CN112766869A - Man-sentry matching algorithm for digital human resource management - Google Patents
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Abstract
A kind of digital human resources management's personnel and sentry matching algorithm, step 1: acquiring the post system of the enterprise and public institution and the information of the current on-post personnel; step 2: establishing a capacity model according to an on-duty personnel sample model of an enterprise and public institution: and step 3: establishing a recruitment block model according to the on-duty personnel sample model of the enterprise and public institution; and 4, step 4: performing information matching on the resume of the recruiter and the recruitment plate model; and 5: performing information matching on the resume of the one trial and employment personnel and the basic capability model; step 6: performing information matching on the resume of the second-examination recruiter and the specific capability model; and 7: performing information matching on the resumes of the three-trial participants and the on-duty personnel sample model of the enterprise and public institution; and 8: and allocating the posts according to the resume of the final-examination and recruitment personnel. The big data matching algorithm is used, the matching degree of the resume of the recruiter and each model is calculated, the accuracy is improved, and the problem of matching between a large number of talents and posts is solved.
Description
Technical Field
The invention relates to the technical field of post management in human resource management, in particular to a human post matching algorithm for digital human resource management.
Background
With the development of society, the post work efficiency of an enterprise and public institution determines the work progress and work efficiency of an enterprise or public institution. Enterprises and institutions, namely enterprise units and institutions. A business entity generally refers by default to a nationally owned business entity. The institution refers to a social service organization established by government using national assets and engaged in education, science and technology, culture, health and other activities. The business entity receives government leaders, and is a legal entity in the form of an organization or organization.
The cadre management and team construction work of the nationally owned enterprises put forward higher requirements along with the deepening and the reformation of the national enterprise and the development target of 'national enterprise goes away', wherein the personnel matching plays a vital role in the cadre management work, namely, candidates which most accord with the arbitrary requirements and are most matched with the working experience are selected for the target post, so that the maximum value of talents in the target post is exerted.
The existing talent selection and evaluation work is mainly subjective scoring and judgment, and quantitative analysis of the value of each section of work history of talents and the matching degree of target posts is lacked. The manual scoring easily causes the problems of inconsistent evaluation standards, poor score comparability due to different scoring groups, and the like. Therefore, the manual scoring mode cannot meet the requirement of quantitative analysis on one hand, and on the other hand, the problem of lack of comparability and fairness is easily caused.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a human-job matching algorithm for digital human resource management.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a kind of people's post matching algorithm of the digitized human resources management, including the following steps:
step 1: acquiring the post system of the enterprise and public institution and the information of the current on-post personnel;
step 2: establishing a capacity model according to an on-duty personnel sample model of an enterprise and public institution:
and step 3: establishing a recruitment block model according to the on-duty personnel sample model of the enterprise and public institution;
and 4, step 4: performing information matching on the resume of the recruiter and the recruitment plate model;
and 5: performing information matching on the resume of the one trial and employment personnel and the basic capability model;
step 6: performing information matching on the resume of the second-examination recruiter and the specific capability model;
and 7: performing information matching on the resumes of the three-trial participants and the on-duty personnel sample model of the enterprise and public institution;
and 8: and allocating the posts according to the resume of the final-examination and recruitment personnel.
The invention also has the following additional technical features:
the technical scheme of the invention is further specifically optimized as follows: in the step 1: collecting the current post system of the enterprise and public institution, including organization structure, department function and post specification; collecting basic information of current on-duty personnel of the enterprise and public institution, wherein the basic information comprises names, departments to which the enterprise and public institution belongs, posts, sexes, ages, academic calendars, working ages and employment types; collecting the current employment information of the enterprises and public institutions, including recent work summary and plan, work content, condition of holding work, leave-on and attendance checking condition, subjective work pressure, efficiency and quality; collecting a sample list of on-duty personnel of the posts of the enterprise and public institution; and generating an on-duty personnel sample model of the enterprise and public institution.
The technical scheme of the invention is further specifically optimized as follows: in the step 2: the position capability model comprises two basic capability models and a specific capability model, wherein the basic capability model comprises basic skill requirements and enterprise culture, and the specific capability model comprises skill requirements of a specific post; data of employees with excellent performance on the existing recruitment posts are collected through an enterprise HR, the data collection is completed through simulation and evaluation, a post performance evaluation matrix is collected, the commonalities in the data are classified into a basic capability model of a company, and the rest data (skill requirements of a specific post) are classified into a specific capability model.
