CN114676316A - Method and system for constructing enterprise employee portrait based on big data - Google Patents
Method and system for constructing enterprise employee portrait based on big data Download PDFInfo
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- CN114676316A CN114676316A CN202210142219.XA CN202210142219A CN114676316A CN 114676316 A CN114676316 A CN 114676316A CN 202210142219 A CN202210142219 A CN 202210142219A CN 114676316 A CN114676316 A CN 114676316A
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Abstract
The application discloses a method and a system for constructing an enterprise employee portrait based on big data, wherein the method comprises the following steps: acquiring personnel information of personnel to be evaluated; the personnel information of the personnel to be evaluated is matched with a plurality of pre-configured personnel templates, wherein each personnel template is obtained in advance through the personnel information of the personnel qualified for the post; acquiring a first post corresponding to the personnel template with the highest matching degree with the personnel to be evaluated; acquiring a second post which is historically used or currently used by the person to be evaluated; and judging whether the person to be evaluated is suitable for the second post or not according to whether the first post and the second post are the same or not. The problem that whether the person is suitable for one post in the prior art and is caused by subjective assessment of the manager is solved, so that the adaptability of the person and the post can be assessed, and data support and guarantee are provided for the decision of the manager.
Description
Technical Field
The application relates to the field of data processing, in particular to a method and a system for constructing an enterprise employee portrait based on big data.
Background
In recent years, the information development of various industries is rapid, and management information system records about people, property and things gradually form massive data. Mining application based on mass data has become development power for driving enterprise innovation. Enterprises will face a new change of thinking and management. Particularly in enterprise management, the influence of big data is shown from the aspects of enterprise strategy formulation and execution to evaluation, and human resource management is used as the core content of enterprise management and inevitably meets new opportunities and challenges. The application of the deep mining big data technology in the management of human resources promotes the innovation of enterprises by using the advantages of big data, and the establishment of digital talents, the optimization of talents, the development of employee inventory of organizations and the like become important requirements in enterprise management. The method creates digital talent management, remodels the productivity of staff and makes management decision more intelligent.
Data surrounding people are finely managed, deep basic data mining, collaborative business information presentation and scientific internal management optimization are achieved, accordingly, the working efficiency of human resource management is improved, the service quality of human resource departments is improved, the traditional personnel management and basic human resource management are changed into the mode that the human resources are used, managed, controlled, monitored, maintained and developed under the overall strategic framework of an enterprise, and therefore the collaborative value is created, and strategic human resource management of the strategic target of the enterprise is achieved. Through introducing intelligent instrument solution management pain point and itch point, build enterprise's staff portrait system, assemble and present each dimension information of staff, record analysis staff career overall process, the talent skill granulation reaches the talent label simultaneously, provides talent search and intelligent recommendation. Talent management activities such as promotion, talent inventory, talent recommendation, succession management and the like are systematized, all talent management data are linked, daily data monitoring and analysis are achieved, intelligent talent prediction and analysis are further achieved, and objective talent decision basis is provided for managers.
At present, whether a person is suitable for a post or not is subjectively evaluated mainly by depending on experience, academic calendar and the like of the person, and the subjective evaluation mode has the problems of being not objective enough, poor in accuracy and the like in evaluation of the person, so that whether the person to be evaluated is suitable for the post or not is not scientifically judged.
Disclosure of Invention
The embodiment of the application provides a method and a system for constructing an enterprise employee portrait based on big data, and the method and the system at least solve the problem that whether personnel are suitable for one post or not depends on subjective evaluation of managers in the prior art.
According to one aspect of the application, a method for constructing an enterprise employee representation based on big data is provided, and comprises the following steps: acquiring personnel information of a person to be evaluated, wherein the personnel information comprises basic information and capability information of the person, the basic information is used for indicating the attribute of the person to be evaluated, and the capability information is used for indicating the historical working condition of the person to be evaluated; the personnel information of the personnel to be evaluated is adapted to a plurality of pre-configured personnel templates, wherein each personnel template corresponds to one post, and each personnel template is obtained in advance through the personnel information of the personnel qualified for the post; acquiring a first post corresponding to the personnel template with the highest matching degree with the personnel to be evaluated; acquiring a second post which is used by the person to be evaluated historically or currently; and judging whether the person to be evaluated is suitable for the second post according to whether the first post and the second post are the same.
