CN113408930B - Employee behavior analysis method, system and storage medium based on employee service system - Google Patents
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Abstract
The invention discloses a staff behavior analysis method and a system based on a staff service system, wherein when a new staff is in a practice period, the interest and literacy of the staff are obtained according to the behavior data of a WeChat ecosphere of a new staff enterprise, so that the character, interest and occupation literacy of the staff are obtained, and the staff can be arranged in a matched post conveniently. If the personal characters of new employees who like to share are biased outwards, the method is suitable for the post of promotion or reception; if new employees with more originality are available, the system is suitable for planning or activity-planning posts, the new employees can be assisted to find a development direction suitable for the new employees as soon as possible, and the management efficiency and the cultivation effect of the new employees are improved. It should be noted that although the adaptive work post is matched according to the interest and literacy of the new employee, the comprehensive scoring is still needed to quantify, and whether the employee is qualified for the post or not is determined, so that the situation that the post arranges the new employee with insufficient capability to influence the operation of the work project is avoided, and the benefit of the enterprise is ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a staff behavior analysis method and system based on a staff service system and a storage medium.
Background
In the banking industry, newly-entered employees need to go to the basic level network site for practice, but the sites of all the networks are scattered, so that the personnel departments in the branch lines cannot deeply understand the characters and all the aspects of the abilities of the fresh blood. The most adopted modes of the existing personnel departments are as follows: questionnaire survey, face-to-face communication, department leader report or human resource department detection and the like, and the interest and literacy of the staff are evaluated. The method is time-consuming and labor-consuming, needs to spend a large amount of resources, has strong subjective factors, and can draw a reasonable conclusion on the interest and literacy of the staff only by a human resource department with abundant experience.
Disclosure of Invention
The invention aims to provide a staff behavior analysis method and system based on a staff service system, and aims to solve the technical problems that the existing investigation mode is time-consuming and labor-consuming, needs to spend a large amount of resources, has strong subjective factors, and can obtain a reasonable conclusion on the interest and literacy of staff only by a human resource department with abundant experience.
In order to achieve the purpose, the invention adopts the following technical scheme: the first aspect of the present invention discloses, as an optional implementation manner, a method for analyzing employee behavior based on an employee service system, including:
step S1: acquiring enterprise WeChat ecosphere behavior data of new employees in a practice period;
step S2: the enterprise WeChat ecosphere behavior data is sorted and analyzed, and the interest literacy of the employee is obtained;
and step S3: establishing a quantitative scoring model of the employee, quantitatively scoring the interest literacy of the employee, and calculating to obtain a comprehensive score of the employee;
and step S4: according to the employee's interest and literacy matching with the appropriate work post, judging whether the employee is qualified for the work post by using the comprehensive score; if yes, arranging the employee to the work post; if not, matching the next working post until the employee is arranged to the competent working post;
step S5: tracking and acquiring the enterprise wechat ecosystem behavior data after the employee arrives at post, and sorting and analyzing the enterprise wechat ecosystem behavior data after the employee arrives at post again to obtain the interest literacy of the employee after arrives at post;
step S6: optimizing and adjusting the quantitative scoring model, and increasing the relevance between the quantitative scoring model of the employee and the employee at the current working post;
step S7: quantitatively scoring the interest literacy of the employee after the employee arrives at post according to the optimized and adjusted quantitative scoring model, and recalculating to obtain a comprehensive score of the employee after the employee arrives at post;
step S8: and when the staff position is adjusted, returning to the step S5.
As an alternative embodiment, in the first aspect of the present invention, the method for establishing the quantitative scoring model of the employee comprises:
determining a scoring project according to the behavior category of the enterprise WeChat ecological circle;
evaluating scores of the employees in various project categories;
and setting up the scoring weight of each item type, and summing the products of the scores of the item types and the corresponding scoring weights.
As an alternative embodiment, in the first aspect of the present invention, the enterprise wechat ecosystem behavior category includes an active participation behavior and a passive participation behavior, and in the scoring weight of the quantitative scoring model, the scoring weight of the active participation behavior is greater than the scoring weight of the passive participation behavior.
