CN111985897A - Method and device for constructing occupational portrait data model by using talent big data - Google Patents
Method and device for constructing occupational portrait data model by using talent big data Download PDFInfo
- Publication number
- CN111985897A CN111985897A CN202010841000.XA CN202010841000A CN111985897A CN 111985897 A CN111985897 A CN 111985897A CN 202010841000 A CN202010841000 A CN 202010841000A CN 111985897 A CN111985897 A CN 111985897A
- Authority
- CN
- China
- Prior art keywords
- task
- data
- portrait
- key
- different
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000013499 data model Methods 0.000 title claims abstract description 27
- 238000009826 distribution Methods 0.000 claims abstract description 110
- 238000011160 research Methods 0.000 claims abstract description 81
- 230000001755 vocal effect Effects 0.000 claims abstract description 40
- 238000006243 chemical reaction Methods 0.000 claims abstract description 34
- 238000012545 processing Methods 0.000 claims abstract description 27
- 238000004458 analytical method Methods 0.000 claims abstract description 12
- 230000006399 behavior Effects 0.000 claims description 35
- 230000001149 cognitive effect Effects 0.000 claims description 18
- 238000000354 decomposition reaction Methods 0.000 claims description 18
- 238000004891 communication Methods 0.000 claims description 17
- 230000009471 action Effects 0.000 claims description 9
- 239000002131 composite material Substances 0.000 claims description 9
- 230000008451 emotion Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 5
- 230000000877 morphologic effect Effects 0.000 claims description 4
- 238000010835 comparative analysis Methods 0.000 claims description 3
- 238000013329 compounding Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 230000004044 response Effects 0.000 claims 2
- 238000005516 engineering process Methods 0.000 abstract description 9
- 238000013473 artificial intelligence Methods 0.000 abstract description 6
- 238000010801 machine learning Methods 0.000 abstract description 6
- 238000007726 management method Methods 0.000 description 19
- 238000010586 diagram Methods 0.000 description 9
- 238000011156 evaluation Methods 0.000 description 7
- 230000008520 organization Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000005336 cracking Methods 0.000 description 2
- 238000005034 decoration Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004451 qualitative analysis Methods 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 229920002558 Curdlan Polymers 0.000 description 1
- 239000001879 Curdlan Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- WQZGKKKJIJFFOK-VFUOTHLCSA-N beta-D-glucose Chemical compound OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-VFUOTHLCSA-N 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 229940078035 curdlan Drugs 0.000 description 1
- 235000019316 curdlan Nutrition 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/105—Human resources
- G06Q10/1053—Employment or hiring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06398—Performance of employee with respect to a job function
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Probability & Statistics with Applications (AREA)
- Quality & Reliability (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the field of vocational portrait, in particular to a method and a device for constructing a vocational portrait data model by using talent big data. The method and the device comprise the steps of processing a data research sample in different scenes, analyzing key tasks completed by a research object in different scenes, forming a key task portrait, then splitting to form a capability structure portrait, obtaining knowledge form distribution and body reaction distribution of the research object according to the capability structure portrait, and constructing a capability structure model according to the knowledge form distribution and the body reaction distribution. The operation difficulty of comparison and analysis is greatly reduced, the efficiency is greatly improved, artificial intelligence and machine learning can be partially realized in the future, intelligent comparison is realized by IT, and the method is similar to the current face recognition technology. The work operation difficulty of the manual operation part is greatly reduced, the manual operation part is not necessarily realized by talent assessment experts, an analyst with certain professional knowledge can complete the operation, and the accuracy and the efficiency are greatly improved.
Description
Technical Field
The invention relates to the field of vocational portrait, in particular to a method and a device for constructing a vocational portrait data model by using talent big data.
Background
When job seekers face a critical period of job selection or face more than two jobs/posts selection, most people do not clearly position what kind of work they are suitable for, so many experts test the characters of job seekers by adopting a certain evaluation method, but at present, the characters of job seekers are subjectively evaluated by the experts mainly through manual one-to-one detection. The expert judges the characters of the job seeker, the defects of subjectivity, randomness, limitation and the like exist, whether the judgment is correct or not is difficult to grasp, and in addition, the professional detects the characters one by one, so that the problems of high cost, low efficiency and the like exist.
In professional evaluation, career tendency, career interest and the like are often used for expressing stable personality tendency characteristics of individuals, which are called as "career personality", and the corresponding main theories are as follows: the hollander occupational psychology type, the lolo occupational psychology type, and the related famous tests are: SCII (streron-campbell interest questionnaire), curdlan career interest questionnaire, JVIS (jackson career interest scale), CAI (lifetime assessment scale), and the like. However, the evaluation methods on the market at present have own short boards, and cannot intuitively and accurately give the required evaluation results to an evaluator.
Disclosure of Invention
The embodiment of the invention provides a method and a device for constructing a vocational portrait data model by using talent big data, which at least solve the technical problem of low accuracy of the existing vocational assessment.
According to an embodiment of the present invention, a method for constructing a vocational portrait data model by using talent big data is provided, which includes the following steps:
the method comprises the following steps of taking the occupation data of a plurality of persons with excellent performance in the same occupation or the occupation data periodically recorded by the persons in different career stages in the same person as a data research sample;
performing scene-by-scene processing on a data research sample;
analyzing key tasks completed by research objects in the data research samples under various scenes, and performing task element decomposition on the key tasks;
analyzing each task element according to different dimensions and forming a key task portrait;
and splitting the key task portrait to form a capability structure portrait, acquiring knowledge form distribution and body reaction distribution of a research object according to the capability structure portrait, and constructing a capability structure model according to the knowledge form distribution and the body reaction distribution.
