for checking the number of talents going to different countries and providing data for subsequent research. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is the principle block diagram of the dynamic monitoring model of academic talent flow law based on resume analysis method;

[0053] Figure 2 This is the principle block diagram of the resume analysis unit;

[0054] Figure 3 A block diagram of the global academic talent extraction unit;

[0055] Figure 4 This is the principle block diagram of the flow law extraction unit;

[0056] Figure 5This is the principle block diagram of the dynamic monitoring mechanism extraction unit.

[0057] Explanation of the accompanying symbols: 1. Octopus collection port; 2. Keyword capture unit; 3. Resume analysis unit; 31. Online resume capture module; 32. Years screening module; 33. Region screening module; 34. Education screening module; 35. Undergraduate education database module; 36. Master's education database module; 37. Doctoral education database module; 38. Postdoctoral database module; 39. Undergraduate education experience analysis module; 310. Master's education experience analysis module; 311. Doctoral education experience analysis module; 312. Postdoctoral education experience analysis module; 4. Table sorting unit; 5. Global academic people Talent extraction unit; 51. Collection module; 52. Doctoral screening module; 53. Comprehensive assessment module; 54. Talent data extraction module; 6. Manual sorting unit; 7. Flow pattern extraction unit; 71. Talent database module; 72. Destination flow information extraction module based on country; 73. Sorting module; 74. Output module; 8. Database of workers with doctoral degrees; 9. Dynamic monitoring mechanism extraction unit; 91. Employment information database module; 92. Employment flow extraction module based on year; 93. Analysis module; 94. Dynamic summary module; 10. Mental network model establishment unit. DETAILED DESCRIPTION

[0058] The following is combined with Figure 1-5 The present invention is described in further detail.

[0059] The dynamic monitoring method of academic talent flow law based on resume analysis method includes the following steps:

[0060] S100, obtaining resume data.

[0061] The resume data is collected through the data collection port, and the keyword data is extracted from the resume data using the preset keywords and keyword crawling technology. The keyword data includes academic qualifications, academic qualification acquisition time, graduation school, work experience, country of destination, time of destination, etc. The extracted keyword data is output in the form of a table.

[0062] If the keyword cannot be captured for keyword extraction, the resume data is sent to the manual database, and the keyword data in the resume is extracted manually. The keyword data extracted manually and the keyword data extracted automatically are output in the form of a table.

[0063] S200, screening the resume data, and classifying and storing the screening results according to the educational background.

[0064] First, the keyword data is filtered according to years and regions, and then further filtered according to education level and work status. The resume data is divided into four categories according to education level: undergraduate resume data, master resume data, doctoral resume data and postdoctoral resume data, and stored in the preset undergraduate degree database, master degree database, doctoral degree database and postdoctoral database respectively. That is, undergraduate resume data is stored in the undergraduate degree database, master resume data is stored in the master degree database, and so on.

[0065] Further analysis is conducted on the resume data in the undergraduate degree database, master's degree database, doctoral degree database and postdoctoral database, and the educational experience, educational mobility patterns, educational level span, etc. in the resume data are parsed to facilitate the analysis of talent mobility patterns.

[0066] S300, resume data analysis and talent resume extraction.

[0067] Filter resume data with a doctoral degree, save it as a doctoral resume, and analyze the academic achievements and academic outputs in the doctoral resume. According to the preset screening conditions, screen the doctoral resumes, retain the doctoral resumes that meet the screening conditions, save them as talent resumes, and save them in the talent database. For ease of understanding, take the number of academic papers as an example for further explanation. The preset screening condition is to publish 2 academic papers after obtaining a doctoral degree. Then, perform semantic analysis on the academic achievements and academic outputs in the doctoral resume, analyze the number of academic papers published after obtaining a doctoral degree, and if the number of academic papers published after obtaining a doctoral degree is greater than or equal to 2, save the doctoral resume as a talent resume and store it in the talent database; otherwise, do not save it.