Further, before adapting the personnel information of the personnel to be evaluated to a plurality of pre-configured personnel templates, the method further comprises: acquiring personnel information of a preset person and a preset position corresponding to the preset person, wherein the preset person is a person suitable for the preset position selected from a plurality of working personnel engaged in the preset position; and generating a personnel template corresponding to the predetermined post according to the personnel information of the plurality of predetermined personnel.
Further, adapting the personnel information of the personnel to be evaluated to a plurality of pre-configured personnel templates comprises: training by using a plurality of groups of training data to obtain a machine learning model, wherein each group of training data in the plurality of groups of training data comprises: personnel information of a person and a personnel template to which the personnel information is adapted; inputting the personnel information of the personnel to be evaluated into the machine learning model; and taking the personnel template which is output by the machine learning model and corresponds to the personnel information of the personnel to be evaluated as the personnel template with the highest matching degree with the personnel to be evaluated.
Further, training a machine learning model using the plurality of sets of training data comprises: dividing the plurality of sets of training data into first data for training and second data for verification; training by using the first data pair to obtain the machine learning model; and verifying the machine learning model by using the second data, and determining that the machine learning model is successfully trained if the verification is passed.
Further, judging whether the person to be evaluated is suitable for the second post according to whether the first post and the second post are the same comprises: and under the condition that the first post is different from the second post, determining that the person to be evaluated is not suitable for the second post, and sending the person information of the first post and the person to be evaluated to a manager.
According to another aspect of the present application, there is also provided a system for constructing an enterprise employee representation based on big data, comprising: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring personnel information of a person to be evaluated, the personnel information comprises basic information and capability information of the person, the basic information is used for indicating the attribute of the person to be evaluated, and the capability information is used for indicating the historical working condition of the person to be evaluated; the matching module is used for matching the personnel information of the personnel to be evaluated with a plurality of pre-configured personnel templates, wherein each personnel template corresponds to one post, and each personnel template is obtained in advance through the personnel information of the personnel qualified for the post; the second acquisition module is used for acquiring a first post corresponding to the personnel template with the highest matching degree with the personnel to be evaluated; the third acquisition module is used for acquiring a second post which is used by the person to be evaluated historically or is currently used; and the judging module is used for judging whether the person to be evaluated is suitable for the second post according to whether the first post and the second post are the same.
Further, the method also comprises the following steps: the generating module is used for acquiring personnel information of preset personnel and a preset post corresponding to the preset personnel, wherein the preset personnel are personnel which are selected from a plurality of working personnel engaged in the preset post and are suitable for the preset post; and generating a personnel template corresponding to the predetermined post according to the personnel information of the plurality of predetermined personnel.
Further, the matching module is configured to: training by using a plurality of groups of training data to obtain a machine learning model, wherein each group of training data in the plurality of groups of training data comprises: personnel information of a person and a personnel template adapted to the personnel information; inputting the personnel information of the personnel to be evaluated into the machine learning model; and taking the personnel template which is output by the machine learning model and corresponds to the personnel information of the personnel to be evaluated as the personnel template with the highest matching degree with the personnel to be evaluated.
Further, the matching module is configured to: dividing the plurality of sets of training data into first data for training and second data for verification; training by using the first data pair to obtain the machine learning model; and verifying the machine learning model by using the second data, and determining that the machine learning model is successfully trained if the verification is passed.
Further, the determining module is configured to: and under the condition that the first post is different from the second post, determining that the person to be evaluated is not suitable for the second post, and sending the person information of the first post and the person to be evaluated to a manager.
In the embodiment of the application, the personnel information of the personnel to be evaluated is obtained, wherein the personnel information comprises basic information and capability information of the personnel, the basic information is used for indicating the attribute of the personnel to be evaluated, and the capability information is used for indicating the historical working condition of the personnel to be evaluated; the personnel information of the personnel to be evaluated is adapted to a plurality of pre-configured personnel templates, wherein each personnel template corresponds to one post, and each personnel template is obtained in advance through the personnel information of the personnel qualified for the post; acquiring a first post corresponding to the personnel template with the highest matching degree with the personnel to be evaluated; acquiring a second post which is used by the person to be evaluated historically or currently; and judging whether the person to be evaluated is suitable for the second post according to whether the first post and the second post are the same. The problem that whether the personnel are suitable for one post or not depends on subjective evaluation of the manager in the prior art is solved through the method and the device, so that the adaptability of the personnel and the post can be evaluated, and data support and guarantee are provided for the personnel decision of the manager.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application, and the description of the exemplary embodiments of the application are intended to be illustrative of the application and are not intended to limit the application. In the drawings:
FIG. 1 is a flow diagram of a method for building a representation of an enterprise employee based on big data, according to an embodiment of the application.