Further, the processing the data study sample into the part scene comprises:
different working scenes are listed according to different geographic positions of the data research samples, and people, objects, time and behaviors contacted by the research objects under different scenes are recorded.
Further, analyzing the key tasks completed by the research objects in the data research samples under each scene, and performing task element decomposition on the key tasks comprises:
analyzing key tasks completed by research objects in data research samples under various scenes, wherein the structure of data extracted in each specific scene is as follows: the method comprises the steps of scene-first-stage task-second-stage task-third-stage task, wherein the later-stage task is a lower-level decomposition task of the former-stage task until the task is decomposed into task elements which can be used as independent analysis factors.
Further, analyzing the task elements in different dimensions and forming a key task sketch comprises:
and counting the frequency of the single type of task elements under each dimension in the three-level task and the frequency of the task formed by compounding more than two types of task elements, naming and coding all the task elements, and forming a key task portrait according to all the named and coded task elements.
Further, the step of splitting the key task portrait to form a capability structure portrait, the step of obtaining knowledge form distribution and body reaction distribution of the research object according to the capability structure portrait, and the step of constructing a capability structure model according to the knowledge form distribution and the body reaction distribution comprises the following steps:
according to a data structure between task elements and task completion logic, splitting the completion process of each key task to form a capability structure portrait in a capability structure model;
and coding, classifying, summarizing and combining the knowledge, behavior and problem solving measure characteristics separated from each key task to form a capability structure distribution map and a capability structure distribution form of the research object, and constructing a capability structure model according to the capability structure distribution map and the capability structure distribution form.
Further, the morphology of the capability structure model includes: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; a multi-component composite type; the divided knowledge characteristics comprise general knowledge and post knowledge; the behavior characteristics of the split-out include: practice of actions, cognitive observation, thinking, communication, feeling of emotion; the characteristic of the measure for solving the problem of disassembly comprises the following steps: problems encountered and solutions.
Further, the method further comprises:
the data of different superior persons in the same post are compared and analyzed, the same points and different points of the key tasks and the capability structure distribution forms of the different superior persons are analyzed, the number of different times of occurrence of each key task is counted, and the probability and the frequency of occurrence of each key task are obtained;
processing knowledge capability structures of different excellent persons to obtain the number and frequency of different knowledge and different behaviors appearing in different excellent persons, obtaining the association probability of the knowledge, the behaviors and the careers, and further obtaining big data for updating and iterating the capability structure model;
the big data is used to perform an update iteration on the capability structure model.
Further, the method further comprises:
and generating a key task probability query table, and querying probability values of key task composition and multi-person association after corresponding according to task element codes in the query table.
According to another embodiment of the present invention, there is provided an apparatus for constructing a vocational portrait data model using talent big data, including:
the sample acquisition unit is used for taking the occupation data of a plurality of persons with excellent performance in the same occupation or the occupation data periodically recorded by the same person at different career stages as data research samples;
the scene processing unit is used for performing scene processing on the data research sample;
the task element decomposition unit is used for analyzing key tasks completed by research objects in the data research samples under various scenes and performing task element decomposition on the key tasks;
the key task portrait forming unit is used for analyzing each task element according to different dimensions and forming a key task portrait;
and the capability structure model construction unit is used for splitting the key task portrait to form a capability structure portrait, acquiring knowledge form distribution and body reaction distribution of the research object according to the capability structure portrait, and constructing a capability structure model according to the knowledge form distribution and the body reaction distribution.
Further, the apparatus further comprises:
the data iteration unit is used for carrying out comparative analysis on the data of different superior persons in the same post, analyzing the same points and different points of the key tasks and the capability structure distribution forms of the different superior persons, and counting the number of different times of occurrence of each key task to obtain the probability and frequency of occurrence of each key task; processing knowledge capability structures of different excellent persons to obtain the number and frequency of different knowledge and different behaviors appearing in different excellent persons, obtaining the association probability of the knowledge, the behaviors and the careers, and further obtaining big data for updating and iterating the capability structure model; the big data is used to perform an update iteration on the capability structure model.
The method and the device for constructing the vocational portrait data model by using the talent big data in the embodiment of the invention perform scene-by-scene processing on a data research sample, analyze key tasks completed by a research object in each scene, form a key task portrait, then split the key task portrait to form an ability structure portrait, acquire knowledge form distribution and body reaction distribution of the research object according to the ability structure portrait, and construct the ability structure model according to the knowledge form distribution and the body reaction distribution. The operation difficulty of comparison and analysis is greatly reduced, the efficiency is greatly improved, artificial intelligence and machine learning can be partially realized in the future, intelligent comparison is realized by IT, and the method is similar to the current face recognition technology. The work operation difficulty of the manual operation part is greatly reduced, the manual operation part is not necessarily realized by talent assessment experts, an analyst with certain professional knowledge can complete the operation, and the accuracy and the efficiency are greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method for constructing a vocational portrait data model using talent big data according to the present invention;
FIG. 2 is a flow chart illustrating an embodiment of a method for constructing a model of job portrait data using big data of talents according to the present invention;
FIG. 3 is a block diagram of an apparatus for constructing a model of job portrait data using talent big data according to the present invention;
FIG. 4 is a block diagram of an apparatus for constructing a model of job portrait data using talent big data according to the present invention;
FIG. 5 is a schematic diagram of a method for constructing a vocational representation data model by using talent big data according to the ability structure distribution diagram of a vocational person to divide the full life cycle of each vocational into nine stages;
FIG. 6 is a schematic diagram of the construction of a vocational representation data model by using talent big data according to the ability structure distribution diagram of a vocational person to divide the full life cycle of each vocational into nine stages.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided a method for constructing a vocational portrait data model by using talent big data, referring to fig. 1, including the following steps:
s101, using the occupation data of a plurality of persons with excellent performance in the same occupation or the occupation data periodically recorded by the same person at different career stages as data research samples;
s102, performing scene-based processing on the data research sample;
s103, analyzing key tasks completed by research objects in the data research samples under all scenes, and performing task element decomposition on the key tasks;
s104, analyzing each task element according to different dimensions and forming a key task portrait;
and S105, splitting the key task portrait to form a capability structure portrait, acquiring knowledge form distribution and body reaction distribution of the research object according to the capability structure portrait, and constructing a capability structure model according to the knowledge form distribution and the body reaction distribution.
