CN112396405A - Network recruitment management platform and method based on big data analysis - Google Patents
Network recruitment management platform and method based on big data analysis Download PDFInfo
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- CN112396405A CN112396405A CN202011414602.3A CN202011414602A CN112396405A CN 112396405 A CN112396405 A CN 112396405A CN 202011414602 A CN202011414602 A CN 202011414602A CN 112396405 A CN112396405 A CN 112396405A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/105—Human resources
- G06Q10/1053—Employment or hiring
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06398—Performance of employee with respect to a job function
Abstract
The invention relates to the field of human resource management, in particular to a network recruitment management platform based on big data analysis, which comprises a user end unit, a data platform end unit, an enterprise end unit and a recruitment platform webpage end unit; the system comprises a recruitment platform, a client side unit, a data platform unit and a recruitment platform unit, wherein the client side unit comprises a client login unit, a job consultation unit, a state query unit, a resume filling unit, a position release unit and a resume modification unit; the data platform end comprises a talent database unit, a data reading unit, a data processing unit and a data management unit; the enterprise end comprises an enterprise talent library, a resume query unit, an enterprise personnel port, an interview invitation unit and a position demand unit; the data management unit is connected with the enterprise talent base.
Description
Technical Field
The invention relates to the field of recruitment management systems, in particular to a network recruitment management platform and a network recruitment management method based on big data analysis, and belongs to the technical field of recruitment management.
Background
Currently, big data mining becomes a popular application field of computer information. The recruitment management system is a detailed branch of a human resource management system and comprises attraction of talents, source control, talent stock management and talent selection, the network recruitment link in the current human resource management of recruitment management has the characteristics of large resume amount, more position information, more analysis types and the like, mass resume data are manually carried out, and the mass data cause the problems of low efficiency, analysis rigor and the like of a human resource management department in the management process, waste of a large amount of manpower and material resources and cause that an optimal solution cannot be found between positions and recruitment.
Disclosure of Invention
In order to solve the technical problems, the invention provides a network recruitment management platform and a network recruitment management method based on big data analysis. The technical scheme of the invention is realized as follows:
a network recruitment management platform based on big data analysis comprises a user side, a data platform side, an enterprise side and a recruitment platform web page side, wherein the user side, the data platform side and the recruitment platform web page side are in bidirectional connection respectively; the input end of the enterprise end is connected with the data platform end, and the output end of the enterprise end is connected with the recruitment platform webpage end;
the user side is configured to comprise a client login unit, a job consultation unit, a state query unit, a resume filling unit, a position release unit and a resume modification unit; the position/interview issuing unit is bidirectionally connected with the recruitment platform webpage end and is used for sending position requirements or interview invitations to the recruitment platform webpage end or receiving the position requirements issued by the recruitment platform webpage end; the resume filling unit, the career consulting unit and the resume modifying unit are all connected with the position/interview issuing unit in a bidirectional mode, the career consulting unit is used for consulting information, the resume filling unit is used for filling job-seeking resumes, the state inquiring unit is used for acquiring recorded information, and the resume modifying unit is used for modifying resumes;
the data platform end comprises a talent database unit, a data reading unit, a data processing unit, a data analysis unit and a data management unit; specifically, the talent database is bidirectionally connected with the recruitment platform webpage end and is configured as a background storage device of the recruitment platform webpage end; the input end of the data reading unit is connected with the output end of the talent database and is used for reading the resume of the user; the input end of the data processing unit is connected with the output end of the data reading unit and is used for digitizing resume data to form an introducible sample set; the input end of the data analysis unit is connected with the output end of the data processing unit, and a big data analysis algorithm is arranged in the data analysis unit and is used for analyzing the sample set to form an analysis report; the input end of the data management unit is connected with the output end of the data analysis unit, the data management unit is configured to form a mapping set between the analysis report and the post requirement, and the output end of the data management unit is connected with the enterprise end;
the enterprise end comprises an enterprise talent library, a resume query unit, an enterprise personnel port, an interview invitation unit and a position demand unit; specifically, the input end of the enterprise talent base is connected with the output end of the data management unit, and is configured to read the analysis data of the data platform end and output the information to the resume query unit; the enterprise personnel port is connected with the resume query unit and the interview invitation unit in a bidirectional mode and is configured for querying talent resumes and data analysis reports, and the input end of the occupation demand unit is connected with the output end of the enterprise personnel port; further, the enterprise personnel port sends interview invitation or position requirements to an interview invitation unit or a position requirement unit according to the position requirements or the enterprise requirements; the output ends of the interview invitation unit and the position demand unit are connected to the recruitment platform webpage end, and interview invitation or position demand information is sent to the position/interview release unit through the recruitment platform webpage end.