Detailed Description
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
In the present embodiment, a method for constructing a representation of an enterprise employee based on big data is provided, and fig. 1 is a flowchart of a method for constructing a representation of an enterprise employee based on big data according to an embodiment of the present application, and steps included in the method are described below as shown in fig. 1.
Step S102, obtaining personnel information of a person to be evaluated, wherein the personnel information comprises basic information and capability information of the person, the basic information is used for indicating the attribute of the person to be evaluated, and the capability information is used for indicating the historical working condition of the person to be evaluated;
step S104, adapting the personnel information of the personnel to be evaluated with a plurality of pre-configured personnel templates, wherein each personnel template corresponds to a post and is obtained in advance through the personnel information of the personnel qualified for the post;
step S106, acquiring a first post corresponding to the personnel template with the highest matching degree with the personnel to be evaluated;
step S108, acquiring a second post which is historically used or currently used by the person to be evaluated;
as an optional implementation manner, acquiring a second position that the person to be evaluated has been taken in the history for recruiting the person to be evaluated, and determining that the person to be evaluated is suitable for the second position and a manager sends a first prompt message when the second position that the person to be evaluated has been taken in the history is the same as the first position, where the first prompt message is used to indicate that the person to be evaluated is recommended to be taken as the person in the second position; and under the condition that the second post is different from the first post, acquiring the matching degree of each item of data in the personnel information of the personnel to be evaluated and each item of data in a personnel template corresponding to the first post, wherein the matching degree is represented by percentage, obtaining the matching percentage of the personnel to be evaluated and the personnel template of the first post according to the matching degree of each item of data of the personnel to be evaluated, and sending second prompt information under the condition that the percentage exceeds a preset percentage, wherein the second prompt information is used for indicating to recommend the personnel to be evaluated to be hired as the personnel of the first post.
And S110, judging whether the person to be evaluated is suitable for the second post according to whether the first post and the second post are the same.
As another optional implementation manner, in a case where the person to be evaluated is hired to the second position, after a predetermined time period (for example, a transition time period) elapses, acquiring the person information of the person to be evaluated, where the capability information of the person information is the capability information embodied in the predetermined time period, determining again a first position corresponding to a person template that matches the person to be evaluated to the highest extent according to the newly acquired person information of the person to be evaluated, and determining to transition the person to be evaluated if the first position is the same as a second position where the person to be evaluated is hired.
In this step, it may be further determined that the person to be evaluated is not suitable for the second post when the first post and the second post are different, and the person information of the first post and the person to be evaluated may be sent to a manager.
Through the steps, the problem that whether the person is suitable for one post in the prior art is solved by means of subjective evaluation of the manager, so that the adaptability between the person and the post can be evaluated, and data support and guarantee are provided for the decision of the manager.
In this embodiment, before adapting the staff information of the staff to be evaluated to a plurality of preset staff templates, the method further includes: acquiring personnel information of a preset person and a preset position corresponding to the preset person, wherein the preset person is a person suitable for the preset position selected from a plurality of working personnel engaged in the preset position; and generating a personnel template corresponding to the predetermined post according to the personnel information of the plurality of predetermined personnel.
As an optional implementation manner, performance indicator assessment results of all employees at one post may be obtained, employees whose performance indicator assessment results exceed a preconfigured range are used as the predetermined staff, whether the number of the predetermined staff is greater than or equal to a preconfigured threshold is judged, and in the case that the number of the predetermined staff is less than the preconfigured threshold, the preconfigured range is numerically adjusted until the number of the predetermined staff is greater than or equal to the preconfigured threshold, staff information of all the predetermined staff is obtained, and the staff information is counted to obtain a staff template.