The method for constructing the vocational portrait data model by using the talent big data comprises the steps of performing scene-by-scene processing on a data research sample, analyzing key tasks completed by research objects in various scenes, forming key task portraits, then splitting the key task portraits to form capability structure portraits, obtaining knowledge form distribution and body reaction distribution of the research objects according to the capability structure portraits, and constructing the capability structure model according to the knowledge form distribution and the body reaction distribution. The operation difficulty of comparison and analysis is greatly reduced, the efficiency is greatly improved, artificial intelligence and machine learning can be partially realized in the future, intelligent comparison is realized by IT, and the method is similar to the current face recognition technology. The work operation difficulty of the manual operation part is greatly reduced, the manual operation part is not necessarily realized by talent assessment experts, an analyst with certain professional knowledge can complete the operation, and the accuracy and the efficiency are greatly improved.
Wherein, the processing of the data research sample by scenes comprises:
different working scenes are listed according to different geographic positions of the data research samples, and people, objects, time and behaviors contacted by the research objects under different scenes are recorded.
The method for analyzing the key tasks completed by the research objects in the data research samples under all scenes and performing task element decomposition on the key tasks comprises the following steps:
analyzing key tasks completed by research objects in data research samples under various scenes, wherein the structure of data extracted in each specific scene is as follows: the method comprises the steps of scene-first-stage task-second-stage task-third-stage task, wherein the later-stage task is a lower-level decomposition task of the former-stage task until the task is decomposed into task elements which can be used as independent analysis factors.
Wherein, analyzing each task element according to different dimensions and forming a key task portrait comprises:
and counting the frequency of the single type of task elements under each dimension in the three-level task and the frequency of the task formed by compounding more than two types of task elements, naming and coding all the task elements, and forming a key task portrait according to all the named and coded task elements.
The method comprises the following steps of splitting a key task portrait to form a capability structure portrait, acquiring knowledge form distribution and body reaction distribution of a research object according to the capability structure portrait, and constructing a capability structure model according to the knowledge form distribution and the body reaction distribution, wherein the capability structure portrait comprises the following steps:
according to a data structure between task elements and task completion logic, splitting the completion process of each key task to form a capability structure portrait in a capability structure model;
and coding, classifying, summarizing and combining the knowledge, behavior and problem solving measure characteristics separated from each key task to form a capability structure distribution map and a capability structure distribution form of the research object, and constructing a capability structure model according to the capability structure distribution map and the capability structure distribution form.
Wherein, the form of the capability structure model comprises: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; a multi-component composite type; the divided knowledge characteristics comprise general knowledge and post knowledge; the behavior characteristics of the split-out include: practice of actions, cognitive observation, thinking, communication, feeling of emotion; the characteristic of the measure for solving the problem of disassembly comprises the following steps: problems encountered and solutions.
Wherein, referring to fig. 2, the method further comprises:
s106, carrying out comparative analysis on data of different superior persons in the same post, analyzing the same points and different points of the key tasks and the capability structure distribution forms of the different superior persons, and carrying out statistics on the number of different times of occurrence of each key task to obtain the probability and frequency of occurrence of each key task;
s107, processing knowledge capability structures of different excellent persons, obtaining the number and frequency of different knowledge and different behaviors appearing in different excellent persons, obtaining the association probability of the knowledge, the behaviors and the professionals, and further obtaining big data for updating and iterating the capability structure model;
and S108, updating and iterating the capacity structure model by using the big data.
Wherein, referring to fig. 2, the method further comprises:
and S109, generating a key task probability query table, and querying probability values of key task composition and multi-person association after corresponding according to task element codes in the query table.
The method for constructing a professional portrait data model by using talent big data according to the present invention is described in detail with specific embodiments below.
1. The occupational space axis data structure is a key task portrait model: a key task representation.
Data of the qualified staff with excellent performance in the professional team organization is analyzed as a standard and a basis for carrying out data modeling of key tasks, and the dimensions of the data model comprise fields (environment), people (occupation and position), objects and behaviors. What kind of occupation and position people react to what kind of behaviour they make in what place and after facing the things they are around. The scene-human-object-behavior forms a data chain, the data chain is used for forming a key task image of a professional, and the differentiated task characteristics of the image are determined to distinguish other persons with professions and posts, so that the characteristics of the profession are accurately defined.
2. The bio-axis data structure is a capability structure model.
The method comprises the steps of calculating corresponding knowledge form distribution and body reaction distribution according to a plurality of personal key tasks of the career, wherein the knowledge form distribution comprises general knowledge, post knowledge and experience, the body reaction distribution comprises action practice, cognitive observation, thinking, communication and feeling of emotion, then carrying out related calculation on the knowledge form distribution and the body reaction distribution, drawing an ability structure distribution map of the career, and dividing the career ability of the career into five types by the ability structure distribution map of the career.