As an alternative embodiment, in the first aspect of the present invention, the method for optimizing the quantitative scoring model for adjusting the interest literacy comprises:
analyzing professional skills and experience insights required by the continuous development of the employee at the current working post;
and according to the analyzed professional skill and experience, adding corresponding scoring items and setting scores for the newly added scoring items.
As an alternative embodiment, in the first aspect of the present invention, the method for optimizing the quantitative scoring model for adjusting the literacy of interest further comprises: and analyzing the post characteristics of the employee at the current working post, and adjusting the scoring weight in the quantitative scoring model according to the post characteristics of the current working post.
The invention discloses a second aspect of employee behavior analysis system based on employee service system, including: the system comprises a first acquisition module, a first sorting and analyzing module, a first quantitative scoring module, a post arrangement module, a second acquisition module, a first sorting and analyzing module, a quantitative scoring model optimizing module, a second quantitative scoring module and an iteration module;
the first acquisition module is used for acquiring enterprise wechat ecosphere behavior data of new employees during training;
the first sorting and analyzing module is used for sorting and analyzing the enterprise WeChat ecosphere behavior data to obtain the interest literacy of the employee;
the first quantitative scoring module is used for establishing a quantitative scoring model of the employee, quantitatively scoring the interest literacy of the employee and calculating to obtain a comprehensive score of the employee;
the post arrangement module is used for judging whether the employee is qualified for the working post or not by utilizing the comprehensive score according to the working post matched with the employee's interest and literacy; if yes, arranging the employee to the work post; if not, matching the next working post until the employee is arranged to the competent working post;
the second acquisition module is used for tracking and acquiring enterprise wechat ecosphere behavior data after the employee arrives at post;
the first sorting and analyzing module is used for sorting and analyzing the enterprise WeChat ecological circle behavior data of the employee after the employee arrives at post to obtain the interest literacy of the employee after the employee arrives at post;
the quantitative scoring model optimizing module is used for optimizing and adjusting a quantitative scoring model and increasing the relevance between the quantitative scoring model of the employee and the employee at the current working post;
the second quantitative scoring module is used for quantitatively scoring the interest literacy of the employee after the employee arrives at post according to the optimized and adjusted quantitative scoring model, and recalculating to obtain the comprehensive score of the employee;
and the iteration module is used for calculating to obtain the comprehensive score of the employee after the employee arrives at post by utilizing the second acquisition module, the first sorting and analyzing module, the quantitative scoring model optimizing module and the second quantitative scoring module when the post of the employee is adjusted.
As an alternative embodiment, in the second aspect of the present invention, the first quantitative scoring module includes:
the scoring item determining module is used for determining scoring items according to the behavior types of the enterprise WeChat ecosphere;
the score determining module is used for setting scores of all the project types according to the rating project types;
the weight setting module is used for setting scoring weights of various project types;
and the comprehensive scoring calculation module is used for summing the products of the scores of the various project types and the corresponding scoring weights.
As an alternative implementation manner, in the second aspect of the present invention, the quantitative scoring model optimization module includes a post analysis module for analyzing professional skills and experience insights required by the employee for continuous development in the current working post;
the scoring item determining module is also used for adding corresponding scoring items according to the analyzed professional skills and experience insights;
the score determining module is further used for setting scores for the scoring items newly added by the scoring item determining module.
As an optional implementation manner, in the second aspect of the present invention, the post analysis module is further configured to analyze the post characteristics of the employee at the current working post, and the weight setting module is further configured to adjust the scoring weight in the quantitative scoring model according to the post characteristics of the employee at the current working post.
In a third aspect of the present invention, a computer-readable storage medium is disclosed, where a computer program is stored, and when the computer program is executed by a processor, the method for analyzing staff behavior based on a staff service system according to any of the above embodiments is implemented.
One of the above technical solutions has the following advantages or beneficial effects:
in the embodiment of the invention, when a new employee is in practice, the interest and literacy of the employee are obtained according to the behavior data of the WeChat ecosystem of the new employee, so that the character, interest and occupation literacy of the employee are obtained, and the employee is arranged in a suitable position. If the personal characters of new employees who like to share are biased outwards, the method is suitable for the post of promotion or reception; if new employees with more originality are available, the system is suitable for planning or activity-planning posts, the new employees can be assisted to find a development direction suitable for the new employees as soon as possible, and the management efficiency and the cultivation effect of the new employees are improved. It should be noted that although the adaptive work post is matched according to the interest and literacy of the new employee, the comprehensive scoring is still needed to quantify and determine whether the employee is qualified for the post, so as to avoid the situation that the post arranges the new employee with insufficient capability to influence the operation of the work project and ensure the benefit of the enterprise.
And after the new employee arrives at the post, continuously tracking the interest literacy of the employee after arriving at the post, and finishing and analyzing the enterprise WeChat ecological cycle behavior data of the employee after arriving at the post again to obtain the interest literacy of the employee after arriving at the post. Therefore, the change of the interest literacy of the employee during the practice period and after the employee arrives at post can be perceived in time, the false impression brought by the subjective factors of the employee during the practice period can be recognized, and the staff can be deeply known by a human resource department.
The relevance between the quantitative scoring module of the employee and the current working post of the employee is increased by optimizing and adjusting the quantitative scoring model, so that the quantitative scoring model of the employee is more fit with the current working post, and the accuracy of comprehensive scoring after the employee arrives at the post is improved. And then, the optimized and adjusted quantitative scoring module is used for recalculating the comprehensive score of the employee after the employee arrives at post and examining the new employee so as to be beneficial to accurately judging whether the current interest literacy of the new employee is suitable for the current post, reasonable data is used as reference, and a relatively reasonable conclusion can be drawn on the interest literacy of the employee without a human resource department with abundant experience.
When the position of the employee changes and is adjusted, the quantitative scoring module of the employee is subjected to iterative optimization, so that the quantitative scoring module can make corresponding adjustment according to the position change of the employee, the comprehensive scoring effectiveness of the employee is improved, and the personnel resource department can check whether the employee is suitable for the current position.
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FIG. 1 is a schematic flow diagram of one embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, 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 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.
The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or article.
The first embodiment is as follows:
in the following, with reference to fig. 1, a method for analyzing employee behavior based on an employee service system according to an embodiment of the present invention is described, including: step S1: acquiring enterprise WeChat ecosphere behavior data of new employees in a practice period; specifically, the staff service system can be built through the mobile application platform, new staff are authorized to acquire information in the staff service system at enterprise WeChat, the staff service system can be used for enterprise WeChat ecosphere behavior data, and the method has the advantages of being timely in data updating and high in acquisition efficiency. The enterprise WeChat ecosystem behavior comprises but is not limited to activity voting, training courses, examinations, questionnaires, creative applications, book sharing, optimization suggestions and walking activities.
Step S2: the enterprise WeChat ecological cycle behavior data is sorted and analyzed, and the interest literacy of the employee is obtained; specifically, the interest literacy includes, but is not limited to, employee personality, personal interests, life philosophy, expertise, and work attitudes.
And step S3: establishing a quantitative scoring model of the employee, quantitatively scoring the interest literacy of the employee, and calculating to obtain a comprehensive score of the employee;
and step S4: according to the employee's interest and literacy matching with the appropriate job position, judging whether the employee is qualified for the job position by using comprehensive scoring; if yes, arranging the employee to the work post; if not, matching the next working post until the employee is arranged to the competent working post;
step S5: tracking and acquiring the enterprise wechat ecosystem behavior data after the employee arrives at post, and sorting and analyzing the enterprise wechat ecosystem behavior data after the employee arrives at post again to obtain the interest literacy of the employee after arrives at post;
step S6: optimizing and adjusting the quantitative scoring model, and increasing the relevance between the quantitative scoring model of the employee and the employee at the current working post;
step S7: quantitatively scoring the interest literacy of the employee after the employee arrives at post according to the optimized and adjusted quantitative scoring model, and recalculating to obtain a comprehensive score of the employee after the employee arrives at post;
step S8: and when the staff position is adjusted, returning to the step S5.
In the embodiment of the invention, when a new employee is in practice, the interest and literacy of the employee are obtained according to the behavior data of the WeChat ecosystem of the new employee, so that the character, interest and occupation literacy of the employee are obtained, and the employee is arranged in a suitable position. If the personal characters of new employees who like to share are biased outwards, the method is suitable for the post of promotion or reception; if new employees with more originality are available, the system is suitable for planning or activity-planning posts, the new employees can be assisted to find a development direction suitable for the new employees as soon as possible, and the management efficiency and the cultivation effect of the new employees are improved. It should be noted that although the adaptive work post is matched according to the interest and literacy of the new employee, the comprehensive scoring is still needed to quantify and determine whether the employee is qualified for the post, so as to avoid the situation that the post arranges the new employee with insufficient capability to influence the operation of the work project and ensure the benefit of the enterprise.
And after the new employee arrives at the post, continuously tracking the interest literacy of the employee after arriving at the post, and finishing and analyzing the enterprise WeChat ecological cycle behavior data of the employee after arriving at the post again to obtain the interest literacy of the employee after arriving at the post. Therefore, the change of the interest literacy of the employee during the practice period and after the employee arrives at post can be perceived in time, the false impression brought by the subjective factors of the employee during the practice period can be recognized, and the staff can be deeply known by a human resource department.
The relevance of the quantitative scoring module of the staff and the current work post of the staff is increased by optimizing and adjusting the quantitative scoring model, so that the quantitative scoring model of the staff is more fit with the current work post, and the accuracy of comprehensive scoring after the staff arrives at the post is improved. And then, the optimized and adjusted quantitative scoring module is used for recalculating the comprehensive score of the employee after the employee arrives at post and examining the new employee so as to be beneficial to accurately judging whether the current interest literacy of the new employee is suitable for the current post, reasonable data is used as reference, and a relatively reasonable conclusion can be drawn on the interest literacy of the employee without a human resource department with abundant experience.
When the position of the employee changes and is adjusted, the quantitative scoring module of the employee is subjected to iterative optimization, so that the quantitative scoring module can make corresponding adjustment according to the position change of the employee, the comprehensive scoring effectiveness of the employee is improved, and the personnel resource department can check whether the employee is suitable for the current position.
The method for establishing the quantitative scoring model of the employee comprises the following steps: determining a scoring project according to the behavior category of the enterprise WeChat ecological circle; evaluating the scores of the employees in various project categories; and setting up the scoring weight of each item type, and summing the products of the scores of the item types and the corresponding scoring weights. Therefore, the comprehensive score of the employee is obtained by establishing terms and the value of the employee's interest literacy in each project type, setting the scoring weight of the project, multiplying the value of the employee's interest literacy in each project type by the corresponding scoring weight, and summing. In one embodiment of the invention, the scoring items in the quantitative scoring model, the scoring value of the assessment staff in each item category and the scoring weight of each item are set by a person experienced by the human resources department. Specifically, the quantitative scoring model may be, where y is the composite score, kn (n =1, 2, … … n) is the scoring weight of the project, and xn (n =1, 2, … … n) is the score of the employee's interest literacy in each project category.