[0068] S400 generates and outputs charts of national flow information in talent resumes.

[0069] Obtain the resumes of talents in the talent database and analyze them by country. Analyze the destination countries, reasons for going to the countries, countries where they taught, length of stay in each country, etc. in the resumes of talents, generate charts and output them to facilitate understanding of the flow of talents.

[0070] S500, extracting employment information from resume data, generating and outputting a chart.

[0071] The employment information extracted from the resume data is saved in the employment information database. The employment information is called and the employment status, unit location, job change status, job change reasons, etc. are analyzed in units of years. Charts are generated and output to facilitate understanding of talent flow.

[0072] It should be noted that, in actual operation, step S500 and step S200 have no sequence, and the sequence number is only used for distinction.

[0073] The present invention also discloses a dynamic monitoring model of academic talent flow law based on resume analysis method, comprising:

[0074] Data collection port, collects resume data from web pages, etc. The data collection port can be Octopus, Alibaba data collector, train collector, etc.

[0075] The keyword grabbing unit 2 extracts keyword data from the resume data through preset keywords and keyword extraction technology.

[0076] The table arrangement unit 4 obtains the keyword data output by the keyword capture unit 2, generates a table and outputs it, so as to facilitate viewing and analyzing the resume data.

[0077] The resume analysis unit 3 includes an online resume grabbing module 31, a years screening module 32, a region screening module 33 and an education screening module 34. The online resume grabbing module 31 obtains the table and resume data generated by the table sorting unit 4, and outputs them to the years screening module 32 and the region screening module 33, and the resume data is screened by the years screening module 32 and the region screening module 33. The years screening module 32 screens the resume data according to years, and the region screening module 33 screens the resume data according to regions.

[0078] It should be noted that the service life screening module 32 and the region screening module 33 can perform all screening, one of them, or neither of them according to the call. When the service life screening module 32 and the region screening module 33 jointly screen the resume data, they can be screened by service life first or by region first.

[0079] The education screening module 34 obtains the screening results output by the years screening module 32 and the region screening module 33, classifies and screens the resume data according to the education level and work status, and outputs them to different databases.

[0080] The resume analysis unit 3 also includes a bachelor's degree database module 35, a master's degree database module 36, a doctoral degree database module 37 and a postdoctoral database module 38. The bachelor's degree database module 35 has a bachelor's degree database for storing resume data of undergraduates. The master's degree database module 36 has a master's degree database for storing resume data of masters. The doctoral degree database module 37 has a doctoral degree database for storing resume data of doctors. The postdoctoral database module 38 has a postdoctoral database for storing resume data of postdoctoral students.

[0081] The resume analysis unit 3 also includes an undergraduate education experience analysis module 39, a master's education experience analysis module 310, a doctoral education experience analysis module 311, and a postdoctoral education experience analysis module 312. The undergraduate education experience analysis module 39 obtains the resume data in the undergraduate education database module 35, analyzes the education experience, education flow law, education level span, etc., so as to understand the flow of undergraduate talents. The master's education experience analysis module 310 obtains the resume data in the master's education database module 36, analyzes the education experience, education flow law, education level span, etc., so as to understand the flow of master talents. The doctoral education experience analysis module 311 obtains the resume data in the doctoral education database module 37, analyzes the education experience, education flow law, education level span, etc., so as to understand the flow of doctoral talents. The postdoctoral education experience analysis module 312 obtains the resume data in the postdoctoral database module 38, analyzes the education experience, education flow law, education level span, etc., so as to understand the flow of postdoctoral talents.

[0082] The manual sorting unit 6 obtains manual operation when the keyword capture unit 2 is unable to extract keywords, performs manual operation to extract keyword data from the resume data, and classifies the resume data, which are stored in the bachelor's degree database module 35, the master's degree database module 36, the doctoral degree database module 37 and the postdoctoral database module 38 respectively.

[0083] A database 8 of workers with doctoral degrees is obtained, and when the keyword capture unit 2 extracts keyword data, the resume data of those with doctoral degrees is identified, and the work information in the resume data is extracted and stored.