And if each item in all the items is a numerical value, the lower limit of the numerical range of the item of the personnel template is made according to the worst numerical value of the item of all the preset personnel, the optimal numerical value is used as the upper limit of the data range of the item of the personnel template, and all the items are traversed to obtain the personnel template.
The embodiment may use a machine learning manner to perform adaptation, for example, adapting the person information of the person to be evaluated to a plurality of pre-configured person templates includes: training by using a plurality of groups of training data to obtain a machine learning model, wherein each group of training data in the plurality of groups of training data comprises: personnel information of a person and a personnel template to which the personnel information is adapted; inputting the personnel information of the personnel to be evaluated into the machine learning model; and taking the personnel template which is output by the machine learning model and corresponds to the personnel information of the personnel to be evaluated as the personnel template with the highest matching degree with the personnel to be evaluated.
A machine learning model trained using the plurality of sets of training data comprises: dividing the plurality of sets of training data into first data for training and second data for verification; training by using the first data pair to obtain the machine learning model; and verifying the machine learning model by using the second data, and determining that the machine learning model is successfully trained if the verification is passed.
As an alternative embodiment, a machine learning model trained by using the plurality of sets of training data includes: under the condition that the machine learning model is not verified by using the second data, increasing the quantity of the first data for training and reducing the quantity of the second data for verification; training with the adjusted first data and verifying with the adjusted second data until the verification is passed.
This is described below in connection with an alternative embodiment. In the embodiment, the data model is based on international common information model CIM standard, combines with the specific application of human resource business, adopts the modes of reference, inheritance and combination to establish a unified employee portrait data model of the whole company, meets the enterprise employee portrait business requirements and intensive management requirements, forms a unified data standard, and accordingly realizes standardization, normalization and transparent sharing of data of enterprise employee portraits.
The data architecture of the enterprise employee representation system can be divided into the following parts according to the theme: the system comprises a staff information theme, a study calendar theme, a training experience theme, a work record theme, a staff assessment theme, a punishment condition theme, a thesis condition theme, a patent condition theme, a post information theme, a staff comprehensive score theme and a post recommendation theme. And the data models are classified and summarized according to the topics, so that the model management is convenient.
2. Intelligent recommendation
The portrait model is established for each employee, the specific fields in the employee information are converted into digital fields, and the data are stored in the employee information base. Meanwhile, a deep learning method is used for training the post classification model to obtain the staff corresponding to the labels, so that the efficiency of the whole recommendation function is greatly improved.
3. Post recommendation model
(1) The work experience mainly comprises the work experience of related posts, the experience of work units, related experience, inspection experience and performance assessment. The accumulation of the current working services has a large influence on the competence of the subsequent posts, and the occupation ratio is high.
(2) The job performance comprises the situation of graduation colleges, cultural degree, occupation qualification and warranty of the whole day, the predictability of the job performance on the competence of future posts is general, and particularly relates to a cross-business line, so the setting proportion is not high.
(3) The personal reputation refers to the winning of the prize from work, and the score obtained varies according to the winning degree.
(4) Adding the subentry, evaluating the personnel ability mainly measures the bottom layer ability quality and personality, belongs to the condition required by all posts, can better predict the performance of the future posts, therefore, the weight setting is relatively higher, and the guiding ability for guiding enterprises to use people is also emphasized
In the embodiment, an enterprise human resource information system is used as a talent management database, comprehensive information of the full-time business life cycle of the staff is associated based on big data mining analysis, the staff management, training management, staff cultivation, post management and other work are supported, intelligent talent prediction analysis is further realized, and objective talent decision basis is provided for managers.
The data of the enterprise human resource management information system is used as support, actual business management requirements are combined, and on the subway map developed by the employee profession, the employee can be enabled to make clear the development route and promotion standard of the employee, so that analysis support is provided for self promotion of the employee and talent culture organization.
The staff comprehensive quality evaluation model, the post selection scientific recommendation model, the post matching analysis model, the comprehensive ability evaluation model and the like of the research enterprise support the construction of staff career data analysis application. The functions of employee portrait, post recommendation, personnel recommendation, label management, post level management and the like are realized.
In this optional embodiment, the big data of the person to be evaluated may be divided into basic information and behavior log data (i.e., capability information), and the method for constructing the employee image based on the big data may include the following steps: collecting basic information and behavior log data of the staff. According to the invention, the basic information of the employee is the information filled in when the employee registers, and comprises the following steps: the employee's name, age, gender, job title, educational history, occupational history, and/or project experience. The behavior log data of the employee includes: personal trends, reading notes, work tracks (reward and punishment, travel, work reporting, leave asking, reimbursement and other recorded information), motion data, his assessment data, self assessment data and/or data obtained by a web crawler, such as news reports and speech materials of employees. And acquiring characteristic labels of the employees from the basic information and the behavior log data to form a personalized word cloud. In the invention, the staff is comprehensively cognized in multiple angles such as occupational literacy, professional ability, sexual hobbies, physical fitness and the like, a series of representative label words are extracted from the data information, and the labeling of the staff information is that the staff pictures. The core task of employee representation is to label employees for easy understanding by managers and for computer processing. And through massive information mining, the information overview of each employee in the enterprise is sketched. The characteristics of each employee are outlined by modeling the characteristics and behaviors of the employee, so that the aim of acquiring more valuable information from data is fulfilled. Valuable data are intercepted and extracted by determining reasonable granularity, and employee tags which can reflect employee characteristics and have analysis values are abstracted out. For example, wait for people and good, have unlimited future, do things and so on. And constructing the employee portrait based on the personalized word cloud. Specifically, in the step, according to analyzable text data, correlation analysis among employees is performed, relationships between entities are extracted or classified, and an employee correlation map is constructed by mining relationships such as superior and subordinate relationships, co-worker relationships, project group membership relationships, endorsement relationships and the like in the text.
Optionally, the basic information and the behavior log data of the employee are separated, cleaned, structured and associated for analysis. The effectiveness and the accuracy of the label can be effectively improved by cleaning the data, analyzing the relevance and the like. The employee representation further includes: behavioral characteristic radar chart and social relationship network chart. Specifically, according to analyzable text data, correlation analysis among employees is carried out, relationships among entities are extracted or classified, and an employee social relationship network graph is constructed by mining upper and lower level relationships, co-worker relationships, project group membership relationships and signing relationships in the text.
Optionally, the other evaluation data may be obtained by: introducing a test question bank conforming to the culture background of an enterprise, and generating text data through evaluation of the test question bank, wherein the text data comprises: data of five dimensions of occupational literacy, professional ability, sexual hobbies, physical fitness and character characteristics; the self-evaluation data can be obtained by the following method: introducing a test question bank conforming to the culture background of an enterprise, and generating text data through self-evaluation, wherein the text data comprises: occupational literacy, professional ability, sexual hobbies, physical fitness, and personality traits.
Anonymous his rating based on blockchain: and creating an anonymous account on the block chain, and storing the employee interaction evaluation information based on the block chain mutual evaluation. The invention makes anonymous his rating based on the block chain. Specifically, an anonymous account is created on the blockchain, mutual evaluation is carried out based on the blockchain, and staff interaction evaluation information is stored. The mutual evaluation based on the block chain mainly comprises the following steps: the method comprises the steps of mutual evaluation service interaction scene design, synchronous working mechanism design of a block chain system and an information system, coupling relation design of a block chain account and an information system account, information storage content planning, information verification and audit and other intelligent contract design. Therefore, other users can acquire the anonymous interaction evaluation information of the user from the blockchain network, so that the users can be known more comprehensively, meanwhile, managers can also have comprehensive cognition on the users, the evaluation is guaranteed to be real and reliable and cannot be falsified, and the seriousness of the interaction evaluation is improved. And a block chain technology is introduced to construct interaction evaluation information, so that the authenticity of interaction evaluation is met. In addition, according to the information of the anonymous evaluation, the positive, negative or neutral emotion of the evaluation information is mined, and emotion analysis of the anonymous evaluation is realized.
In this embodiment, an electronic device is provided, comprising a memory in which a computer program is stored and a processor configured to run the computer program to perform the method in the above embodiments.
The programs described above may be run on a processor or may also be stored in memory (or referred to as computer-readable media), which includes both non-transitory and non-transitory, removable and non-removable media, that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks, and corresponding steps may be implemented by different modules.
Such an apparatus or system is provided in this embodiment. The system is called a system for constructing an enterprise employee representation based on big data, and comprises the following steps: the system comprises a first acquisition module, a first evaluation module and a second evaluation module, wherein the first acquisition module is used for acquiring personnel information of personnel to be evaluated, the personnel information comprises basic information and capability information of the personnel, the basic information is used for indicating attributes of the personnel to be evaluated, and the capability information is used for indicating historical working conditions of the personnel to be evaluated; the matching module is used for matching the personnel information of the personnel to be evaluated with a plurality of pre-configured personnel templates, wherein each personnel template corresponds to one post, and each personnel template is obtained in advance through the personnel information of the personnel qualified for the post; the second acquisition module is used for acquiring a first post corresponding to the personnel template with the highest matching degree with the personnel to be evaluated; the third acquisition module is used for acquiring a second post which is used by the person to be evaluated historically or is currently used; and the judging module is used for judging whether the person to be evaluated is suitable for the second post according to whether the first post and the second post are the same.
The system or the apparatus is used for implementing the functions of the method in the foregoing embodiments, and each module in the system or the apparatus corresponds to each step in the method, which has been described in the method and is not described herein again.
For example, it also includes: the system comprises a generating module, a judging module and a judging module, wherein the generating module is used for acquiring personnel information of a preset person and a preset post corresponding to the preset person, and the preset person is a person which is selected from a plurality of working personnel engaged in the preset post and is suitable for the preset post; and generating a personnel template corresponding to the predetermined post according to the personnel information of the plurality of predetermined personnel.
For another example, the matching module is configured to: training by using a plurality of groups of training data to obtain a machine learning model, wherein each group of training data in the plurality of groups of training data comprises: personnel information of a person and a personnel template to which the personnel information is adapted; inputting the personnel information of the personnel to be evaluated into the machine learning model; and taking the personnel template which is output by the machine learning model and corresponds to the personnel information of the personnel to be evaluated as the personnel template with the highest matching degree with the personnel to be evaluated. Optionally, the matching module is configured to: dividing the plurality of sets of training data into first data for training and second data for verification; training by using the first data pair to obtain the machine learning model; and verifying the machine learning model by using the second data, and determining that the machine learning model is successfully trained if the verification is passed.
For another example, the determining module is configured to: and under the condition that the first post is different from the second post, determining that the person to be evaluated is not suitable for the second post, and sending the person information of the first post and the person to be evaluated to a manager.
Optionally, the matching module is configured to: under the condition that the machine learning model is not verified by using the second data, increasing the quantity of the first data for training and reducing the quantity of the second data for verification; training using the adjusted first data and verifying using the adjusted second data until verification is passed.
The embodiment solves the problem that whether the personnel is suitable for one post in the prior art is judged by the subjective evaluation of the manager, so that the adaptability between the personnel and the post can be evaluated, and data support and guarantee are provided for the personnel decision of the manager.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for constructing enterprise employee images based on big data is characterized by comprising the following steps:
acquiring personnel information of a person to be evaluated, wherein the personnel information comprises basic information and capability information of the person, the basic information is used for indicating the attribute of the person to be evaluated, and the capability information is used for indicating the historical working condition of the person to be evaluated;
the personnel information of the personnel to be evaluated is adapted to a plurality of pre-configured personnel templates, wherein each personnel template corresponds to one post, and each personnel template is obtained in advance through the personnel information of the personnel qualified for the post;
acquiring a first post corresponding to the personnel template with the highest matching degree with the personnel to be evaluated;
acquiring a second post which is used by the person to be evaluated historically or currently;
and judging whether the person to be evaluated is suitable for the second post according to whether the first post and the second post are the same.
2. The method according to claim 1, wherein prior to adapting the person information of the person to be assessed to a plurality of pre-configured person templates, the method further comprises:
Acquiring personnel information of a preset person and a preset position corresponding to the preset person, wherein the preset person is a person suitable for the preset position selected from a plurality of working personnel engaged in the preset position;
and generating a personnel template corresponding to the predetermined post according to the personnel information of the plurality of predetermined personnel.
3. The method of claim 2, wherein adapting the person information of the person to be assessed to a plurality of pre-configured person templates comprises:
training by using a plurality of groups of training data to obtain a machine learning model, wherein each group of training data in the plurality of groups of training data comprises: personnel information of a person and a personnel template adapted to the personnel information;
inputting the personnel information of the personnel to be evaluated into the machine learning model;
and taking the personnel template which is output by the machine learning model and corresponds to the personnel information of the personnel to be evaluated as the personnel template with the highest matching degree with the personnel to be evaluated.