The technical scheme of the invention is further specifically optimized as follows: in the step 3: the recruitment block model comprises: basic information plates, education and training requirement plates, post responsibility plates, job development tendency plates, and other recruitment requirement plates.
The technical scheme of the invention is further specifically optimized as follows: in the step 4: a recruitment block model, wherein a standard recruitment model is automatically generated in a server; performing information matching on the resume of the recruiters and the recruitment plate model, starting from the recruitment plate model, performing linear regression on the relationship among all variables, calculating the capacity value of each student at each position by a scoring algorithm, then sequencing the scores of all the students, obtaining a matching degree index in percentiles, and obtaining a competitive ranking; and selecting to obtain a resume of the trial personnel according to the order of the positions.
The technical scheme of the invention is further specifically optimized as follows: in the step 5: matching the resume of the one-sided contestant with the basic capability model, starting from the basic capability model, linearly regressing the relation among all variables, calculating the capability value of each student at each position by a scoring algorithm, then sequencing the scores of all persons, obtaining a matching degree index in percentile, and obtaining a competitive ranking; and selecting to obtain the resume of the second-examination recruiter according to the position sequence.
The technical scheme of the invention is further specifically optimized as follows: in the step 6: performing information matching on the resume of the second-trial participants and the specific capacity model, starting from the specific capacity model, performing linear regression to obtain the relationship among all variables, calculating the capacity value of each student at each position by a scoring algorithm, then sequencing scores of all persons, obtaining a matching degree index in percentiles, and obtaining a competitive ranking; and selecting to obtain the resumes of the three-examination recruiters according to the order of the positions.
The technical scheme of the invention is further specifically optimized as follows: in step 7: performing information matching on the resumes of the three-trial participants and the on-duty personnel sample models of the enterprises and public institutions, starting from the on-duty personnel sample models of the enterprises and public institutions, performing linear regression to obtain the relation among all variables, calculating the capability value of each student at each position by a scoring algorithm, then ordering the scores of all the students, obtaining a matching degree index in percentiles, and obtaining a competitive ranking; and selecting to obtain the resume of the final-examination recruiter according to the order of the positions.
The technical scheme of the invention is further specifically optimized as follows: in step 8: the method for allocating the posts according to the resume of the final-examination recruiter comprises the following steps:
the method comprises the following steps: a post preliminary screening list;
the requirement of a post system threshold + the requirement of excellent job of a post;
screening candidates meeting the conditions;
the service personnel adjust the screening conditions according to actual requirements;
forming a primary screening list;
step two: preferentially screening a calculation model;
adjusting the inspection dimension weight based on the cadre history scoring model;
forming a candidate overall score;
step three: comparing the candidate cadre ranks;
and ranking according to the final scores of the candidates.
Forming a final initiative list advantages of the invention over the prior art are:
advantage (1): in order to match the optimal candidate for the target post, the work records of the candidate talents need to be evaluated in a fair, logical and quantifiable mode, ranking comparison is formed among the candidates, and support is provided for final decision scientifically and quantificationally.
Advantage (2): the invention uses big data matching algorithm to calculate the matching degree of the resume of the engaging personnel and each model, and uses the data to obtain the competitiveness and the engaging rank of each person for a specific position, thereby having self-learning ability, improving the accuracy through training and use, solving the problem that the matching selection between a large number of talents and the positions mainly depends on the manual selection of HR at the present stage, saving the time cost, improving the efficiency and achieving better effect.
Advantage (3): the invention carries out quantitative analysis on the past work history of the people, namely, the value of each section of work experience is comprehensively evaluated and scored based on the working duration and by combining with the multidimensional factors such as the mechanism where the reference is located, the talent level and the like, and finally the total score of the past history is formed. More objective data support is provided for transversely comparing the candidate, a uniform comparison standard is provided, and the matching degree of the candidate and the target post and the working quality of post matching are effectively improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a human job matching algorithm for digital human resource management according to the present invention;
FIG. 2 is a schematic diagram of a method for allocating posts according to a final review recruiter resume of the present invention;
FIG. 3 is a schematic diagram of the model for establishing the capability according to the on duty personnel sample model of the enterprise and public institution.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings, in order that the present disclosure may be more fully understood and fully conveyed to those skilled in the art. While the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the invention is not limited to the embodiments set forth herein.
A kind of people's post matching algorithm of the digitized human resources management, including the following steps:
step 1: acquiring the post system of the enterprise and public institution and the information of the current on-duty personnel.
