CN110874694A - Talent classification platform based on big data - Google Patents

Talent classification platform based on big data Download PDF

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Publication number
CN110874694A
CN110874694A CN201911110144.1A CN201911110144A CN110874694A CN 110874694 A CN110874694 A CN 110874694A CN 201911110144 A CN201911110144 A CN 201911110144A CN 110874694 A CN110874694 A CN 110874694A
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big data
talent
employee
evaluation unit
evaluation
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CN201911110144.1A
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廖均
廖尘浩
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Sichuan Chuangfeng Information Technology Co Ltd
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Sichuan Chuangfeng Information Technology Co Ltd
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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
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G06F16/26—Visual data mining; Browsing structured data
    • 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
    • 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
    • G06Q10/10—Office automation; Time management
    • G06Q10/105—Human resources

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  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
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  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
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Abstract

The invention discloses a talent classification platform based on big data, which comprises: the employee information module comprises a first evaluation unit, a second evaluation unit and a third evaluation unit and is used for evaluating the indexes of the employees; and the data analysis module is used for evaluating the employee information module by different evaluation subjects and scoring according to preset weights to obtain talent classification. The invention has comprehensiveness and fairness for talent classification.

Description

Talent classification platform based on big data
Technical Field
The invention relates to the technical field of big data, in particular to a talent classification platform based on big data.
Background
Talents are strategic resources for realizing ethnic revival and gaining international active competitiveness. Talent evaluation is an important prerequisite for talent selection, talent use and cultivation, and maximum utilization of talents can be realized only by reasonable classification of talents.
The existing talents are classified according to single standard, and the talents are difficult to be classified comprehensively. In addition, the subjects of talent evaluation are unclear, and the results of talent classification are not fair.
Disclosure of Invention
In order to solve the technical problems, the invention provides a talent classification platform based on big data so as to realize comprehensiveness and justice of talent classification.
The invention provides a talent classification platform based on big data, which comprises: the employee information module comprises a first evaluation unit, a second evaluation unit and a third evaluation unit and is used for evaluating the indexes of the employees; and the data analysis module is used for evaluating the employee information module by different evaluation subjects and scoring according to preset weights to obtain talent classification.
By adopting the technical scheme, the data analysis module evaluates the first evaluation unit, the second evaluation unit and the third evaluation unit of the staff respectively so as to realize comprehensiveness of talent classification; meanwhile, different evaluation subjects are adopted for evaluation so as to realize the fairness of the evaluation of the talents; meanwhile, different weights are set for different talents, and comprehensiveness and fairness of talent classification are further achieved.
Preferably, the first evaluation unit is staff professional ethics. Employee career moral includes career specifications, responsibility trust, and scientific quality.
Preferably, the second evaluation unit is employee competency. Employee competency qualities include: psychology, knowledge innovation and social practice.
Preferably, the third evaluation unit contributes to the performance of the employee. Employee performance contributions include: and (5) performing result and benefit conversion.
Preferably, the subject of evaluation is: government departments, human units, and the public.
Preferably, the subject of evaluation is: peer experts and intermediaries.
Preferably, the subject of evaluation is: external markets and consumer enterprises.
Preferably, the data analysis module comprises a data mining unit, the data mining unit is used for cleaning and converting data of the staff information module to form a feature table, a Spark R is adopted to construct a data mining model, and big data analysis is carried out on the staff information through the data model.
Preferably, the talent classification includes: basic research talents, application research talents, technical development talents, and achievement transformation talents.
In summary, according to the talent classification platform based on big data provided by the invention, different evaluation subjects can realize comprehensiveness and fairness of employee evaluation by evaluating different weights of the first evaluation unit, the second evaluation unit and the third evaluation unit of the employee.
Drawings
FIG. 1 is a block diagram of the big data-based talent classification platform according to the present invention.
Reference numerals:
10. the system comprises an employee information module, 101, a first evaluation unit, 102, a second evaluation unit, 103 and a third evaluation unit; 20. the data analysis module 201 is a data mining unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The existing classification standard of talents is single and cannot adapt to the classification of compound talents in the current society.
Referring to fig. 1, the present invention provides a talent classification platform based on big data, comprising: the employee information module 10, wherein the employee information module 10 comprises a first evaluation unit 101, a second evaluation unit 102 and a third evaluation unit 103, and is used for evaluating the indexes of the employees; and the data analysis module 20 evaluates the employee information module 10 by different evaluation subjects, and scores the employee information according to preset weights to obtain talent classification.
By adopting the technical scheme, the data analysis module 20 evaluates the first evaluation unit 101, the second evaluation unit 102 and the third evaluation unit 103 of the staff respectively so as to realize comprehensiveness of talent classification; meanwhile, different evaluation subjects are adopted for evaluation so as to realize the fairness of talent evaluation; meanwhile, different weights are set for different talents, and comprehensiveness and fairness of talent classification are further achieved.
On the basis of the above embodiment, further, the first evaluation unit 101 is the employee job morality. Employee career moral includes career specifications, responsibility trust, and scientific quality.
On the basis of the above embodiment, further, the second evaluation unit 102 is the employee competency. Employee competency qualities include: mental quality, knowledge innovation, and social practice.
On the basis of the above embodiment, further, the third evaluation unit 103 contributes to the employee performance. Employee performance contributions include: and (5) performing achievement and benefit transformation.
On the basis of the above examples, further, the evaluation subjects were: government, human and social entities.
On the basis of the above examples, further, the evaluation subjects were: peer experts and intermediaries.
On the basis of the above examples, further, the evaluation subjects were: external markets and consumer enterprises.
On the basis of the above embodiment, the data analysis module 20 further includes a data mining unit 201, which performs data cleaning and conversion on the employee information module 10 to form a feature table, and adopts Spark R to construct a data mining model, and performs big data analysis on the employee information through the data model.
It should be noted that the data mining module uses HIVE as a data cleaning engine to provide PB (Petabyte, beat byte, terabyte or kt byte) level data preprocessing, processing and integrating services to form a feature width table, and based on the data of the feature width table, algorithms such as Spark R are used to invoke clustering and classification to perform model development, model evaluation and model application of data mining. And carrying out big data analysis on the human talent data through a data mining model.
On the basis of the above embodiment, further, the talent classification includes: basic research talents, application research talents, technical development talents, and achievement transformation talents.
The invention also provides another specific embodiment:
the weight of the first evaluation unit 101 by the government department is 5%, the weight of the first evaluation unit 101 by the human unit is 15%, and the weight of the first evaluation unit 101 by the public is 2%; the peer expert's weight for the second evaluation unit 102 is set to 70%, and the agency's weight for the second evaluation unit 102 is set to 10%; the external market sets different weights for the weight of the third evaluation unit 103, wherein the weight for the application research talents is set to 25%, the weight for the basic research talents is set to 0, the weight for the technical development talents is set to 25%, and the weight for the achievement transformation talents is set to 20%; the user enterprises set different weights for the weight of the third evaluation unit 103, wherein the weight for the application research talent is set to 35%, the weight for the basic research talent is set to 0, the weight for the technology development talent is set to 35%, and the weight for the achievement transformation talent is set to 40%. And classifying talents according to the different evaluation subjects.
In summary, according to the talent classification platform based on big data provided by the present invention, different evaluation subjects can evaluate the staff by different weights through the first evaluation unit 101, the second evaluation unit 102, and the third evaluation unit 103, so as to achieve comprehensiveness and fairness of staff evaluation.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (9)