4. The method of claim 3, wherein training a machine learning model using the plurality of sets of training data comprises:
Dividing the plurality of sets of training data into first data for training and second data for verification;
training to obtain the machine learning model by using the first data pair;
and verifying the machine learning model by using the second data, and determining that the machine learning model is successfully trained if the verification is passed.
5. The method of any one of claims 1 to 4, wherein determining whether the person to be assessed fits in the second post based on whether the first post and the second post are the same comprises:
and under the condition that the first post is different from the second post, determining that the person to be evaluated is not suitable for the second post, and sending the person information of the first post and the person to be evaluated to a manager.
6. A system for enterprise employee portrayal based on big data construction is characterized by comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring personnel information of a person to be evaluated, the personnel information comprises basic information and capability information of the person, the basic information is used for indicating the attribute of the person to be evaluated, and the capability information is used for indicating the historical working condition of the person to be evaluated;
The matching module is used for matching the personnel information of the personnel to be evaluated with a plurality of pre-configured personnel templates, wherein each personnel template corresponds to one post, and each personnel template is obtained in advance through the personnel information of the personnel qualified for the post;
the second acquisition module is used for acquiring a first post corresponding to the personnel template with the highest matching degree with the personnel to be evaluated;
the third acquisition module is used for acquiring a second post which is used by the person to be evaluated historically or is currently used;
and the judging module is used for judging whether the person to be evaluated is suitable for the second post according to whether the first post and the second post are the same.
7. The system of claim 6, further comprising:
the system comprises a generating module, a judging module and a judging module, wherein the generating module is used for acquiring personnel information of a preset person and a preset post corresponding to the preset person, and the preset person is a person which is selected from a plurality of working personnel engaged in the preset post and is suitable for the preset post; and generating a personnel template corresponding to the predetermined post according to the personnel information of the plurality of predetermined personnel.
8. The system of claim 7, wherein the matching module is configured to:
training by using a plurality of groups of training data to obtain a machine learning model, wherein each group of training data in the plurality of groups of training data comprises: personnel information of a person and a personnel template to which the personnel information is adapted;
inputting the personnel information of the personnel to be evaluated into the machine learning model;
and taking the personnel template which is output by the machine learning model and corresponds to the personnel information of the personnel to be evaluated as the personnel template with the highest matching degree with the personnel to be evaluated.
9. The system of claim 8, wherein the matching module is configured to:
dividing the plurality of sets of training data into first data for training and second data for verification;
training by using the first data pair to obtain the machine learning model;
and verifying the machine learning model by using the second data, and determining that the machine learning model is successfully trained if the verification is passed.
10. The system according to any one of claims 6 to 9, wherein the determining module is configured to:
and under the condition that the first post is different from the second post, determining that the person to be evaluated is not suitable for the second post, and sending the person information of the first post and the person to be evaluated to a manager.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116029687A (en) * | 2023-03-30 | 2023-04-28 | 国投人力资源服务有限公司 | Intelligent talent selection evaluation analysis management system in enterprise |
| CN116485281A (en) * | 2023-06-16 | 2023-07-25 | 国网信息通信产业集团有限公司 | Employee portraying method and system based on longitudinal federal learning and knowledge graph |
| CN117131791A (en) * | 2023-10-27 | 2023-11-28 | 德特赛维技术有限公司 | Model evaluation method, system and storage medium based on big data platform |
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2022
- 2022-02-16 CN CN202210142219.XA patent/CN114676316A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116029687A (en) * | 2023-03-30 | 2023-04-28 | 国投人力资源服务有限公司 | Intelligent talent selection evaluation analysis management system in enterprise |
| CN116485281A (en) * | 2023-06-16 | 2023-07-25 | 国网信息通信产业集团有限公司 | Employee portraying method and system based on longitudinal federal learning and knowledge graph |
| CN117131791A (en) * | 2023-10-27 | 2023-11-28 | 德特赛维技术有限公司 | Model evaluation method, system and storage medium based on big data platform |
| CN117131791B (en) * | 2023-10-27 | 2024-01-23 | 德特赛维技术有限公司 | Model evaluation method, system and storage medium based on big data platform |
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