It is worth to be noted that the enterprise wechat ecosystem behavior category includes an active participation behavior and a passive participation behavior, and in the scoring weight of the quantitative scoring model, the scoring weight of the active participation behavior is greater than that of the passive participation behavior. Specifically, in the embodiment of the invention, the enterprise WeChat ecological circle behavior categories comprise activity voting, training courses, examinations, questionnaires, creative applications, book reading sharing, optimization suggestions and walking activities. The activity voting, the training course and the questionnaire survey are generally arranged according to internal requirements, belong to passive participation behaviors, are hard tasks, and can feed back a small proportion of personal interest and literacy, so that the scoring weight of the project type corresponding to the passive participation behaviors can be reduced. The creative application, the book sharing, the optimization suggestion and the walking exercise activity are the active participation behaviors of the staff, the interest and the competence of the staff can be effectively reflected, the innovation ability of the staff is reflected if the creative adoption, the summary analysis ability of the staff is reflected if the realization of the suggestion, the learning ability of the staff is reflected if the examination score reflects the learning ability of the staff, the book sharing score reflects the idea that the reading preference of the staff is more outward than the sharing character, and the walking exercise activity score reflects the living health of the staff, so the scoring weight of the project types corresponding to the active participation behaviors can be increased. Therefore, in the scoring weight of the quantitative scoring model, the scoring weight of the active participation behavior is larger than that of the passive participation behavior, so that the comprehensive score calculated by the quantitative scoring model can effectively feed back the interest literacy of the employee.
In some embodiments, a method of optimizing a quantitative scoring model that adjusts literacy of interest comprises: analyzing professional skills and experience insights required by the continuous development of the employee on the current working post; and adding corresponding scoring items according to the analyzed professional skills and experience insights, and setting scores for the newly added scoring items. Specifically, if the current job position of the employee is a literary position, when optimizing the quantitative scoring model for adjusting the interest literacy, scoring items of the professional skill mastery of the employee, such as proficiency of an Excel form, typing speed, and the like, associated with the literary position, may be increased. Corresponding scoring items can be added according to the experience and the knowledge of the staff and the posts, for example, scoring items for the technicians to go out for competition are added, and books related to the posts are read. Therefore, the quantitative scoring model is optimized, so that the quantitative scoring model is more suitable for the current post of the employee, and the effective comprehensive scoring of the employee after the employee arrives at the post is calculated.
Preferably, the method for optimizing the quantitative scoring model for adjusting the literacy of interest further comprises: and analyzing the post characteristics of the employee at the current working post, and adjusting the scoring weight in the quantitative scoring model according to the post characteristics of the current working post. Specifically, the scoring weights in the quantitative scoring model are different between different job positions. If the promotion position has the outward position characteristic, the talent of the position needs the talent of the employee, so the scoring weight of the employee in the sharing aspect can be increased. For example, in a technical post, the post characteristics of the post belong to an inward type post, and the professional skill of the employee is required, so that the scoring weight of the employee in the aspect of learning examination can be appropriately increased. Therefore, by analyzing the current working position characteristic of the employee and then correspondingly adjusting the scoring weight of the quantitative scoring model of the employee according to the position characteristic, the quantitative scoring model is more suitable for the current position of the employee, and the effective comprehensive scoring of the employee after the employee arrives at the position is calculated.
Example two:
staff's action analytic system based on staff service system includes: the system comprises a first acquisition module, a first sorting and analyzing module, a first quantitative scoring module, a post arrangement module, a second acquisition module, a first sorting and analyzing module, a quantitative scoring model optimizing module, a second quantitative scoring module and an iteration module; the first acquisition module is used for acquiring enterprise WeChat ecosphere behavior data of new employees during training; the first sorting and analyzing module is used for sorting and analyzing the enterprise WeChat ecosphere behavior data to obtain the interest literacy of the employee; the first quantitative scoring module is used for establishing a quantitative scoring model of the employee, quantitatively scoring the interest literacy of the employee and calculating to obtain a comprehensive score of the employee; the post arrangement module is used for judging whether the employee is qualified for the working post or not by utilizing the comprehensive score according to the working post matched with the employee's interest and literacy; if yes, the employee is arranged to the work post; if not, matching the next working post until the employee is arranged to the competent working post; the second acquisition module is used for tracking and acquiring enterprise wechat ecosphere behavior data after the employee arrives at post; the first sorting and analyzing module is used for sorting and analyzing the enterprise WeChat ecological circle behavior data of the employee after the employee arrives at post to obtain the interest literacy of the employee after the employee arrives at post; the quantitative scoring model optimizing module is used for optimizing and adjusting a quantitative scoring model and increasing the relevance between the quantitative scoring model of the employee and the employee at the current working post; the second quantitative scoring module is used for quantitatively scoring the interest literacy of the employee after the employee arrives at post according to the optimized and adjusted quantitative scoring model, and recalculating to obtain the comprehensive score of the employee; and the iteration module is used for calculating to obtain the comprehensive score of the employee after the employee arrives at post by utilizing the second acquisition module, the first sorting and analyzing module, the quantitative scoring model optimizing module and the second quantitative scoring module when the post of the employee is adjusted.