[0084] The global academic talent extraction unit 5 includes a collection module 51, a doctoral screening module 52, a comprehensive evaluation module 53 and a talent data extraction module 54. The collection module 51 obtains the resume data in the database 8 of workers with doctoral degrees and the resume data in the doctoral degree database, and outputs them to the doctoral screening module 52. The doctoral screening module 52 screens the obtained resume data of the doctoral degree according to the preset selection conditions, retains the resume data that meets the selection conditions, and outputs them to the comprehensive evaluation module 53. The comprehensive evaluation module 53 extracts the academic achievements and academic outputs in the resume data for parsing and analysis, and outputs the analysis results to the talent data extraction module 54. The talent data extraction module 54 compares the analysis results of the comprehensive evaluation module 53 with the preset screening conditions, retains the resume data that meets the screening conditions and outputs them. The resume data that meets the screening conditions is the talent resume, that is, the talent data extraction module 54 outputs the talent resume.

[0085] The flow pattern extraction unit 7 includes a talent database module 71, a terminal flow information extraction module 72 based on countries, a sorting module 73 and an output module 74. The talent database module 71 obtains the talent resume output by the talent data extraction module 54 and stores it. The terminal flow information extraction module 72 based on countries analyzes the talent resumes in the talent database module 71, parses and analyzes the destination country, the reason for going to the country, the country of teaching, etc. in the talent resume, and outputs the analysis results. The sorting module 73 obtains the analysis results output by the terminal flow information extraction module 72 based on countries, sorts and generates a first chart, and outputs it visually through the output module 74.

[0086] The dynamic monitoring mechanism extraction unit 9 includes an employment information database module 91, an annual employment flow extraction module 92, an analysis module 93 and a dynamic summary module 94. The employment information database module 91 obtains the employment information in the resume data collected by the data collection port and stores it. The annual employment flow extraction module 92 analyzes the employment information in the employment information database module 91, parses and analyzes the employment situation, the location of the unit, the job change situation, the reason for the job change, etc. in the employment information, and outputs the analysis results. The analysis module 93 obtains the analysis results output by the annual employment flow extraction module 92, organizes and generates a second chart, and outputs it visually through the dynamic summary module 94.

[0087] The academic talent flow regularity dynamic monitoring model based on resume analysis of the present invention can be trained by neural networks and machine learning, thereby improving the accuracy of model analysis.

[0088] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made according to the structure, shape, and principle of the present invention specification should be included in the protection scope of the present invention.

Claims

1. A method for dynamically monitoring the flow of academic talents based on resume analysis, characterized in that: The steps include: Obtain resume data, and extract keyword data from the resume data by using keywords; Filter the keyword data according to years and regions, retain the screening results of the required years and regions, further screen the screening results by academic qualifications, save the resume data of different academic qualifications in different academic qualification databases, and analyze the resume data in the academic qualification databases; Screen the resumes of PhD candidates, analyze the academic achievements in the resumes, and based on the preset screening conditions, retain the resumes of talents that meet the screening conditions and store them in the talent database; Extract the country flow information from the resumes of talents, generate charts and output them; Extract employment information from resume data, generate charts and output them.

2. The method for dynamically monitoring the flow of academic talents based on resume analysis according to claim 1 is characterized by: When extracting keyword data, if the keyword cannot be identified for extraction, the resume data will be sent to the manual database.

3. The method for dynamically monitoring the flow of academic talents based on resume analysis according to claim 1 is characterized by: The academic degree database includes an undergraduate degree database, a master's degree database, a doctoral degree database and a postdoctoral database.