And collecting the current post system of the enterprise and public institution, which comprises an organization structure, department functions and post specifications. Collecting the basic information of the current on-duty personnel of the enterprise and public institution, including name, affiliated department, post, gender, age, academic calendar, working age and employment type. Collecting the current employment information of the enterprises and institutions, including recent work summary and plan, work content, double work condition, leave and attendance condition, subjective work pressure, efficiency and quality. And collecting a sample list of the on-duty personnel of the posts of the enterprise and public institution. And generating an on-duty personnel sample model of the enterprise and public institution.
Step 2: establishing a capacity model according to an on-duty personnel sample model of an enterprise and public institution:
the position capability model comprises two basic capability models and a specific capability model, wherein the basic capability model comprises basic skill requirements and enterprise culture, and the specific capability model comprises skill requirements of a specific position. Data of employees with excellent performance on the existing recruitment posts are collected through an enterprise HR, the data collection is completed through simulation and evaluation, a post performance evaluation matrix is collected, the commonalities in the data are classified into a basic capability model of a company, and the rest data (skill requirements of a specific post) are classified into a specific capability model.
And step 3: and establishing a recruitment block model according to the on-duty personnel sample model of the enterprise and public institution.
The recruitment block model comprises: basic information plates, education and training requirement plates, post responsibility plates, job development tendency plates, and other recruitment requirement plates.
And 4, step 4: and performing information matching on the resume of the recruiter and the recruitment plate block model.
And (4) recruiting the plate block model, and automatically generating a standard recruiting model in the server. And performing information matching on the resume of the recruiter and the recruitment block model, starting from the recruitment block model, performing linear regression on the relationship among all variables, calculating the capacity value of each student at each position by using a scoring algorithm, then sequencing the scores of all the students, obtaining a matching degree index according to the percentile, and obtaining a competitive ranking. And selecting to obtain a resume of the trial personnel according to the order of the positions.
And 5: and performing information matching on the resume of the one trial participant and the basic capability model.
And performing information matching on the resume of the one-sided contestant and the basic capability model, starting from the basic capability model, performing linear regression on the relationship among all variables, calculating the capability value of each student at each position by using a scoring algorithm, then sequencing scores of all persons, obtaining a matching degree index in percentiles, and obtaining a competitive ranking. And selecting to obtain the resume of the second-examination recruiter according to the position sequence.
Step 6: and performing information matching on the resume of the second-trial participants and the specific capability model.
And performing information matching on the resume of the second-trial participants and the specific capacity model, starting from the specific capacity model, performing linear regression on the relationship among all variables, calculating the capacity value of each student at each position by using a scoring algorithm, then sequencing scores of all persons, obtaining a matching degree index in percentiles, and obtaining a competitive ranking. And selecting to obtain the resumes of the three-examination recruiters according to the order of the positions.
And 7: and performing information matching on the resumes of the three-trial participants and the on-duty personnel sample model of the enterprise and public institution.
And performing information matching on the resumes of the three-trial participants and the on-duty personnel sample models of the enterprises and public institutions, starting from the on-duty personnel sample models of the enterprises and public institutions, performing linear regression on the relations among all variables, calculating the capability value of each student at each position by using a scoring algorithm, then ordering the scores of all the students, obtaining a matching degree index in percentiles, and obtaining a competitive ranking. And selecting to obtain the resume of the final-examination recruiter according to the order of the positions.
And 8: and allocating the posts according to the resume of the final-examination and recruitment personnel. The method comprises the following steps:
the method comprises the following steps: a post preliminary screening list;
the requirement of a post system threshold + the requirement of excellent job of a post;
screening candidates meeting the conditions;
the service personnel adjust the screening conditions according to actual requirements;
forming a primary screening list;
step two: preferentially screening a calculation model;
adjusting the inspection dimension weight based on the cadre history scoring model;
forming a candidate overall score;
step three: comparing the candidate cadre ranks;
ranking according to the final scores of the candidates;
forming a final kinetic list. In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described above with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the above detailed description of the embodiments of the invention presented in the drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Claims (9)
1. A personnel and post matching algorithm for digital human resource management is characterized by comprising the following steps:
step 1: acquiring the post system of the enterprise and public institution and the information of the current on-post personnel;
step 2: establishing a capacity model according to an on-duty personnel sample model of an enterprise and public institution;
and step 3: establishing a recruitment block model according to the on-duty personnel sample model of the enterprise and public institution;
and 4, step 4: performing information matching on the resume of the recruiter and the recruitment plate model;
and 5: performing information matching on the resume of the one trial and employment personnel and the basic capability model;
step 6: performing information matching on the resume of the second-examination recruiter and the specific capability model;
and 7: performing information matching on the resumes of the three-trial participants and the on-duty personnel sample model of the enterprise and public institution;
and 8: and allocating the posts according to the resume of the final-examination and recruitment personnel.