Therefore, when a new employee is in practice, the interest and literacy of the employee are obtained according to the behavior data of the enterprise WeChat ecosystem of the new employee, and therefore the character, interest and occupation literacy of the employee are obtained, and the employee can be arranged in a matched post conveniently. If the personal characters of new employees who like to share are biased outwards, the method is suitable for the post of promotion or reception; if new employees with more originality are available, the system is suitable for planning or activity-planning posts, the new employees can be assisted to find a development direction suitable for the new employees as soon as possible, and the management efficiency and the cultivation effect of the new employees are improved. It should be noted that although the adaptive work post is matched according to the interest and literacy of the new employee, the comprehensive scoring is still needed to quantify and determine whether the employee is qualified for the post, so as to avoid the situation that the post arranges the new employee with insufficient capability to influence the operation of the work project and ensure the benefit of the enterprise.
And after the new employee arrives at the post, continuously tracking the interest literacy of the employee after arriving at the post, and finishing and analyzing the enterprise WeChat ecological cycle behavior data of the employee after arriving at the post again to obtain the interest literacy of the employee after arriving at the post. Therefore, the change of the interest literacy of the employee during the practice period and after the employee arrives at post can be perceived in time, the false impression brought by the subjective factors of the employee during the practice period can be recognized, and the staff can be deeply known by a human resource department.
The relevance of the quantitative scoring module of the staff and the current work post of the staff is increased by optimizing and adjusting the quantitative scoring model, so that the quantitative scoring model of the staff is more fit with the current work post, and the accuracy of comprehensive scoring after the staff arrives at the post is improved. And then, the optimized and adjusted quantitative scoring module is used for calculating the comprehensive score of the employee after the employee arrives at the post again, and the new employee is assessed, so that whether the current interest literacy of the new employee is suitable for the current post or not can be accurately judged, reasonable data is used as reference, and a reasonable conclusion can be drawn on the interest literacy of the employee without a human resource department with abundant experience.
When the position of the employee changes and is adjusted, the quantitative scoring module of the employee is subjected to iterative optimization, the quantitative scoring module can be adjusted correspondingly according to the position change of the employee, so that the comprehensive scoring effectiveness of the employee is improved, and the human resource department can check whether the employee is suitable for the current position.
Specifically, the first quantitative scoring module comprises: the scoring item determining module is used for determining scoring items according to the behavior types of the enterprise WeChat ecosphere; the score determining module is used for setting scores of all the project types according to the rating project types; the weight setting module is used for setting scoring weights of various project types; and the comprehensive scoring calculation module is used for summing the products of the scores of the various project types and the corresponding scoring weights. Therefore, the value of the employee's interest literacy in each project type is determined by the scoring project determination module, the value of the employee's interest literacy in each project type is determined by the value determination module, the scoring weight of the project is set by the weight setting module, and finally the value of the employee's interest literacy in each project type is multiplied by the corresponding scoring weight by the comprehensive scoring calculation module and summed, so that the comprehensive score of the employee is obtained. In one embodiment of the invention, the quantitative scoring model may be where y is the composite score, kn (n =1, 2, … … n) is the scoring weight for the project, and xn (n =1, 2, … … n) is the score for the employee's interest literacy in each project category.
It is worth to be noted that the quantitative scoring model optimization module comprises a post analysis module for analyzing professional skills and experience insights required by the continuous development of the employee at the current working post; the scoring item determining module is also used for adding corresponding scoring items according to the analyzed professional skills and experience insights; the score determining module is also used for setting scores for the scoring items newly added by the scoring item determining module. Specifically, in this embodiment, the score weight of the newly added item is set to 1 by default, the post analysis module is first used to analyze the current post of the employee, if the current working post of the employee is a position of literary work, when the quantitative score model of interest literacy is optimized and adjusted, the score item determination module is used to increase the score items of the professional skill mastery degree of the employee, such as the proficiency degree of an Excel form, the typing speed, and other professional skill mastery degrees associated with the position of literary work, and then the score determination module is used to set a score for the newly added score item. The scoring item determining module is also used for adding corresponding scoring items according to the experience insights of the staff related to the posts, for example, adding scoring items for the technicians to go out for competition, reading books related to the posts and the like, and setting scores for the added scoring items through the scoring item determining module. Therefore, the quantitative scoring model is optimized, so that the quantitative scoring model is more suitable for the current post of the employee, and the effective comprehensive scoring of the employee after the employee arrives at the post is calculated.
Preferably, the post analysis module is further configured to analyze the post characteristics of the employee at the current working post, and the weight setting module is further configured to adjust the scoring weight in the quantitative scoring model according to the post characteristics of the employee at the current working post. Specifically, the scoring weights in the quantitative scoring model are different between different job positions. If the promotion post is in an outward post, the post characteristic of the post needs the talent of the employee, so the scoring weight of the employee in the sharing aspect can be increased through the weight setting module. For example, the technical post, the post characteristic of which belongs to the inward type post, needs the professional skill of the employee, so the scoring weight of the employee in the aspect of learning examination can be appropriately increased through the weight establishing module. Therefore, by analyzing the current working position characteristic of the employee and then correspondingly adjusting the scoring weight of the quantitative scoring model of the employee according to the position characteristic, the quantitative scoring model is more suitable for the current position of the employee, and the effective comprehensive scoring of the employee after the employee arrives at the position is calculated.
EXAMPLE III
A computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the employee behavior analysis method based on the employee service system in the first embodiment.
Other configurations and operations of the employee behavior analysis method and system based on the employee service system according to the embodiment of the present invention are known to those skilled in the art and will not be described in detail herein.
In the description herein, references to the description of the terms "embodiment," "example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (8)
1. The employee behavior analysis method based on the employee service system is characterized by comprising the following steps:
step S1: acquiring enterprise WeChat ecosphere behavior data of new employees in a practice period;
step S2: the enterprise WeChat ecological cycle behavior data is sorted and analyzed, and the interest literacy of the employee is obtained;
and step S3: the method for establishing the quantitative scoring model of the employee comprises the following steps:
determining a scoring project according to the behavior category of the enterprise WeChat ecological circle;
evaluating scores of the employees in various project categories;
setting up the scoring weight of each item type, and summing the products of the scores of the item types and the corresponding scoring weights;
the enterprise WeChat ecosphere behavior types comprise active participation behaviors and passive participation behaviors, and in the scoring weight of the quantitative scoring model, the scoring weight of the active participation behaviors is larger than that of the passive participation behaviors;
in the quantitative scoring model, y is a comprehensive score, kn (n =1, 2, … … n) is a scoring weight of a project, xn (n =1, 2, … … n) is a score of each project type of the employee's interest literacy, the employee's interest literacy is quantitatively scored, and the comprehensive score of the employee is calculated;
and step S4: according to the employee's interest and literacy matching with the appropriate work post, judging whether the employee is qualified for the work post by using the comprehensive score; if yes, arranging the employee to the work post; if not, matching the next working post until the employee is arranged to the competent working post;
step S5: tracking and acquiring the enterprise wechat ecosystem behavior data after the employee arrives at post, and sorting and analyzing the enterprise wechat ecosystem behavior data after the employee arrives at post again to obtain the interest literacy of the employee after arrives at post;
step S6: optimizing and adjusting the quantitative scoring model, and increasing the relevance of the quantitative scoring model of the employee and the employee on the current working post;
step S7: quantitatively scoring the interest literacy of the employee after the employee arrives at post according to the optimized and adjusted quantitative scoring model, and recalculating to obtain a comprehensive score of the employee after the employee arrives at post;
step S8: and when the staff position is adjusted, returning to the step S5.
2. The employee behavior analysis method based on employee service system according to claim 1, wherein the method of optimizing the quantitative scoring model for adjusting the literacy of interest comprises:
analyzing professional skills and experience insights required by the continuous development of the employee on the current working post;
and according to the analyzed professional skill and experience, adding corresponding scoring items and setting scores for the newly added scoring items.
3. The employee behavior analysis method based on employee service system according to claim 2, wherein the method of optimizing the quantitative scoring model for adjusting the literacy of interest further comprises: and analyzing the post characteristics of the employee at the current working post, and adjusting the scoring weight in the quantitative scoring model according to the post characteristics of the current working post.
4. Staff's action analytic system based on staff service system, its characterized in that includes: the system comprises a first acquisition module, a first sorting and analyzing module, a first quantitative scoring module, a post arrangement module, a second acquisition module, a quantitative scoring model optimization module, a second quantitative scoring module and an iteration module;
the first acquisition module is used for acquiring enterprise WeChat ecosphere behavior data of new employees during training;
the first sorting and analyzing module is used for sorting and analyzing the enterprise WeChat ecological cycle behavior data to obtain the interest literacy of the employee;
the first quantitative scoring module is used for establishing a quantitative scoring model of the employee, quantitatively scoring the interest literacy of the employee and calculating to obtain a comprehensive score of the employee;
the post arrangement module is used for matching the adaptive working post according to the employee's interest and literacy and judging whether the employee is qualified for the working post or not by utilizing the comprehensive score; if yes, arranging the employee to the work post; if not, matching the next working post until the employee is arranged to the competent working post;
the second acquisition module is used for tracking and acquiring enterprise wechat ecosphere behavior data after the employee arrives at post;
the first sorting and analyzing module is used for sorting and analyzing the enterprise WeChat ecological circle behavior data of the employee after the employee arrives at post to obtain the interest literacy of the employee after the employee arrives at post;
the quantitative scoring model optimizing module is used for optimizing and adjusting a quantitative scoring model and increasing the relevance between the quantitative scoring model of the employee and the employee at the current working post;
the second quantitative scoring module is used for quantitatively scoring the interest literacy of the employee after the employee arrives at post according to the optimized and adjusted quantitative scoring model, and recalculating to obtain the comprehensive score of the employee;
and the iteration module is used for calculating to obtain the comprehensive score of the employee after the employee arrives at post by utilizing the second acquisition module, the first sorting and analyzing module, the quantitative scoring model optimizing module and the second quantitative scoring module when the post of the employee is adjusted.
5. The employee behavior analysis system based on an employee service system according to claim 4, wherein: the first quantitative scoring module includes:
the scoring item determining module is used for determining scoring items according to the behavior types of the enterprise WeChat ecosphere;
the score determining module is used for setting scores of various project types according to the scoring project types;
the weight setting module is used for setting scoring weights of various project types;
and the comprehensive scoring calculation module is used for summing the products of the scores of the various project types and the corresponding scoring weights.
6. The employee behavior analysis system based on an employee service system according to claim 5, wherein: the quantitative scoring model optimization module comprises a post analysis module used for analyzing professional skills and experience insights required by the continuous development of the employee at the current working post;
the scoring item determining module is also used for adding corresponding scoring items according to the analyzed professional skills and experience insights;
the score determining module is further used for setting scores for the scoring items newly added by the scoring item determining module.
7. The employee behavior analysis system based on an employee service system of claim 6, wherein: the post analysis module is also used for analyzing the post characteristics of the employee at the current working post, and the weight setting module is also used for adjusting the scoring weight in the quantitative scoring model according to the post characteristics of the employee at the current working post.
8. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the employee behavior analysis method based on an employee service system according to any one of claims 1 to 3.
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