Five models are as follows: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; bench type (multi-unit composite type); corresponding to nine stages in the critical growth path model: n; s1; s2; E1-E3; M1-M3.
3. The time axis data structure is a key growth path model: critical growth path (critical path squared).
The full life cycle of each profession is divided into nine stages according to the competency structure distribution map of the professions, and as shown in fig. 5, the new person who enters the profession and finally becomes the high-hand and professional of the profession, the life cycle can be divided into nine stages.
Wherein, N is a new person in only one type;
qualified, S can be divided into two sub-types, S1 (qualified professional or management competencies, including professional technical posts, research and development engineers or IT) and S2 (qualified multi-functional posts, and two or more of both professional or management competencies);
experienced, E can be divided into three sub-types, E1 (experienced professional, management competent), E2 (experienced and professional plus management competent), E3 (experienced and professional, management competence, thinking ability, communication ability, or more);
the expert master, M, may also be divided into three sub-types, M1 (expert professional, management competent), M2 (expert and combined professional plus management competent), M3 (expert and combined professional, management, thinking, and communication).
Specifically, the detailed steps of the method of the invention are as follows:
1. and selecting a plurality of persons with excellent performance in the same profession and post as data research samples, or selecting professional data which is regularly recorded by the persons in different professional career stages of the same person as data research samples for portraying key tasks.
2. Listing different working scenes according to different geographic positions of the data research samples, and recording people (occupation and post), objects, time (occasion, environment) and behaviors which are contacted by the research objects under different scenes; different geographical locations, different work environments, different people and objects in contact with them may all be considered different scenarios.
3. Analyzing the key tasks completed by the research objects under various scenes, wherein the structure of the data extracted by each specific post is as follows: the method comprises the steps of scene-first-stage task-second-stage task-third-stage task, wherein the later-stage task is a lower-level decomposition task of the former-stage task until the task is decomposed into task elements which can be used as independent analysis factors.
4. Analyzing the three-level task according to different dimensions and forming a key task portrait: different dimensions can be frequency, importance degree, complexity degree and achievement certainty, the tasks are classified into task elements according to the types of the three-level tasks, the task elements comprise operation types, professional technology types, skill types, technical service types, research types, interpersonal communication types, organization management types, decision types and creation types, the frequency of the single type task elements in each dimension in the three-level tasks and the frequency of the task combined by two, three or even four types of task elements are counted; and naming and encoding all task elements, wherein all the named and encoded task elements are used for constructing a knowledge graph, the knowledge graph comprises knowledge form distribution and body reaction distribution, the knowledge form distribution comprises general knowledge, post knowledge and experience, and the body reaction distribution comprises action practice, cognitive observation, thinking, communication and emotion feeling.
5. And according to a data structure between task elements and logic of the completed task, the completion process of each key task is divided to form a capability structure representation in the capability structure model. And splitting a knowledge structure required for completing each three-level task in the capability structure portrait into: general knowledge + post knowledge + behavior (action practice + cognitive observation + thinking + communication + feeling emotion) + problems encountered + solutions, and the like. The latter 2 encountered problems and solutions are characterized by empirical features in the knowledge morphological distribution.
6. The characteristics of knowledge, behavior, problem solving measures and the like separated by each key task are coded, classified, induced and combined to form a capability structure distribution diagram and a capability structure distribution form of a research object, and each distribution form presents a distribution graph. The 5 forms are specifically as follows: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; the bench type (multi-component composite type) corresponds the 5 forms to nine stages in the critical growth path model in the following way: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; the bench type (multi-component composite type) corresponds to the nine stages: n; s1; s2; E1-E3; M1-M3.
7. The data of different superior persons in the same post are compared and analyzed, the same points and different points of the key tasks and the capability structure distribution forms of the different superior persons are analyzed, the number of different times of occurrence of each key task is counted, and the probability and the frequency of occurrence of each key task are obtained; and similarly processing the knowledge structure, obtaining the number and frequency of people with different knowledge and different behaviors appearing in different excellences, obtaining the association probability of the knowledge, the behaviors and the professionals, further obtaining big data capable of carrying out update iteration on the capability structure model, and carrying out update iteration on the capability structure model by using the big data.
8. Generating a key task probability query table used for querying the vocational assessment as follows:
the invention subdivides the vocational evaluation into each specific vocational, firstly, big data are used for grabbing and sorting various data of talents generally recognized by the industry and supported by performance to form a vocational portrait data model, various independent labels are arranged in the vocational portrait data model, the independent labels form a vocational portrait, and the vocational portrait is correspondingly associated with the vocational talent big data; then comparing the characteristic data of the evaluated person with the labels of the talent professional big data one by one, and analyzing the goodness of fit of the big data of a certain person and the talent professional like cracking the genetic DNA of the person. The invention converts the advantages of human ability and occupational demand into various data and data labels, is more accurate than the original qualitative analysis, and realizes the quantitative analysis and accurate mapping of human.
The operation difficulty of comparison and analysis is greatly reduced, the efficiency is greatly improved, artificial intelligence and machine learning can be partially realized in the future, intelligent comparison is realized by IT, and the method is similar to the current face recognition technology. The work operation difficulty of the manual operation part is greatly reduced, the manual operation part is not necessarily realized by talent assessment experts, an analyst with certain professional knowledge can complete the operation, and the accuracy and the efficiency are greatly improved.