2. The human job matching algorithm for digitalized human resource management according to claim 1, wherein:
in the step 1: collecting the current post system of the enterprise and public institution, including organization structure, department function and post specification; collecting basic information of current on-duty personnel of the enterprise and public institution, wherein the basic information comprises names, departments to which the enterprise and public institution belongs, posts, sexes, ages, academic calendars, working ages and employment types; collecting the current employment information of the enterprises and public institutions, including recent work summary and plan, work content, condition of holding work, leave-on and attendance checking condition, subjective work pressure, efficiency and quality; collecting a sample list of on-duty personnel of the posts of the enterprise and public institution; and generating an on-duty personnel sample model of the enterprise and public institution.
3. The human job matching algorithm for digitalized human resource management according to claim 1, wherein:
in the step 2: the position capability model comprises two basic capability models and a specific capability model, wherein the basic capability model comprises basic skill requirements and enterprise culture, and the specific capability model comprises skill requirements of a specific post; data of employees with excellent performance on the existing recruitment posts are collected through an enterprise HR, the data collection is completed through simulation and evaluation, a post performance evaluation matrix is collected, the commonalities in the data are classified into a basic capability model of a company, and the rest data (skill requirements of a specific post) are classified into a specific capability model.
4. The human job matching algorithm for digitalized human resource management according to claim 1, wherein:
in the step 3: the recruitment block model comprises: basic information plates, education and training requirement plates, post responsibility plates, job development tendency plates, and other recruitment requirement plates.
5. The human job matching algorithm for digitalized human resource management according to claim 1, wherein:
in the step 4: a recruitment block model, wherein a standard recruitment model is automatically generated in a server; performing information matching on the resume of the recruiters and the recruitment block model, starting from the recruitment block model, performing linear regression on the relationship among variables (variables including but not limited to basic capacity and specific capacity), calculating the capacity value of each recruiter in each position by a scoring algorithm, then sequencing the scores of all persons, obtaining a matching degree index in percentiles, and obtaining a competitive ranking; and selecting to obtain a resume of the trial personnel according to the order of the positions.
6. The human job matching algorithm for digitalized human resource management according to claim 1, wherein:
in the step 5: performing information matching on the resume of the one trial participant and the basic capability model, starting from the basic capability model, performing linear regression on the relationship among all variables, calculating the capability value of each job position by using a scoring algorithm, then sequencing the scores of all persons, obtaining a matching degree index according to the percentile, and obtaining a competitive ranking; and selecting to obtain the resume of the second-examination recruiter according to the position sequence.
7. The human job matching algorithm for digitalized human resource management according to claim 1, wherein:
in the step 6: performing information matching on the resume of the second-examination recruiters and the specific capacity model, starting from the specific capacity model, performing linear regression to obtain the relation among all variables, calculating the capacity value of each recruiter in each position by a scoring algorithm, then sequencing the scores of all persons, obtaining a matching degree index in percentiles, and obtaining a competitive ranking; and selecting to obtain the resumes of the three-examination recruiters according to the order of the positions.
8. The human job matching algorithm for digitalized human resource management according to claim 1, wherein:
in step 7: performing information matching on the resumes of the three-trial participants and the on-duty personnel sample models of the enterprises and public institutions, starting from the on-duty personnel sample models of the enterprises and public institutions, performing linear regression to obtain the relation among all variables, calculating the capacity value of each competitor in each position by using a scoring algorithm, then sequencing the scores of all persons, obtaining a matching degree index in percentiles, and obtaining a competitive ranking; and selecting to obtain the resume of the final-examination recruiter according to the order of the positions.
9. The human job matching algorithm for digitalized human resource management according to claim 1, wherein:
in step 8: the method for allocating the posts according to the resume of the final-examination recruiter comprises the following steps:
the method comprises the following steps: a post preliminary screening list;
the requirement of a post system threshold + the requirement of excellent job of a post;
screening candidates meeting the conditions;
the service personnel adjust the screening conditions according to actual requirements;
forming a primary screening list;
step two: preferentially screening a calculation model;
adjusting the inspection dimension weight based on the cadre history scoring model;
forming a candidate overall score;
step three: comparing the candidate cadre ranks;
ranking according to the final scores of the candidates;
forming a final kinetic list.
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