1. A talent classification platform based on big data, comprising:
the employee information module comprises a first evaluation unit, a second evaluation unit and a third evaluation unit and is used for evaluating the indexes of the employees;
and the data analysis module is used for evaluating the employee information module by different evaluation subjects and scoring according to preset weights to obtain talent classification.
2. The big data-based talent classification platform according to claim 1, wherein the first evaluation unit is employee career moral; the employee professional morality includes professional norms, responsibility trust, and scientific quality.
3. The talent classification platform based on big data according to claim 1, wherein the second evaluation unit is employee competency; the employee competency quality comprises: mental quality, knowledge innovation, and social practice.
4. The talent classification platform based on big data according to claim 1, wherein the third evaluation unit contributes to employee performance; the employee performance contribution comprises: and (5) performing achievement and benefit transformation.
5. The talent classification platform based on big data according to any one of claims 1 or 2, wherein the evaluation subject is: government departments, human units, and the public.
6. The talent classification platform based on big data according to any one of claims 1 or 3, wherein the evaluation subject is: peer experts and intermediaries.
7. The talent classification platform based on big data according to claim 1 or 4, wherein the evaluation subjects are: external markets and consumer enterprises.
8. The talent classification platform based on big data according to claim 1, wherein the data analysis module comprises a data mining unit, the employee information module is subjected to data cleaning and conversion to form a feature table, a data mining model is constructed by sparkR, and big data analysis is performed on the employee information through the data model.
9. The big data-based talent classification platform of claim 1, wherein the talent classification comprises: basic research talents, application research talents, technical development talents, and achievement transformation talents.
CN201911110144.1A 2019-11-14 2019-11-14 Talent classification platform based on big data Pending CN110874694A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115471200A (en) * 2022-08-05 2022-12-13 广州红海云计算股份有限公司 Cloud computing-based human resource data management system, method and device

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115471200A (en) * 2022-08-05 2022-12-13 广州红海云计算股份有限公司 Cloud computing-based human resource data management system, method and device

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