Example 2
According to another embodiment of the present invention, there is provided an apparatus for constructing a vocational portrait data model using talent big data, referring to fig. 3, including:
a sample acquisition unit 201, configured to use the job data of multiple persons with excellent performance in the same job or job data periodically recorded in different lifetime stages of the same person as data research samples;
a scene processing unit 202, configured to perform scene-based processing on the data research sample;
the task element decomposition unit 203 is used for analyzing key tasks completed by research objects in the data research samples under various scenes and performing task element decomposition on the key tasks;
a key task portrait forming unit 204, configured to analyze each task element according to different dimensions and form a key task portrait;
and the capability structure model constructing unit 205 is used for splitting the key task portrait to form a capability structure portrait, acquiring knowledge form distribution and body reaction distribution of the research object according to the capability structure portrait, and constructing a capability structure model according to the knowledge form distribution and the body reaction distribution.
The device for constructing the vocational portrait data model by using the talent big data performs scene-by-scene processing on a data research sample, analyzes key tasks completed by research objects in each scene, forms a key task portrait, then splits the key task portrait to form an ability structure portrait, acquires knowledge form distribution and body reaction distribution of the research objects according to the ability structure portrait, and constructs the ability structure model according to the knowledge form distribution and the body reaction distribution. The operation difficulty of comparison and analysis is greatly reduced, the efficiency is greatly improved, artificial intelligence and machine learning can be partially realized in the future, intelligent comparison is realized by IT, and the method is similar to the current face recognition technology. The work operation difficulty of the manual operation part is greatly reduced, the manual operation part is not necessarily realized by talent assessment experts, an analyst with certain professional knowledge can complete the operation, and the accuracy and the efficiency are greatly improved.
Wherein, referring to fig. 4, the apparatus further comprises:
the data iteration unit 206 is configured to compare and analyze data of different superior persons in the same post, analyze the same points and different points of the key tasks and the distribution forms of the capability structures of the different superior persons, and count the number of times that each key task occurs, so as to obtain the probability and frequency of occurrence of each key task; processing knowledge capability structures of different excellent persons to obtain the number and frequency of different knowledge and different behaviors appearing in different excellent persons, obtaining the association probability of the knowledge, the behaviors and the careers, and further obtaining big data for updating and iterating the capability structure model; the big data is used to perform an update iteration on the capability structure model.
The following describes in detail an apparatus for constructing a model of job portrait data using talent big data according to the present invention with specific embodiments.
1. The occupational space axis data structure is a key task portrait model: a key task representation.
Data of the qualified staff with excellent performance in the professional team organization is analyzed as a standard and a basis for carrying out data modeling of key tasks, and the dimensions of the data model comprise fields (environment), people (occupation and position), objects and behaviors. What kind of occupation and position people react to what kind of behaviour they make in what place and after facing the things they are around. The scene-human-object-behavior forms a data chain, the data chain is used for forming a key task image of a professional, and the differentiated task characteristics of the image are determined to distinguish other persons with professions and posts, so that the characteristics of the profession are accurately defined.
2. The bio-axis data structure is a capability structure model.
The method comprises the steps of calculating corresponding knowledge form distribution and body reaction distribution according to a plurality of personal key tasks of the career, wherein the knowledge form distribution comprises general knowledge, post knowledge and experience, the body reaction distribution comprises action practice, cognitive observation, thinking, communication and feeling of emotion, then carrying out related calculation on the knowledge form distribution and the body reaction distribution, drawing an ability structure distribution map of the career, and dividing the career ability of the career into five types by the ability structure distribution map of the career.
Five models are as follows: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; bench type (multi-unit composite type); corresponding to nine stages in the critical growth path model: n; s1; s2; E1-E3; M1-M3.
3. The time axis data structure is a key growth path model: critical growth path (critical path squared).
The full life cycle of each profession is divided into nine stages according to the competency structure distribution diagram of the professions, and as shown in fig. 6, the new person entering the profession, and finally the senior and the specialist in the workplace, can be divided into nine stages.
Wherein, N is a new person in only one type;
qualified, S can be divided into two sub-types, S1 (qualified professional or management competencies, including professional technical posts, research and development engineers or IT) and S2 (qualified multi-functional posts, and two or more of both professional or management competencies);
experienced, E can be divided into three sub-types, E1 (experienced professional, management competent), E2 (experienced and professional plus management competent), E3 (experienced and professional, management competence, thinking ability, communication ability, or more);
the expert master, M, may also be divided into three sub-types, M1 (expert professional, management competent), M2 (expert and combined professional plus management competent), M3 (expert and combined professional, management, thinking, and communication).
Specifically, the detailed processing steps of the device of the invention are as follows:
1. the sample acquisition unit 201: and selecting a plurality of persons with excellent performance in the same profession and post as data research samples, or selecting professional data which is regularly recorded by the persons in different professional career stages of the same person as data research samples for portraying key tasks.
2. The scene processing unit 202: listing different working scenes according to different geographic positions of the data research samples, and recording people (occupation and post), objects, time (occasion, environment) and behaviors which are contacted by the research objects under different scenes; different geographical locations, different work environments, different people and objects in contact with them may all be considered different scenarios.
3. The task element decomposition unit 203: analyzing the key tasks completed by the research objects under various scenes, wherein the structure of the data extracted by each specific post is as follows: the method comprises the steps of scene-first-stage task-second-stage task-third-stage task, wherein the later-stage task is a lower-level decomposition task of the former-stage task until the task is decomposed into task elements which can be used as independent analysis factors.