4. A dynamic monitoring model for the flow of academic talents based on resume analysis, characterized by: include: A keyword grabbing unit (2) extracts keyword data from the acquired resume data according to the keywords; A table arrangement unit (4) acquires the keyword data extracted by the keyword capture unit (2) and generates a table; The resume analysis unit (3) obtains the table of the table sorting unit (4), filters the keyword data according to years and regions, and then further filters the keyword data according to education level and work status, and stores the screening results by category according to education level; Obtain a database of workers with doctoral degrees (8), and save the resume data of those with doctoral degrees in the resume data; The global academic talent extraction unit (5) analyzes the academic achievements in the resume data of the candidates with a doctoral degree in the resume analysis unit (3), and retains and outputs the resumes of the candidates that meet the screening conditions according to the preset screening conditions; A flow pattern extraction unit (7) is used to obtain and store the talent resume output by the global academic talent extraction unit (5), extract the country flow information in the talent resume and generate a first chart, and output the first chart; The dynamic monitoring mechanism extraction unit (9) extracts the employment information in the resume data and generates a second chart, and outputs the second chart.

5. The dynamic monitoring model of academic talent flow law based on resume analysis method according to claim 4 is characterized by: It also includes a manual sorting unit (6), which stores the resume data in a manual database if the keyword grabbing unit (2) cannot identify the keywords in the established data.

6. The dynamic monitoring model of academic talent flow law based on resume analysis method according to claim 4 is characterized by: The resume analysis unit (3) includes: An online resume grabbing module (31) obtains the table generated by the table sorting unit (4); The years of service screening module (32) screens resume data according to years of service; A regional screening module (33) is used to screen resume data according to regions; The education screening module (34) obtains the resume data screened by the region screening module (33) and the years screening module (32), and classifies the resume data according to education and work status; Bachelor degree database module (35), storing resume data of undergraduates; Master's degree database module (36), storing resume data of people with a master's degree; Doctoral degree database module (37), storing resume data of people with doctoral degree; The postdoctoral database module (38) stores the resume data of postdoctoral students.

7. The dynamic monitoring model of academic talent flow law based on resume analysis method according to claim 6 is characterized by: The resume analysis unit (3) also includes: The undergraduate education experience analysis module (39) obtains the resume data in the undergraduate education database module (35) and analyzes the education experience, education flow pattern, and education level span; A master's education experience analysis module (310) obtains resume data from the master's degree database module (36) and analyzes education experience, education flow rules, and education level span; A doctoral education experience analysis module (311) obtains resume data from the doctoral degree database module (37) and analyzes educational experience, educational mobility patterns, and educational level span; The postdoctoral education experience analysis module (312) obtains the resume data in the postdoctoral database module (38) and analyzes the education experience, education flow pattern, and education level span.

8. The dynamic monitoring model of academic talent flow law based on resume analysis method according to claim 4 is characterized by: The Global Academic Talent Extraction Unit (5) includes: A collection module (51) acquires resume data of workers with doctoral degrees from a database (8) and resume data of workers with doctoral degrees from a resume analysis unit (3); A doctoral screening module (52) screens the obtained resume data of persons with doctoral degrees according to preset selection conditions; A comprehensive evaluation module (53) obtains the resume data screened by the doctoral screening module (52), extracts the academic achievements and academic outputs in the resume data for analysis; The talent data extraction module (54) obtains the analysis results of the comprehensive evaluation module (53), compares them with the preset screening conditions, retains the resume data that meets the screening conditions and outputs them.

9. The dynamic monitoring model of academic talent flow law based on resume analysis method according to claim 4 is characterized by: The flow pattern extraction unit (7) comprises: A talent database module (71) for storing the talent resumes output by the global academic talent extraction unit (5); The terminal flow information extraction module (72) based on countries analyzes the destination country, the reason for going to the country, and the country of teaching in the resume of the talent in the talent database module (71); A sorting module (73) obtains the analysis results output by the terminal flow information extraction module (72) in units of countries and sorts them into a chart; The output module (74) outputs the chart generated by the sorting module (73).

10. The dynamic monitoring model of academic talent flow law based on resume analysis method according to claim 4 is characterized by: The dynamic monitoring model of academic talent flow rules based on resume analysis is trained through a neural network.

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