In some embodiments, the data processing algorithm adopts a fuzzy genetic algorithm, and further, in the data processing unit, the resume data is divided into a plurality of fuzzy rules, wherein the fuzzy rules comprise working years, industry matching degrees, graduation colleges, graduation degrees, working skills and practice experiences, each fuzzy rule is scored according to three levels of 1, 2 and 5, and the final score is obtained by adopting a weighted average algorithm.
In some embodiments, in the data analysis unit, a built-in big data algorithm takes a weighted score of data processing as an input, connects a big data analysis model, and analyzes and predicts each item of data of the resume to form an analysis report;
in some embodiments, the data management unit is configured to compare the job requirements of the enterprise with the analysis reports, compare the job positions with the highest matching degree, and send the analysis results and the talent resume to the enterprise talent library.
In some embodiments, the big data algorithm is a deep neural network algorithm.
According to the recruitment management platform, the invention also discloses a recruitment management method, which specifically comprises the following steps:
step S1, data acquisition: reading talent resume information and job requirement information in a talent database;
step S2, dividing the position requirement: dividing the position requirements according to a fuzzy rule by using a fuzzy genetic algorithm; checking the integrity and consistency of the platform data, and judging whether the data is missing or not;
if the missing item between the resume and the job requirement exceeds the threshold value, deleting the data, and turning to step S7;
if no missing item exists between the resume and the job requirement or the missing item is within the threshold range, denoising and filling the missing domain are carried out on each data, each data is scored by using a genetic algorithm, the weighted sum of each iteration is calculated, the weighted sum is converted into a sample set, and the step S3 is carried out;
step S3, data analysis: importing the sample set data into a deep neural network model, converting the sample set data into a data analysis model, setting a feature matrix, taking a character string array of a corresponding feature vector as a sequence parameter, setting a lower limit of support degree and a lower limit of confidence degree of data mining, and entering a step S4 after forming a data analysis result;
step S4, data management: and importing the data analysis result into a data management unit, analyzing each item of data by using a decision tree mechanism, comparing enterprise demand information issued by an enterprise, matching an optimal result, and forming an analysis report.
Step S5, data output: and sending the analysis report and the personal resume to an enterprise talent library for corresponding position storage.
Step S6, interview invitation and job requirement issue: and the enterprise personnel end utilizes the resume query unit to query resumes and analysis reports in the enterprise talent library and issues interview invitation or position requirements.
Step S7 ends.
In some embodiments, the job demand division is divided by fuzzy rules, and the fuzzy rules comprise working years, industry matching degrees, graduates, working skills and practice experiences; each fuzzy rule is scored according to the three levels of 1, 2 and 5; the final score is obtained by adopting a weighted average algorithm.
In some embodiments, in step S2, the threshold of missing items between the resume and job requirements is no more than 3 items.
In some embodiments, in step S3: the lower limit of the support degree and the lower limit of the confidence degree of the data mining are both 92%.
The invention has the beneficial effects that: compared with the prior art, the invention has the following good technical effects that firstly, the job seeker can directly search position information after establishing the resume on the network, can directly deliver the resume according with conditions, and can interact with enterprises in real time; secondly, the delivery information is subjected to deep processing, mining, analysis and management, so that managers can find talents meeting conditions conveniently, and the speed and the accuracy of talent discrimination are improved; and thirdly, the system has an intelligent effect, better maintains and timely follows up talents, and meets the requirements of business development of enterprises on talents.
Drawings
Fig. 1 is a hardware schematic diagram of a network recruitment management platform based on big data analysis according to the present invention;
fig. 2 is a flowchart of a method for network recruitment management based on big data analysis according to the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1 and 2, a network recruitment management platform based on big data analysis comprises a user side, a data platform side, an enterprise side and a recruitment platform web page side, wherein the user side, the data platform side and the recruitment platform web page side are respectively connected in a bidirectional manner; the input end of the enterprise end is connected with the data platform end, and the output end of the enterprise end is connected with the recruitment platform webpage end;
the user side is configured to comprise a client login unit, a job consultation unit, a state query unit, a resume filling unit, a position release unit and a resume modification unit; the position/interview issuing unit is bidirectionally connected with the recruitment platform webpage end and is used for sending position requirements or interview invitations to the recruitment platform webpage end or receiving the position requirements issued by the recruitment platform webpage end; the resume filling unit, the career consulting unit and the resume modifying unit are all connected with the position/interview issuing unit in a bidirectional mode, the career consulting unit is used for consulting information, the resume filling unit is used for filling job-seeking resumes, the state inquiring unit is used for acquiring recorded information, and the resume modifying unit is used for modifying resumes;
the data platform end comprises a talent database unit, a data reading unit, a data processing unit, a data analysis unit and a data management unit; specifically, the talent database is bidirectionally connected with the recruitment platform webpage end and is configured as a background storage device of the recruitment platform webpage end; the input end of the data reading unit is connected with the output end of the talent database and is used for reading the resume of the user; the input end of the data processing unit is connected with the output end of the data reading unit and is used for digitizing resume data to form an introducible sample set; the input end of the data analysis unit is connected with the output end of the data processing unit, and a big data analysis algorithm is arranged in the data analysis unit and is used for analyzing the sample set to form an analysis report; the input end of the data management unit is connected with the output end of the data analysis unit, the data management unit is configured to form a mapping set between the analysis report and the post requirement, and the output end of the data management unit is connected with the enterprise end;
the enterprise end comprises an enterprise talent library, a resume query unit, an enterprise personnel port, an interview invitation unit and a position demand unit; specifically, the input end of the enterprise talent base is connected with the output end of the data management unit, and is configured to read the analysis data of the data platform end and output the information to the resume query unit; the enterprise personnel port is connected with the resume query unit and the interview invitation unit in a bidirectional mode and is configured for querying talent resumes and data analysis reports, and the input end of the occupation demand unit is connected with the output end of the enterprise personnel port; further, the enterprise personnel port sends interview invitation or position requirements to an interview invitation unit or a position requirement unit according to the position requirements or the enterprise requirements; the output ends of the interview invitation unit and the position demand unit are connected to the recruitment platform webpage end, and interview invitation or position demand information is sent to the position/interview release unit through the recruitment platform webpage end.
In some embodiments, the data processing algorithm adopts a fuzzy genetic algorithm, and further, in the data processing unit, the resume data is divided into a plurality of fuzzy rules, wherein the fuzzy rules comprise working years, industry matching degrees, graduation colleges, graduation degrees, working skills and practice experiences, each fuzzy rule is scored according to three levels of 1, 2 and 5, and the final score is obtained by adopting a weighted average algorithm.
Preferably, in the data analysis unit, a built-in big data algorithm takes the weighted score of data processing as input, connects with a big data analysis model, and analyzes and predicts each item of data of the resume to form an analysis report;
preferably, the data management unit is configured to compare the job requirements of the enterprise with the analysis reports, compare the job matching degree with the highest post, and send the analysis result and the talent resume to the enterprise talent library.
Preferably, the big data algorithm is a deep neural network algorithm.
According to the recruitment management platform, the invention also discloses a recruitment management method, which specifically comprises the following steps:
step S1, data acquisition: reading talent resume information and job requirement information in a talent database;
step S2, dividing the position requirement: dividing the position requirements according to a fuzzy rule by using a fuzzy genetic algorithm; checking the integrity and consistency of the platform data, and judging whether the data is missing or not;
if the missing item between the resume and the job requirement exceeds the threshold value, deleting the data, and turning to step S7;
if no missing item exists between the resume and the job requirement or the missing item is within the threshold range, denoising and filling the missing domain are carried out on each data, each data is scored by using a genetic algorithm, the weighted sum of each iteration is calculated, the weighted sum is converted into a sample set, and the step S3 is carried out;
step S3, data analysis: importing the sample set data into a deep neural network model, converting the sample set data into a data analysis model, setting a feature matrix, taking a character string array of a corresponding feature vector as a sequence parameter, setting a lower limit of support degree and a lower limit of confidence degree of data mining, and entering a step S4 after forming a data analysis result;
step S4, data management: and importing the data analysis result into a data management unit, analyzing each item of data by using a decision tree mechanism, comparing enterprise demand information issued by an enterprise, matching an optimal result, and forming an analysis report.