4. The key task representation forming unit 204: analyzing the three-level task according to different dimensions and forming a key task portrait: different dimensions can be frequency, importance degree, complexity degree and achievement certainty, the tasks are classified into task elements according to the types of the three-level tasks, the task elements comprise operation types, professional technology types, skill types, technical service types, research types, interpersonal communication types, organization management types, decision types and creation types, the frequency of the single type task elements in each dimension in the three-level tasks and the frequency of the task combined by two, three or even four types of task elements are counted; and naming and encoding all task elements, wherein all the named and encoded task elements are used for constructing a knowledge graph, the knowledge graph comprises knowledge form distribution and body reaction distribution, the knowledge form distribution comprises general knowledge, post knowledge and experience, and the body reaction distribution comprises action practice, cognitive observation, thinking, communication and emotion feeling.
5. Capability structure model construction unit 205: and according to a data structure between task elements and logic of the completed task, the completion process of each key task is divided to form a capability structure representation in the capability structure model. And splitting a knowledge structure required for completing each three-level task in the capability structure portrait into: general knowledge + post knowledge + behavior (action practice + cognitive observation + thinking + communication + feeling emotion) + problems encountered + solutions, and the like. The latter 2 encountered problems and solutions are characterized by empirical features in the knowledge morphological distribution.
6. Capability structure model construction unit 205: the characteristics of knowledge, behavior, problem solving measures and the like separated by each key task are coded, classified, induced and combined to form a capability structure distribution diagram and a capability structure distribution form of a research object, and each distribution form presents a distribution graph. The 5 forms are specifically as follows: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; the bench type (multi-component composite type) corresponds the 5 forms to nine stages in the critical growth path model in the following way: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; the bench type (multi-component composite type) corresponds to the nine stages: n; s1; s2; E1-E3; M1-M3.
7. The data iteration unit 206: the data of different superior persons in the same post are compared and analyzed, the same points and different points of the key tasks and the capability structure distribution forms of the different superior persons are analyzed, the number of different times of occurrence of each key task is counted, and the probability and the frequency of occurrence of each key task are obtained; and similarly processing the knowledge structure, obtaining the number and frequency of people with different knowledge and different behaviors appearing in different excellences, obtaining the association probability of the knowledge, the behaviors and the professionals, further obtaining big data capable of carrying out update iteration on the capability structure model, and carrying out update iteration on the capability structure model by using the big data.
8. Generating a key task probability query table used for querying the vocational assessment as follows:
the invention subdivides the vocational evaluation into each specific vocational, firstly, big data are used for grabbing and sorting various data of talents generally recognized by the industry and supported by performance to form a vocational portrait data model, various independent labels are arranged in the vocational portrait data model, the independent labels form a vocational portrait, and the vocational portrait is correspondingly associated with the vocational talent big data; then comparing the characteristic data of the evaluated person with the labels of the talent professional big data one by one, and analyzing the goodness of fit of the big data of a certain person and the talent professional like cracking the genetic DNA of the person. The invention converts the advantages of human ability and occupational demand into various data and data labels, is more accurate than the original qualitative analysis, and realizes the quantitative analysis and accurate mapping of human.
The operation difficulty of comparison and analysis is greatly reduced, the efficiency is greatly improved, artificial intelligence and machine learning can be partially realized in the future, intelligent comparison is realized by IT, and the method is similar to the current face recognition technology. The work operation difficulty of the manual operation part is greatly reduced, the manual operation part is not necessarily realized by talent assessment experts, an analyst with certain professional knowledge can complete the operation, and the accuracy and the efficiency are greatly improved.
The invention has the innovative technical points and the beneficial effects that:
1. typical characteristics of high hands on each workplace are accurately described by objective big data and various models, and are visualized, so that perceptual cognition in talent evaluation under the traditional human resource management theory and judgment and analysis of talents by an evaluator artificially and subjectively are replaced;
2. by accumulating talent data of each professional senior and forming a database, the data of the database is formed into different data models, such as: the key task portrait model, the capability structure model and the key growth path model are used for comparing and analyzing data of a consultant who wants to know the development of the future profession with data of talents in the same profession to obtain the possibility and probability of the consultant becoming a high hand in the profession;
3. by dividing the occupation into nine stages, continuously acquiring data of the nine stages, discovering characteristics and rules of development of the occupation through continuous change of the data, and helping a new professional to acquire effective and direct data in the process of simulating and learning a foreigner trajectory.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, a division of a unit may be a logical division, and an actual implementation may have another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for constructing a vocational portrait data model by using talent big data is characterized by comprising the following steps:
the method comprises the following steps of taking the occupation data of a plurality of persons with excellent performance in the same occupation or the occupation data periodically recorded by the persons in different career stages in the same person as a data research sample;
performing scene-by-scene processing on a data research sample;
analyzing key tasks completed by research objects in the data research samples under various scenes, and performing task element decomposition on the key tasks;
analyzing each task element according to different dimensions and forming a key task portrait;
and splitting the key task portrait to form a capability structure portrait, acquiring knowledge form distribution and body reaction distribution of a research object according to the capability structure portrait, and constructing a capability structure model according to the knowledge form distribution and the body reaction distribution.
2. The method for constructing a vocational portrait data model by using talent big data as claimed in claim 1, wherein the processing of the data research sample in different scenes comprises:
different working scenes are listed according to different geographic positions of the data research samples, and people, objects, time and behaviors contacted by the research objects under different scenes are recorded.
3. The method of claim 1, wherein analyzing key tasks performed by research objects in the data research sample under each scene, and performing task meta-decomposition on the key tasks comprises:
analyzing key tasks completed by research objects in data research samples under various scenes, wherein the structure of data extracted in each specific scene is as follows: the method comprises the steps of scene-first-stage task-second-stage task-third-stage task, wherein the later-stage task is a lower-level decomposition task of the former-stage task until the task is decomposed into task elements which can be used as independent analysis factors.
4. The method of claim 3, wherein analyzing task elements in different dimensions and forming a key task representation comprises:
and counting the frequency of the single type of task elements under each dimension in the three-level task and the frequency of the task formed by compounding more than two types of task elements, naming and coding all the task elements, and forming a key task portrait according to all the named and coded task elements.
5. The method of claim 4, wherein the step of splitting the key task representation to form a capability structure representation, the step of obtaining knowledge morphological distribution and body response distribution of the study subject based on the capability structure representation, and the step of constructing the capability structure model based on the knowledge morphological distribution and the body response distribution comprises:
according to a data structure between task elements and task completion logic, splitting the completion process of each key task to form a capability structure portrait in a capability structure model;
and coding, classifying, summarizing and combining the knowledge, behavior and problem solving measure characteristics separated from each key task to form a capability structure distribution map and a capability structure distribution form of the research object, and constructing a capability structure model according to the capability structure distribution map and the capability structure distribution form.
6. The method for constructing a vocational portrait data model using talent big data as claimed in claim 5, wherein the form of the capability structure model comprises: performing fuzzy modeling; a practical type; a cognitive type; a balanced type; a multi-component composite type; the divided knowledge characteristics comprise general knowledge and post knowledge; the behavior characteristics of the split-out include: practice of actions, cognitive observation, thinking, communication, feeling of emotion; the characteristic of the measure for solving the problem of disassembly comprises the following steps: problems encountered and solutions.
7. The method for constructing a model of portrait data using talent big data as claimed in claim 1, further comprising:
the data of different superior persons in the same post are compared and analyzed, the same points and different points of the key tasks and the capability structure distribution forms of the different superior persons are analyzed, the number of different times of occurrence of each key task is counted, and the probability and the frequency of occurrence of each key task are obtained;
processing knowledge capability structures of different excellent persons to obtain the number and frequency of different knowledge and different behaviors appearing in different excellent persons, obtaining the association probability of the knowledge, the behaviors and the careers, and further obtaining big data for updating and iterating the capability structure model;
the big data is used to perform an update iteration on the capability structure model.
8. The method for constructing a model of portrait data using talent big data as claimed in claim 1, further comprising:
and generating a key task probability query table, and querying probability values of key task composition and multi-person association after corresponding according to task element codes in the query table.
9. An apparatus for constructing a vocational portrait data model using talent big data, comprising:
the sample acquisition unit is used for taking the occupation data of a plurality of persons with excellent performance in the same occupation or the occupation data periodically recorded by the same person at different career stages as data research samples;
the scene processing unit is used for performing scene processing on the data research sample;
the task element decomposition unit is used for analyzing key tasks completed by research objects in the data research samples under various scenes and performing task element decomposition on the key tasks;
the key task portrait forming unit is used for analyzing each task element according to different dimensions and forming a key task portrait;
and the capability structure model construction unit is used for splitting the key task portrait to form a capability structure portrait, acquiring knowledge form distribution and body reaction distribution of the research object according to the capability structure portrait, and constructing a capability structure model according to the knowledge form distribution and the body reaction distribution.
10. The apparatus for constructing a model of portrait data using talent big data as claimed in claim 9, further comprising:
the data iteration unit is used for carrying out comparative analysis on the data of different superior persons in the same post, analyzing the same points and different points of the key tasks and the capability structure distribution forms of the different superior persons, and counting the number of different times of occurrence of each key task to obtain the probability and frequency of occurrence of each key task; processing knowledge capability structures of different excellent persons to obtain the number and frequency of different knowledge and different behaviors appearing in different excellent persons, obtaining the association probability of the knowledge, the behaviors and the careers, and further obtaining big data for updating and iterating the capability structure model; the big data is used to perform an update iteration on the capability structure model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010841000.XA CN111985897B (en) | 2020-08-20 | 2020-08-20 | Method and device for constructing professional portrait data model by using talent big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010841000.XA CN111985897B (en) | 2020-08-20 | 2020-08-20 | Method and device for constructing professional portrait data model by using talent big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111985897A true CN111985897A (en) | 2020-11-24 |