Step S5, data output: and sending the analysis report and the personal resume to an enterprise talent library for corresponding position storage.
Step S6, interview invitation and job requirement issue: and the enterprise personnel end utilizes the resume query unit to query resumes and analysis reports in the enterprise talent library and issues interview invitation or position requirements.
Step S7 ends.
Preferably, in step S2, the job requirement division is divided by fuzzy rules, where the fuzzy rules include a work year, an industry matching degree, a graduation institution, a graduation academic degree, a work skill and a practice experience; each fuzzy rule is scored according to the three levels of 1, 2 and 5; the final score is obtained by adopting a weighted average algorithm.
Preferably, in step S2, the threshold of missing items between resume and job requirement is no more than 3 items.
Preferably, in step S3: the lower limit of the support degree and the lower limit of the confidence degree of the data mining are both 92%.
The invention has the beneficial effects that: compared with the prior art, the invention has the following good technical effects that firstly, the job seeker can directly search position information after establishing the resume on the network, can directly deliver the resume according with conditions, and can interact with enterprises in real time; secondly, the delivery information is subjected to deep processing, mining, analysis and management, so that managers can find talents meeting conditions conveniently, and the speed and the accuracy of talent discrimination are improved; and thirdly, the system has an intelligent effect, better maintains and timely follows up talents, and meets the requirements of business development of enterprises on talents.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A network recruitment management platform based on big data analysis comprises a user side, a data platform side, an enterprise side and a recruitment platform web page side, wherein the user side, the data platform side and the recruitment platform web page side are in bidirectional connection respectively; the input end of the enterprise end is connected with the data platform end, and the output end of the enterprise end is connected with the recruitment platform webpage end;
the method is characterized in that:
the user side is configured to comprise a client login unit, a job consultation unit, a state query unit, a resume filling unit, a position release unit and a resume modification unit; the position/interview issuing unit is bidirectionally connected with the recruitment platform webpage end and is used for sending position requirements or interview invitations to the recruitment platform webpage end or receiving the position requirements issued by the recruitment platform webpage end; the resume filling unit, the career consulting unit and the resume modifying unit are all connected with the position/interview issuing unit in a bidirectional mode, the career consulting unit is used for consulting information, the resume filling unit is used for filling job-seeking resumes, the state inquiring unit is used for acquiring recorded information, and the resume modifying unit is used for modifying resumes;
the data platform end comprises a talent database unit, a data reading unit, a data processing unit, a data analysis unit and a data management unit; specifically, the talent database is bidirectionally connected with the recruitment platform webpage end and is configured as a background storage device of the recruitment platform webpage end; the input end of the data reading unit is connected with the output end of the talent database and is used for reading the resume of the user; the input end of the data processing unit is connected with the output end of the data reading unit and is used for digitizing resume data to form an introducible sample set; the input end of the data analysis unit is connected with the output end of the data processing unit, and a big data analysis algorithm is arranged in the data analysis unit and is used for analyzing the sample set to form an analysis report; the input end of the data management unit is connected with the output end of the data analysis unit, the data management unit is configured to form a mapping set between the analysis report and the post requirement, and the output end of the data management unit is connected with the enterprise end;
the enterprise end comprises an enterprise talent library, a resume query unit, an enterprise personnel port, an interview invitation unit and a position demand unit; specifically, the input end of the enterprise talent base is connected with the output end of the data management unit, and is configured to read the analysis data of the data platform end and output the information to the resume query unit; the enterprise personnel port is connected with the resume query unit and the interview invitation unit in a bidirectional mode and is configured for querying talent resumes and data analysis reports, and the input end of the occupation demand unit is connected with the output end of the enterprise personnel port; further, the enterprise personnel port sends interview invitation or position requirements to an interview invitation unit or a position requirement unit according to the position requirements or the enterprise requirements; the output ends of the interview invitation unit and the position demand unit are connected to the recruitment platform webpage end, and interview invitation or position demand information is sent to the position/interview release unit through the recruitment platform webpage end.
2. The network recruitment management platform of claim 1, wherein: the data processing unit processes resume data by adopting a fuzzy genetic algorithm, the resume data are divided into a plurality of levels of fuzzy rules, the fuzzy rules comprise working years, industry matching degrees, graduation schools, graduation academic ranks, working skills and practice experiences, each fuzzy rule scores according to the three levels of 1, 2 and 5, and finally the scores are obtained by adopting a weighted average algorithm.
3. The network recruitment management platform according to claim 1 or 2, wherein: and a big data algorithm is built in the data analysis unit, a weighted score obtained by processing the data is used as input, a big data analysis model is connected, and analysis and prediction are carried out on each item of data of the resume to form an analysis report.
4. The network recruitment management platform according to claim 1 or 2, wherein: the data management unit is configured to compare various job requirements of the enterprise with the analysis report, compare the job positions with the highest matching degree, and send the analysis result and the talent resume to the enterprise talent library.
5. The network recruitment management platform according to claim 1 or 2, wherein: the big data algorithm is a deep neural network algorithm.
6. A recruitment management method applied to the big data analysis-based network recruitment management platform according to any one of claims 1 to 5, comprising the following steps:
step S1, data acquisition: reading talent resume information and job requirement information in a talent database;
step S2, dividing the position requirement: dividing the position requirements according to a fuzzy rule by using a fuzzy genetic algorithm; checking the integrity and consistency of the platform data, and judging whether the data is missing or not;
if the missing item between the resume and the job requirement exceeds the threshold value, deleting the data, and turning to step S7;
if no missing item exists between the resume and the job requirement or the missing item is within the threshold range, denoising and filling the missing domain are carried out on each data, each data is scored by using a genetic algorithm, the weighted sum of each iteration is calculated, the weighted sum is converted into a sample set, and the step S3 is carried out;
step S3, data analysis: importing the sample set data into a deep neural network model, converting the sample set data into a data analysis model, setting a feature matrix, taking a character string array of a corresponding feature vector as a sequence parameter, setting a lower limit of support degree and a lower limit of confidence degree of data mining, and entering a step S4 after forming a data analysis result;
step S4, data management: importing the data analysis result into a data management unit, analyzing each item of data by using a decision tree mechanism, comparing enterprise demand information issued by an enterprise, matching an optimal result, and forming an analysis report;
step S5, data output: sending the analysis report and the personal resume to an enterprise talent library for corresponding position storage;
step S6, interview invitation and job requirement issue: the enterprise personnel end utilizes the resume query unit to query resumes and analysis reports in the enterprise talent library and issues interview invitation or position requirements;
step S7 ends.
7. The recruitment management method of claim 6 wherein: the job position demand division is divided by adopting fuzzy rules, and the fuzzy rules comprise working years, industry matching degrees, graduation schools, graduation academic positions, working skills and practice experiences; each fuzzy rule is scored according to the three levels of 1, 2 and 5; the final score is obtained by adopting a weighted average algorithm.
8. The recruitment management method according to claim 6 or 7, wherein: in step S2, the threshold for missing items between resume and job requirement is no more than 3 items.
9. The recruitment management method according to claim 6 or 7, wherein: in step S3: the lower limit of the support degree and the lower limit of the confidence degree of the data mining are both 92%.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221013A (en) * | 2021-06-04 | 2021-08-06 | 金保信社保卡科技有限公司 | Occupational development planning application method and system |
| CN113706096A (en) * | 2021-07-29 | 2021-11-26 | 上海优尔蓝信息科技有限公司 | Network recruitment platform system based on big data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221013A (en) * | 2021-06-04 | 2021-08-06 | 金保信社保卡科技有限公司 | Occupational development planning application method and system |
| CN113706096A (en) * | 2021-07-29 | 2021-11-26 | 上海优尔蓝信息科技有限公司 | Network recruitment platform system based on big data |
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Application publication date: 20210223 |