| CN111985897B CN111985897B (en) | 2024-01-12 |
Family
ID=73442402
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010841000.XA Active CN111985897B (en) | 2020-08-20 | 2020-08-20 | Method and device for constructing professional portrait data model by using talent big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111985897B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030177027A1 (en) * | 2002-03-08 | 2003-09-18 | Dimarco Anthony M. | Multi-purpose talent management and career management system for attracting, developing and retaining critical business talent through the visualization and analysis of informal career paths |
| KR20140052354A (en) * | 2012-10-24 | 2014-05-07 | 주식회사 국민은행 | Competency development supporting system and method thereof |
| CN106156935A (en) * | 2015-04-22 | 2016-11-23 | 董武斌 | A kind of enrollment based on Post Model, train, practise, device of obtaining employment |
| CN106339784A (en) * | 2015-09-22 | 2017-01-18 | 第推动（北京）国际咨询有限公司 | Big data type statistical analysis and intelligent processing method |
| CN107126222A (en) * | 2017-06-23 | 2017-09-05 | 中国科学院心理研究所 | A kind of cognitive ability evaluation system and its assessment method |
| CN107437131A (en) * | 2016-05-26 | 2017-12-05 | 上海易识教育科技有限公司 | A kind of talent ability for university student crowd is assessed and the method for Postmatch |
| CN110516959A (en) * | 2019-08-23 | 2019-11-29 | 浙江甄才科技发展有限公司 | Quality ability post modeling method and system |
| CN110517017A (en) * | 2019-08-23 | 2019-11-29 | 浙江甄才科技发展有限公司 | Talent role post modeling method and system |
| CN111080241A (en) * | 2019-12-04 | 2020-04-28 | 贵州非你莫属人才大数据有限公司 | Internet platform-based data-based talent management analysis system |
| CN111325529A (en) * | 2020-03-20 | 2020-06-23 | 广东人啊人网络技术开发有限公司 | Talent evaluation-based personnel and post matching method and system |
-
2020
- 2020-08-20 CN CN202010841000.XA patent/CN111985897B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030177027A1 (en) * | 2002-03-08 | 2003-09-18 | Dimarco Anthony M. | Multi-purpose talent management and career management system for attracting, developing and retaining critical business talent through the visualization and analysis of informal career paths |
| KR20140052354A (en) * | 2012-10-24 | 2014-05-07 | 주식회사 국민은행 | Competency development supporting system and method thereof |
| CN106156935A (en) * | 2015-04-22 | 2016-11-23 | 董武斌 | A kind of enrollment based on Post Model, train, practise, device of obtaining employment |
| CN106339784A (en) * | 2015-09-22 | 2017-01-18 | 第推动（北京）国际咨询有限公司 | Big data type statistical analysis and intelligent processing method |
| CN107437131A (en) * | 2016-05-26 | 2017-12-05 | 上海易识教育科技有限公司 | A kind of talent ability for university student crowd is assessed and the method for Postmatch |
| CN107126222A (en) * | 2017-06-23 | 2017-09-05 | 中国科学院心理研究所 | A kind of cognitive ability evaluation system and its assessment method |
| CN110516959A (en) * | 2019-08-23 | 2019-11-29 | 浙江甄才科技发展有限公司 | Quality ability post modeling method and system |
| CN110517017A (en) * | 2019-08-23 | 2019-11-29 | 浙江甄才科技发展有限公司 | Talent role post modeling method and system |
| CN111080241A (en) * | 2019-12-04 | 2020-04-28 | 贵州非你莫属人才大数据有限公司 | Internet platform-based data-based talent management analysis system |
| CN111325529A (en) * | 2020-03-20 | 2020-06-23 | 广东人啊人网络技术开发有限公司 | Talent evaluation-based personnel and post matching method and system |
Non-Patent Citations (2)
| Title |
|---|
| 张勤等: "中等职业学校班主任胜任力模型的构建与应用研究", 中国职业技术教育, no. 20, pages 5 - 8 * |
| 牛艳萍;刘辉;彭海文;王哲;: "维和护士核心胜任力模型构建", 解放军医院管理杂志, no. 10, pages 944 - 946 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111985897B (en) | 2024-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108182489B (en) | Personalized learning recommendation method based on online learning behavior analysis | |
| Dakic et al. | BUSINESS PROCESS MINING APPLICATION: A LITERATURE REVIEW. | |
| Kaunang et al. | Students' academic performance prediction using data mining | |
| Alguliyev et al. | Multicriteria personnel selection by the modified fuzzy VIKOR method | |
| Lotsari et al. | A learning analytics methodology for student profiling | |
| Magruk | The process of selection of the main research methods in foresight from different perspectives | |
| Mazaheri et al. | Research directions in information systems field, current status and future trends: A literature analysis of AIS basket of top journals | |
| Li et al. | Fuzzy-clustering embedded regression for predicting student academic performance | |
| Rahman et al. | Predictive analysis and data mining among the employment of fresh graduate students in HEI | |
| Mhon et al. | ETL preprocessing with multiple data sources for academic data analysis | |
| Iqbal et al. | Building most effective requirements engineering teams by evaluating their personality traits using big-five assessment model | |
| CN104615910A (en) | Method for predicating helix interactive relationship of alpha transmembrane protein based on random forest | |
| JP2018147351A (en) | Knowledge model construction system and knowledge model construction method | |
| CN111985897A (en) | Method and device for constructing occupational portrait data model by using talent big data | |
| Alqahtani et al. | Analysis and Prediction of Employee Promotions Using Machine Learning | |
| JP2012098921A (en) | User classification system | |
| Özdaban et al. | A FUZZY METHOD ON DETERMINING OF JOB AND PERSONNEL EVALUATION RESULTS, AND MATCHING THEM WITH SUGGESTED MODEL. | |
| Elbadrawy et al. | Upm: Discovering course enrollment sequences associated with success | |
| Gata et al. | The Feasibility of Credit Using C4. 5 Algorithm Based on Particle Swarm Optimization Prediction | |
| KR101680195B1 (en) | Method for analyzing relationship between human personality and favored location | |
| Qu et al. | What is my next job: Predicting the company size and position in career changes | |
| Dalimunthe et al. | Study of C45 Algorithm In Predicting New Employee Acception | |
| Sardar et al. | Employee Turnover Prediction by Machine Learning Techniques | |
| Maulana et al. | Accuracy Analysis of Community Satisfaction in Population Administration Services Using the C4. 5 Algorithm and Naïve Bayes Method: Accuracy Analysis of Community Satisfaction in Population Administration Services Using the C4. 5 Algorithm and Naïve Bayes Method | |
| Csalódi et al. | Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout. Mathematics 2021, 9, 463 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |