CN113222400A

CN113222400A - Intelligent career planning and individual growth strategy making system and method

Info

Publication number: CN113222400A
Application number: CN202110500115.7A
Authority: CN
Inventors: 冀伟; 王云松
Original assignee: Beijing Future Exploration Technology Co ltd
Current assignee: Beijing Future Exploration Technology Co ltd
Priority date: 2021-05-08
Filing date: 2021-05-08
Publication date: 2021-08-06

Abstract

The invention provides an intelligent career planning and individual growth strategy making system and method. The method comprises the following steps: a first memory storing a standard model database; the information acquisition module is used for acquiring user information; the second memory is used for storing the user information in a classified manner to form a personal database; the processing module is used for generating a value view matching rate of the user, generating a user competency model, generating a competency matching rate and a competency overflow rate of each occupation of the user, and obtaining a user matching occupation; generating a personal growth strategy report according to the matched occupation of the user; and the user terminal receives the career planning report and the personal growth strategy report. This intelligence career planning and individual growth strategy formulation system has improved the artifical when evaluating and planning career development direction for the user of line professional planner in the prior art, and the price is expensive, inefficiency, can't popularize fast and easily receive planner subjective factor and self professional level influence, has the problem of certain planning deviation.

Description

Intelligent career planning and individual growth strategy making system and method

Technical Field

The invention relates to the technical field of education science and technology, in particular to an intelligent career planning and individual growth strategy making system and method.

Background

According to data statistics, more than 70% of people's regret selects wrong occupation in young, and more than 70% of college students' regret blindly selects profession; in addition, the occupation tendency of the new-generation teenagers in China exceeds 70 percent and is biased to the entertainment industry, and talents in high-tech industries such as communication, electronics, internet, energy and the like in five years in the future in China have more than 4000 ten thousand gaps, so that talent resources are seriously unmatched. Therefore, systematic occupational planning, correct occupational cognition and scientific development paths are necessary for the young and the young in China. However, most of the existing domestic professional planning only evaluates and plans by the offline professional planners manually, and is expensive, low in efficiency and incapable of being popularized quickly, and the professional planning is easily affected by subjective factors of the planners and professional levels of the planners, so that certain planning deviation is caused.

Disclosure of Invention

The invention aims to provide an intelligent career planning and individual growth strategy making system and method, and the intelligent career planning and individual growth strategy making system can solve the problems that in the prior art, a lower professional planner manually evaluates and plans the development direction of careers for users, is high in price, low in efficiency, incapable of being popularized quickly, easily influenced by subjective factors of the planner and the professional level of the planner and has certain planning deviation.

In order to achieve the above purpose, the invention provides the following technical scheme:

an intelligent career planning and individual growth strategy making system, comprising:

a first memory storing a standard model database;

the information acquisition module is electrically connected with the standard model database and is used for acquiring user information, wherein the personal information comprises user value and appearance information, user interest and occupation information, user personality information, user talent information and user circle information;

the second memory is electrically connected with the information acquisition module and is used for classifying the user information into a value model, a character model, a talent model, a circle model and a character model of the user and storing the user information to form a personal database;

the processing module is electrically connected with the first memory and the second memory and is used for matching the user value view information in the personal database with the standard model database to generate a value view matching rate of the user and matching the user character information and the user talent information with the standard model database to generate a user competency model;

comparing the user competence model with a standard occupation competence model library in the first memory, generating competence matching rate and competence overflow rate of each occupation of the user, performing occupation ranking according to the competence matching rate and the competence overflow rate, and obtaining user matching occupation according to the occupation ranking, the user value observation matching rate, the user interest occupation information and the user circle information;

the processing module generates a personal growth strategy report according to the matched occupation of the user;

and the user terminal is electrically connected with the processing module and is used for receiving the career planning report and the personal growth strategy report.

On the basis of the technical scheme, the invention can be further improved as follows:

further, the standard model database comprises a professional value view model library, a competency-personality model library, a professional competency model library, a professional information library and a professional-college professional-science scientific library;

the professional value view model library comprises value views of each professional satisfaction; the victory force-character talent model library comprises characters matched with each victory force, talents and probability statistical models thereof; the occupation information base comprises the brief introduction, representative characters, work content, work duration, work pressure, development condition and salary condition of each occupation; the profession-college profession-discipline library includes college professions, institutions, and basic disciplines for each profession match.

Furthermore, the intelligent career planning and individual growth strategy making system further comprises a model optimization module, the model optimization module is electrically connected with the information acquisition module and the processing module, the information acquisition module is used for inputting test information of a tester, and the standard career competency model base is calibrated according to the test information through a dynamic minimum discrete regression algorithm.

Further, the processing module compares the standard occupation competence model library with the user competence model to generate a first quadrant window;

the user circle information further comprises 360-degree evaluation information, and the processing module generates a second quadrant window according to the 360-degree evaluation information in the user circle information after obtaining the user competence model.

An intelligent career planning method, the method specifically comprising:

s101, storing a standard model database into a standard model database;

s102, acquiring user information through an information acquisition module;

s103, storing the user information into a personal database;

s104, matching user value view information in a personal database with the standard model database to generate a value view matching rate of the user, and matching the user character information and the user talent information with the standard model database to generate a user competence model;

s105, comparing the user competence model with the standard occupation competence model base, generating competence matching rate and competence overflow rate of each occupation of the user, performing occupation ranking according to the competence matching rate and the competence overflow rate, and obtaining user matching occupation according to the occupation ranking, the user value observation matching rate, the user interest occupation information and the user circle information;

s106, generating a growth plan of the user according to the matched occupation of the user through a processing module;

and S107, receiving the career planning report and the personal growth strategy report through the user terminal.

Further, the method further comprises S108, inputting test information of the tester through the information acquisition module;

and S109, calibrating a standard professional competence model base according to the test information of the tester through a model optimization module.

Further, the S106 specifically includes S1061, and after the user matching occupation is obtained, the user competence model is compared with the standard occupation competence model library to generate the first quadrant window.

Further, the S106 specifically includes S1062, and after the user competency model is obtained, a second quadrant window is generated according to 360 evaluation information in the user circle information.

Further, the S106 further includes S1063, after the matching career is obtained, extracting data in the career-university profession-subject library, and generating university profession information, university information, and university subject information adapted to the matching career of the user.

Further, S106 further includes S1064, after the matching career is obtained, extracting the comprehensive information of the career in the career information base, and receiving the comprehensive information of the career through the user terminal.

The invention has the following advantages:

the invention aims to provide an intelligent career planning and individual growth strategy making system in an online mode, the system can provide career planning and individual growth strategy making services for all user groups in the online mode, help teenagers form correct job cognition, and solve the problems that artificial career planning is difficult to popularize quickly and is easily influenced by artificial subjective factors, so that career planning and individual growth strategy making can be performed on users more systematically, scientifically and objectively; the problem of the prior art under the line profession planner manual work evaluate and plan profession development direction for the user, expensive, inefficiency, can't popularize fast and easily receive the influence of planner's subjective factor and self professional level, have certain planning deviation is solved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic diagram of an intelligent career planning and individual growth strategy making system in an embodiment of the invention;

FIG. 2 is a flow chart of an intelligent career planning method in an embodiment of the invention;

FIG. 3 is a flow chart of a method for intelligent career planning in an embodiment of the invention;

FIG. 4 is a flowchart of generating a growth plan for a user in an embodiment of the present invention;

FIG. 5 is a diagram illustrating a first window according to an embodiment of the present invention;

FIG. 6 is a diagram illustrating a second window according to an embodiment of the present invention.

Description of reference numerals:

the system comprises a first memory 10, a standard database 101, an information acquisition module 20, a second memory 30, a personal database 301, a processing module 40, a user terminal 50, a model optimization module 60, a first quadrant window 70 and a second quadrant window 80.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 1, an intelligent career planning and individual growth strategy making system includes:

a first memory storing a standard model database;

the second memory is electrically connected with the information acquisition module and is used for classifying the user information into a value model, a character model, a talent model, a circle model 60 and a character model of the user and storing the user information to form a personal database;

the processing module is electrically connected with the first memory and the second memory and is used for matching the user value view information in the personal database with the standard model database to generate a value view matching rate of a user and matching the user character information and the user talent information with the standard model database to generate a user competency model;

Personal value view and work view, personal interests, personal characters, personal talents and personal life circle are the most important 5 factors affecting life; establishing a standard model database of multiple standards by using comprehensive forms of big data, interview, investigation and the like, wherein the standard model database comprises all-dimensional databases of value view and working view information data _ JV, competence information data _ JA, a career information database data _ JI, an optimal development path database data _ JD and the like of each career, and synchronously establishing a database data _ JMS for matching college major, career and basic disciplines;

capturing information of personal value, personal interests, personal characters, personal talents, personal life circles and the like of a user through various online interaction forms, and establishing a personal database of the user;

the system generates personal competence information of the user through matching of a personal database of the user;

the system matches with the standard model database through the information of the personal value view, the working view, the personal interest, the personal competency model, the personal life circle and the like of the user to match the most suitable occupation for the user;

the system recommends the optimal personal promotion method and strategy for the user according to the difference between the personal competence model of the user and the standard model database;

the planning result comprises the contents of a user personal value view and a work view, personal interests, personal characters, personal talents, a personal competency model, a personal life circle, a job matching result, a job achievement path and the like, and is presented to the user through an online version report or a paper version report;

and after a large amount of user data is generated, continuously and perfectly calibrating the logic algorithm by adopting big data computing logic according to the user data. The method and the strategy can help the user to recommend the optimal occupation and achieve the optimal personal promotion method and strategy for the optimal occupation for the user matching.

As shown in fig. 2, an intelligent career planning method specifically includes:

s101, storing the standard model database into a standard model database;

in the step, storing a standard model database into a standard model database;

s102, acquiring user information;

in the step, user information is obtained through an information obtaining module;

s103, storing the user information into a personal database;

in the step, the user information is stored in a personal database;

s104, generating a user competence model;

in the step, matching user value view information in a personal database with the standard model database to generate a value view matching rate of a user, and matching the user character information and the user talent information with the standard model database to generate a user competence model;

s105, obtaining the matched occupation of the user;

comparing the user competence model with the standard occupation competence model library to generate a competence matching rate and a competence overflow rate of each occupation, performing occupation ranking according to the competence matching rate and the competence overflow rate, and obtaining a user matching occupation according to the occupation ranking, the user value observation matching rate, the user interest occupation information and the user circle information;

s106, generating a growth plan of the user;

in the step, a growth plan of the user is generated through a processing module according to the matched occupation of the user;

s107, the user terminal receives the report;

in this step, the career planning report and the personal growth strategy report are received through a user terminal.

As shown in fig. 3, the method further includes S108, inputting test information of the tester;

in the step, test information of a tester is input through an information acquisition module;

s109, calibrating a standard professional competence model;

in the step, a standard professional competence model base is calibrated according to the test information of the tester through a model optimization module.

As shown in fig. 4, the S106 specifically includes S1061, which generates a first quadrant window;

in the step, after the matched occupation of the user is obtained, the standard occupation competence model base is compared with the user competence model to generate a first quadrant window.

Further, the S106 specifically includes S1062, and a second quadrant window is generated;

in the step, after the user competency model is obtained, the second quadrant window is generated according to 360 evaluation information in the user circle information.

Further, the S106 further includes S1063, generating information adapted to the matching occupation of the user;

in this step, after the matched career is obtained, the data in the career-university specialty-subject library is extracted, and university professional information, university information, and university subject information matched with the career matched with the user are generated.

Further, S106 further includes S1064, where the ue receives the omnidirectional information;

in this step, after the matched occupation is obtained, the all-dimensional information of the occupation in the occupation information base is extracted, and the all-dimensional information of the occupation is received through the user terminal.

The user can input the personal value view and the working view of the user through the life wheel and the working view pyramid:

a. the life wheel, which establishes an ideal life model of a user, comprises: home modules VF (native home VF1, child VF2, companion VF3), social modules VS (human vein VS1, friendship VS2, dedication VS3), VJ cause modules (VJ1 money, VJ2 power, VJ3 reputation), VP personal modules (VP1 personal entertainment, VP2 personal health, VP3 personal growth). Through online interaction, the user is given 24 hours, and the user selects the allocated time in each module, so that the optimal life of the user is formed;

VF(VF1+VF2+VF3)+VS(VS1+VS2+VS3)+VJ(VJ1+VJ2+VJ3)+VP(VP1+VP2+VP3)＝24h

b. the working view pyramid is interacted with the user in an online mode in a pyramid sorting mode, and a working view model of the user is established, wherein the working view model comprises the following steps: VC1 material wealth, VC2 power, VC3 status, VC4 popularity, VC5 commercial value, VC6 aesthetic, VC7 moral standard, VC8 safety and stability, VC9 working environment, VC10 competitive atmosphere, VC11 working family balance, VC12 low pressure, VC13 challenging, VC14 independent and independent, VC15 interesting and changeable, VC16 creativity, VC17 devoting, and VC18 personal growth. Through online interaction, users are ordered in the value views, wherein VC (1-3 bits) is assigned to be 1, a value view model of work pursuit of the users is recorded, gains VJ1 → VC1, VJ2 → VC2, VJ3 → VC3, VC4, VF → VC11, VS3 → VC17 and VP3 → VC18 are set, the gain rule is that after the users select and complete VC (1-3 bits), if the gains are ranked three first in VJ1, VJ2, VJ3, VS3 and VP3, the gains are expanded to enter VC (1-3 bits), and VC (1-n bits) is synchronously assigned to be 1 and n is less than or equal to 6.

The interest data are: dream stations (more than 100, defined as J1-n, n is more than or equal to 100) such as J2 main edition, J10 product manager, J13 plane designer, J43 software engineer, J61 lawyer, J69 physician, etc., and the components of each station comprise a work scene, a representative character, a station brief introduction, etc. Through online interaction, a user selects the most expected position, the assigned position expected value I _ J (1-3 positions, the most preferred 3 positions of the user) is 2, I _ J (4-10 positions, the top 10 positions but not the top3 positions liked by the user) is 1, and I _ J (after 10 positions, the user does not select the liked position) is 0.

Matching database data _ JMS of profession and university profession: industry recruitment information and national-issued college professional guidance information are captured through big data, a professional and professional matching database is established, and unified codes are kept between the college professional database and a professional database issued by an education department, such as: 020101-economics specialty, 071101-psychology specialty, 080208-automotive services engineering specialty; professional matching models, for example, the P2 director matches 130101, 130301, 130302, 130303, 130304, 130305, 130306, 130307, 130308, 130310, 130311T.

Talent data, including:

a. intelligence quotient: TIQ1 is an observation force, TIQ2 is an understanding force, TIQ3 is an inference force, TIQ4 is a calculation force, TIQ5 is a language force, TIQ6 is a memory force, TIQ7 is a creativity force, TIQ8 is a space force, TIQ9 is an aesthetic force, and TIQ10 is a reaction force.

b. Affection business: TEQ1, TEQ2, TEQ3, infectivity, TEQ4, avidity, and TEQ5, excitability.

c. Inverse quotient Tianfu: TAQ1 is bearing force, TAQ2 is self-controlling force, TAQ3 is regulating force, TAQ4 is self-driving force, and TAQ5 is concentration force.

d. Body talent: TBQ1 is image, TBQ2 is physical, TBQ3 is visual, TBQ4 is hand-eye coordination, TBQ5 is will, TBQ6 is mental health.

e. The thinking way is as follows: TMT1 is a prospective-TMT 2-intuitive thinking, TMT3 is a systematic-TMT 4-divergent thinking, TMT5 is a linear-TMT 6-curvilinear-thinking, and TMT7 is an abstract-TMT 8-figurative thinking.

Through online interaction, the user's talent index is judged, different user talent indexes TIQ, TEQ, TAQ, TBQ and TMT are assigned as H, and H belongs to (0, 1 and 2), so different talent combinations: SUM (user personality combination) 3³³＝5.55×1015。

Personality data, including:

c1 pursued changes-C2 pursued stabilization, C3 pursued extremely-C4 pursued harmony, C5 exon-C6 converged, C7 initiative-C8 passive, C9 darted to-C10 prudent, C11 dominated-C12 obedience, C12 predominate-C12 consensus, C12 innovation-C12 tradition, C12 competition-C12 dispute, C12 responsibility-C12 relaxed, C12 practice-C12 flexible, C12 autonomy-C12 borrowing, C12 single stem-C12 cooperative, C12 criticism-C12 tolerant, C12 rational-C12 perceptual, C12 idiocraving-C12 cooperative, CP12 open (conservative) -CP 12 (open to human (CP) -CP 12 (conservative self-CP) alleviation, CP-CP self-CP-12 suspicion human, CP-12 subjective, CP-observation-CP 12 subjective and optimistic-CP-12. And judging the character index of the user through various online interaction forms.

And assigning Cn/CPn of different users as K, K belongs to (0, 1, 3), so that different character combinations: SUM (user personality combination) 3⁴⁴9.85 × 1020 species.

Life circle data:

defined as (E-LO loved, E-RO walk the way, E-BO read book) x (PA parental line, FA family line, SC campus line, CM corporate line, SO social line), 15 total circled areas:

a.E-LO-PA area, E-LO-PA1 is the parental desire and resource, E-LO-PA2 is the parental academic calendar, E-LO-PA3 is the parental personality and talent judgment assignment for the user, and 360 degree analysis is performed by f (E-LO-PA3) mapping → f (self-judgment) -360ia, and f (360ia) × 50% × f (E-LO-PA 3). E-LO-PA4 is parental, E-LO-PA5 is parental, E-LO-PA6 is native family income, E-LO-PA7 is native family hierarchy, E-LO-PA8 is parentage.

b.E-LO-FA area, E-LO-FA1 is important relative resource and expectation, E-LO-FA2 is important relative personality and talent judgment assignment for users, 360-degree analysis is carried out through f (E-LO-FA2) mapping → f (self judgment) -360ib, f (360ib) is 25% multiplied by f (E-LO-FA2), and E-LO-FA3 is important relative comprehensive evaluation for users;

c.E-LO-FA area, E-LO-FA3 is partner resource, E-LO-FA4 is partner value view, E-LO-FA5 is partner judgment assignment for user's character and talent, 360 degree analysis is carried out through f (E-LO-FA5) mapping → f (self judgment) -360ic, f (360ic) is 25% multiplied by f (E-LO-FA5), E-LO-FA6 is partner character, E-LO-FA7 is partner career, E-LO-FA8 is relation with partner;

d.E-LO-SC area, E-LO-SC1 is the teacher's personality and talent judgment assignment, and is mapped → f (self-judgment) through f (E-LO-SC1) to perform 360-degree analysis-360 id, f (360id) is 25% x f (E-LO-SC1), E-LO-SC3 is the teacher's personality, E-LO-SC4 is the teacher's comprehensive evaluation on the user, E-LO-SC5 is the teacher's value, E-LO-SC6 is the teacher's discipline;

e.E-LO-SC area, E-LO-SC2 is assigned to the personality and talent judgment of the user by classmate, and is analyzed 360 degrees by f (E-LO-SC2) mapping → f (self-judgment) -360ie, f (360ie) × 25% f (E-LO-SC2), E-LO-SC7 is the label of the user by classmate, E-LO-SC8 is the personality of the classmate, E-LO-SC9 is the comprehensive evaluation of the user by the classmate, E-LO-SC10 is the value view of the classmate, and E-LO-SC11 is the relationship between the classmate and the user.

f.E-LO-CM area, E-LO-CM1 is assigned to the personality of the user and the talent judgment, and is mapped → f (self-judgment) by f (E-LO-CM1) to perform 360-if analysis, f (360if) is 50% × f (E-LO-CM1), E-LO-CM3 is the value view of the supervisor, E-LO-CM4 is the management style of the supervisor, E-LO-CM5 is the general evaluation of the supervisor on my, E-LO-CM6 is the resource of the supervisor, and E-LO-CM7 is the personality of the supervisor.

g.E-LO-CM area, E-LO-CM2 is assigned to the personality and talent judgment of the user by colleagues and subordinate, 360-360 ig is analyzed by f (E-LO-CM2) mapping → f (self-judgment), f (360ig) × f (E-LO-CM2), E-LO-CM7 is the relationship between colleagues and users, E-LO-CM8 is the personality of colleagues, E-LO-CM9 is the value view of colleagues, E-LO-CM10 is the comprehensive evaluation of colleagues for users, and E-LO-CM11 is the label of colleagues for users.

h.E-LO-SO area, E-LO-SO 1-idol occupation, E-LO-SO 2-idol value view, E-LO-SO 3-dedication point, E-LO-SO 4-idol character view, E-LO-SO 5-user label for idol, E-LO-SO 6-friend character view, and assignment to user character view, E-LO-SO 7-friend character view, E-LO-SO 8-friend interest, E-LO-SO 9-friend label, E-LO-SO 10-friend value view, E-LO-SO 11-friend academic calendar, and E-LO-SO 12-friend social.

i.E-RO-PA area, E-RO-PA1 was a cause of family change.

j.E-RO-FA area, E-RO-FA1 ═ genetic history.

k.E-RO-SC zone, E-RO-SC1 is school achievement and frustration, E-RO-SC2 is learning burnout, E-RO-SC3 is school grade.

l.E-RO-CM area, E-RO-CM1 professional experience, E-RO-CM2 job satisfaction, E-RO-CM3 job burnout, E-RO-CM4 achievement and frustration in companies, E-RO-CM5 company grade.

m.E-RO-SO area, E-RO-SO1 is city level, E-RO-SO2 is economic level, E-RO-SO3 is frustration and achievement in society, and E-RO-SO4 is social milestone.

n.E-BO-PA area, E-BO-PA1, family fumigated pottery and enlightenment educational situations.

o.E-BO-FA region, E-BO-FA1 is an important relativistic educational impact.

p.E-BO-SC area, E-BO-SC1, academic case, E-BO-SC2, university specialty.

q.E-BO-CM region, E-BO-CM1 ═ professional skills.

r.E-BO-SO region, E-BO-SO1, E-BO-SO2, personal cognitive level, E-BO-SO3, information acquisition channel, E-BO-SO4, time shard.

We establish 17 kinds of competence model standard values A (standard) n according to the C/CP value of the character standard model and the TIQ value, the TEQ value, the TAQ value, the TBQ value and the TMT value of the talent standard model, and the following substitution by A _ standard _ n comprises:

defining:

α — a _ standard _ n is strongly associated, and is expressed as: C/CPn, TIQ, TEQ, TAQ, TMT ═ K, or H ═ 2;

β ═ a _ standard _ n weak association, expressed as: C/CPn, TIQ, TEQ, TAQ, TMT ═ K, or H ═ 1;

γ is a _ standard _ n negative association, which is expressed as: if the user's C/CPn, TIQ, TEQ, TAQ, TMT ═ K, or H ═ 2, let a _ standard _ n ═ a _ standard _ n-1 for the user;

δ is a _ standard _ n independent item, and the independent item does not enter into the operation and is expressed as: C/CPn, TIQ, TEQ, TAQ, TMT ═ K, or H ═ 0.

Competence formula:

a. the planning force As1 ═ fC/CP (α C2+ γ C9+ β C10+ α C11+ α C19+ α C29) × fT (α TIQ2+ β TIQ3+ α TIQ4+ α TIQ7+ α TMT1+ α TMT3)

b. Tissue force As2 ═ fC/CP (β C11+ α C19+ γ C23+ α C24+ α C29+ α C30+ α CP10) x fT (α TIQ2+ β TIQ3+ α TIQ4+ α TEQ1+ α TEQ2+ β TEQ5+ α TMT1+ α TMT3)

c. Decision force As3 ═ fC/CP (β C9+ γ C10+ α C11+ α C13+ α C19+ α C27+ α C29) × fT (α TIQ2+ α TIQ3+ TIQ10+ β TAQ1+ α TAQ2+ α TMT3)

d. Execution force As4 ═ fC/CP (α C3+ β C12+ α C17+ β C19+ γ C20) × fT (α TIQ2+ α TAQ5+ β TAQ1+ α TAQ2+ α TAQ3+ α TBQ5)

f. The driving force As5 ═ fC/CP (α C3+ α C7+ γ C8+ α C9+ α C17+ α C19+ α C21+ α CP7) × fT (α TAQ5+ α TAQ1+ β TAQ2+ α TAQ3+ β TAQ4+ α TBQ5)

g. Thinking As6 ═ fC/CP (α C3+ α C10+ α C13+ α C15+ γ C16+ α C27+ β C29+ α CP30) χ fT (α TIQ1+ β TIQ2+ β TIQ3+ β TIQ4+ α TIQ6+ α TIQ7+ α TIQ8+ α TIQ10+ α TEQ1+ α TEQ2+ β TAQ5, α TAQ2+ α TMT1+ α TMT3+ α TMT4+ α TMT6+ α TMT7+ α TMT8)

h. Learning force As7 ═ fC/CP (β C3+ α C13+ γ C16+ α C17+ α C19+ α C21+ α C29+ α CP1+ γ CP2+ α CP7) × fT (α TIQ1+ β TIQ2+ α TIQ3+ β TIQ6+ β TAQ5+ β TAQ2+ β TAQ4+ β TBQ5+ α TMT3+ α TMT4)

i. Inner labor saving As8 ═ fC/CP (alpha C3+ alpha C13+ gamma C20+ alpha C27+ alpha C29+ beta C31) x fT (alpha TIQ2+ beta TIQ3+ beta TAQ5+ alpha TAQ2+ beta TAQ4+ alpha TMT1+ alpha TMT3+ alpha TMT4)

g. Monitor As9 ═ fC/CP (α C10+ α C16+ α C19+ γ C20+ β C27+ γ C28+ β C29+ α CP9+ γ CP10) x fT (β TIQ1+ α TIQ2+ α TIQ3+ α TIQ6+ α TAQ5+ α TAQ2+ α TMT1)

k. Anti-stress As10 ═ fC/CP (β C19+ α C21+ α C29+ α CP5+ γ CP6+ γ CP11+ α CP12) x fT (β TAQ1+ β TAQ2+ β TAQ3+ β TAQ4+ β Ter5)

Strain As11 ═ fC/CP (α C1+ α C9+ α C15+ γ C16+ α C19+ β C22+ α C29) × fT (α TIQ2+ α TIQ3+ α TIQ7+ β TIQ10+ β TAQ1+ β TAQ2)

Adaptive As12 ═ fC/CP (α C7+ α C19+ α C20 α + C22+ α C28+ α CP1+ γ CP2+ α CP3+ γ CP4+ α CP8+ α CP12) χ fT (β TAQ1+ α TAQ2+ β TAQ3+ α TAQ4+ α TBQ5)

Daddy As13 ═ fC/CP (γ C1+ α C2+ β C16+ α C19+ β C30+ γ CP7) xft (α TEQ1+ α TEQ2+ β TAQ2)

o. innovative As14 ═ fC/CP (α C1+ γ C2+ α C3+ α C13+ β C15+ γ C16+ α C29+ α C30+ α CP1+ γ CP2) xft (β TIQ7+ β TIQ8+ β TIQ9+ α TAQ5+ α TMT4)

p. social force As15 ═ fC/CP (α C5+ γ C6+ α C7+ α C14+ α C22+ α C30+ γ C31+ β C32+ α CP3+ γ CP4+ α CP5+ γ CP6) χ fT (α TIQ5+ β TEQ1+ β TEQ2+ β TEQ4+ α TAQ1+ α TAQ2+ α TAQ3+ α TMT6)

q. expression As16 ═ fC/CP (α C5+ α C11+ α C22+ α C29+ β C30+ α CP5+ γ CP6) χ fT (α TIQ2+ α TIQ3+ β TIQ5+ α TIQ10+ β TEQ1+ β TEQ2+ β TEQ3+ β TEQ5+ α TAQ1+ α TAQ2+ α TMT6)

The cooperative force As17 ═ fC/CP (γ C11+ α C14+ α C24+ γ C25+ β C26+ α C28+ α C30+ γ C31+ α C32+ α CP10) × fT (α TEQ1+ α TEQ2+ α TEQ4+ α TAQ1+ α TAQ2+ α TAQ 3).

After online interaction, capturing character model variables C/CPn and talent model variables TIQ, TEQ, TAQ and TMT of the user, and forming 17 types of competence indexes A (user) n of the user according to the formula, hereinafter referred to as A _ user _ n.

In the above models, the fC/CP function and the fT function both adopt probability deduction models, and according to research and big data, the single variable C/CPn in the personality model and the single variables TIQ, TEQ, TAQ, and TBQ in the talent model are pyramid probability distribution in the population, where P (K or H ═ 2) is approximately 20%, P (K or H ═ 1) is approximately 30%, and P (K or H ═ 0) is approximately 50%; the natural model variable TMT is in uniform probability distribution, P (K or H ═ 1 or 2) is approximately equal to 50%, and P (K or H ═ 0) is approximately equal to 50%; and the character model overall variable fC/CP and the Tian assigned model overall variable fT are in normal distribution probability in the population, P (overall high score) is approximately equal to 20%, P (overall medium score) is approximately equal to 50%, and P (overall low score) is approximately equal to 30%, so that the rule of the fC/CP function and the fT function is defined as follows:

compared with the standard value of standard _ fC/CP and fT of each competence,

if the User _ fC/CP and the fT User value probability belong to standard _ fC/CP and fT standard values of 20% of the head, assigning 2 to the User _ fC/CP and fT of the User;

if the User _ fC/CP and fT User value probability belongs to standard values of standard _ fC/CP and fT of 20% -middle part 70% of the head part, assigning the User _ fC/CP and fT of the User to be 1;

if the User _ fC/CP and the fT User value probability belong to standard _ fC/CP and fT standard values with 30% of tail, assigning the User _ fC/CP and fT of the User to be 0;

for example:

the planned force criterion As1 ═ fC/CP (α C2+ γ C9+ β C10+ α C11+ α C19+ α C29) × fT (α TIQ2+ β TIQ3+ α TIQ4+ α TIQ7+ α TMT1+ α TMT3),

planned force user value Au1 ═ fC/CP (1+1+2+0+0+0) × fT (0+2+2+1+ 1) ═ 1 × 2 ═ 2

Through big data and model calculation, the probability of competence A _ standard _ n in the steps is distributed in a pyramid shape, P (first level-top) is approximately equal to 5%, P (second level-excellent) is approximately equal to 20%, P (third level-good) is approximately equal to 25%, and P (fourth level-common) is approximately equal to 50%; we define a _ standard _ n as four grades and assign a _ standard _ n top to 4, a _ standard _ n to excel to 2, a _ standard _ n to good to 1, a _ standard _ n to ordinary to 0.

A standard competency database data _ JA for more than 100 occupations is established by using comprehensive forms of big data, interviews, research and the like according to a competency model, a body talent model and the like.

For example:

j10 Standard value of product manager: j10_ As1 ═ 2, J10_ As2 ═ 4, J10_ As3 ═ 4, J10_ As4 ═ 2, J10_ As5 ═ 2, J10_ As6 ═ 4, J10_ As7 ═ 4, J10_ As8 ═ 1, J10_ As9 ═ 2, J10_ As10 ═ 2, J10_ As11 ═ 2, J10_ As12 ═ 2, J10_ As13 ═ 1, J10_ As14 ═ 4, J10_ As15 ═ 2, J10_ As16 ═ 2, J10_ As17 ═ 4; TBQ1 ═ 0, TBQ2 ═ 0, TBQ3 ═ 0, and TBQ1 ═ 0.

J62 pilot standard: j62_ As1 is 0, J62_ As2 is 0, J62_ As3 is 4, J62_ As4 is 2, J62_ As5 is 2, J62_ As6 is 2, J62_ As7 is 2, J62_ As8 is 0, J62_ As9 is 2, J62_ As10 is 2, J62_ As11 is 2, J62_ As12 is 1, J62_ As13 is 1, J62_ As14 is 0, J62_ As15 is 0, J62_ As16 is 0, J62_ As17 is 2; TBQ1 ═ 0, TBQ2 ═ 0, TBQ3 ═ 2, and TBQ1 ═ 1.

Determining the competence index matching rate Match Ratio (AMR value) and overflow Ratio Spillover Ratio (ASR value) of the user in each occupation by taking the competence index of the user as a reference:

firstly, for each competence matching rate AMR-An, firstly judging whether each competence of the user exceeds a standard value, if so, the matching rate is equal to the maximum value 100%. If the logic is If a _ user _ n/Jx _ a _ standard _ n > 1, then let a _ user _ n/Jx _ a _ standard _ n be 1, otherwise let a _ user _ n/Jx _ a _ standard _ n be a _ user _ n/Jx _ a _ standard _ n;

AMR is the mean value of 17 items of competence matching rates, and the logic is

And thirdly, judging whether each item of competence of the user overflows or not for the overflow rate ASR _ An of each item of competence, if the overflow condition exists, calculating the overflow rate, wherein the ASR _ An of the competence which does not overflow is 0, and the overflow rate is 0 by default. The logic is if a _ user _ n > Jx _ a _ standard _ n, then a _ user _ n-Jx _ a _ standard _ n is a _ user _ n-Jx _ a _ standard _ n, else a _ user _ n-Jx _ a _ standard _ n is 0,

fourthly, the total competence overflow rate ASR is the average value of the 17 competence overflow rates,

and fifthly, the occupational matching rate AMR of the TBQ exists, the corresponding TBQ is added according to the formula for calculation, and the TBQ only influences the AMR and does not influence the ASR.

The AMR value is a first judgment sequence, the ASR value is a second judgment sequence, A _ J (1-3 bits) is assigned to be 2, A _ J (4-10 bits, suitable occupation of a user) is assigned to be 1, and A _ J (after 10 bits) is assigned to be 0. If the third bit or the tenth bit is parallel, the value of the observation matching rate VMR is selected to be higher.

For example:

j10 Standard value of product manager: j10_ As1 ═ 2, J10_ As2 ═ 4, J10_ As3 ═ 4, J10_ As4 ═ 2, J10_ As5 ═ 2, J10_ As6 ═ 4, J10_ As7 ═ 4, J10_ As8 ═ 1, J10_ As9 ═ 2, J10_ As10 ═ 2, J10_ As11 ═ 2, J10_ As12 ═ 2, J10_ As13 ═ 1, J10_ As14 ═ 4, J10_ As15 ═ 2, J10_ As16 ═ 2, J10_ As17 ═ 4;

and (4) user results: au1 ═ 1, Au2 ═ 2, Au3 ═ 4, Au4 ═ 4, Au5 ═ 4, Au6 ═ 2, Au7 ═ 2, Au8 ═ 1, Au9 ═ 1, Au10 ═ 1, Au11 ═ 2, Au12 ═ 2, Au13 ═ 4, Au14 ═ 2, Au15 ═ 1, Au16 ═ 1, Au17 ═ 2;

AMR(J1)＝70.59％，

ASR(J1)＝29.41％；

user occupation matching sorting rule:

and sequencing according to an interest and competency ranking rule to determine the most matched occupation of the user, wherein the logic is as follows:

if I _ Jn + A _ Jn is 4, the then Jn is the best matching career and is preferentially recommended to the user, if the scores are the same, the final sorting is determined according to the competence ranking, and if the competence ranking is the same, the sorting is performed according to the interest and value view matching sequence;

and if I _ Jn + A _ Jn is 3, the then Jn is a matching career and is recommended to a user, if scores are the same, final sequencing is determined according to competence ranking, and if the competence ranking is the same, sequencing is performed according to interest and value view matching sequence;

and if I _ Jn + A _ Jn is 2, the then Jn is an unmatched job and is not recommended, and the user is guided to select a job according to competency and then carries out job recommendation according to A _ J ranking;

and if I _ Jn + A _ Jn is 1, the then Jn is a very unmatched job and is not recommended, and the user is guided to select a job according to competency and then carries out job recommendation according to A _ J ranking;

if I _ Jn + A _ Jn is 0, then Jn is an extremely unmatched job and is not recommended, and the user is guided to select a job according to competency and then carries out job recommendation according to A _ J ranking;

so as to obtain the occupation J (MATCH) which is best matched with the user;

and (3) judging the value visual index matching rate Match Ratio (VMR value) of the user in each occupation by taking the value visual index of the user as a reference, wherein data _ JV is a working visual standard value of each occupation obtained by investigation and big data, and VCuser is a captured user value.

Firstly, judging whether the value view selected by the user is matched with the standard value view of the profession, if so, carrying out subsequent calculation, if not, the term VMR _ VCn is 0, the logic is if VCuser _ n is Jx _ Vcstandard _ n and VCuser _ n is more than 0, the n is assigned with VCuser _ n/Jx _ VCstandard _ n is 1,

the total value view matching rate VMR is the ratio of the number of the users matched with the career standard value view to the total career standard value view, and the logic is

For example:

j20 strategic planner values of job sight: j20_ VCstandard _1 is 1, J20_ VCstandard _2 is 0, J20_ VCstandard _3 is 1, J20_ VCstandard _4 is 1, J20_ VCstandard _5 is 1, J20_ VCstandard _6 is 0, J20_ VCstandard _7 is 0, J20_ VCstandard _8 is 0, J20_ VCstandard _9 is 1, J20_ VCstandard _10 is 0, J20_ VCstandard _11 is 1, J20_ VCstandard _12 is 0, J20_ VCstandard _13 is 1, J20_ VCstandard _14 is 0, J20_ VCstandard _1, J5816 _ VCstandard _1, J7316 _ VCstandard _ 1;

and (4) user results: VCuser _1 is 1, VCuser _2 is 0, VCuser _3 is 0, VCuser _4 is 0, VCuser _5 is 0, VCuser _6 is 0, VCuser _7 is 0, VCuser _8 is 0, VCuser _9 is 0, VCuser _10 is 0, VCuser _11 is 1, VCuser _12 is 0, VCuser _13 is 0, VCuser _14 is 0, VCuser _15 is 0, VCuser _16 is 0, VCuser _17 is 1, and VCuser _18 is 0;

VMR(J20)＝12.5％；

in the life circle of the user, the important intersection of the user and the user are interacted on line to obtain a complete 360-degree evaluation of the user, which is defined as 360-degree index 360i,

f (360i) ═ f (360ia +360ib +360ic +360id +360ie +360if +360ig), if the user skips 360 evaluation links, the module does not enter into operation, if some important intersection persons do not complete interaction, the completing persons expand to 100% according to equal proportion to operate, if the weight addition of 360i is greater than 100%, the equal proportion is reduced to 100% to execute operation.

In the user life circle, the parental expectations/resources E-LO-PA1 data have been captured, and if j (match) contains this occupation, special notes are added.

In the user life circle, the E-BO-SC1 data, which is good at subject conditions, is captured, and the E-BO-SC1 data is associated with the latest data of the education department so as to give the optimized college entrance strategy to some users. For example: user J (match) contains J29 chemists, who advises university specialties as chemistry specialties, applied chemistry specialties, etc., college subject must select chemistry.

In the life circle of the user, the read book E-BO-SO1 data is captured, the data _ JB database is matched, for example, E-BO-SO 1-relativistic theory-generates a forward gain when the user selects related professions (professions such as physicists and mechanical engineers), and TOP3 selected by the book is uniformly counted into the personal interest model of the user (the total number of I _ J-2 is less than or equal to 13).

(1) Establishing a career database and a career development path for each career:

a. occupation information base data _ JI: through the ways of big data capture, investigation and the like, an all-round database of video introduction, occupation description, daily average salary, salary change within 5 years, working time, satisfaction degree, working pressure, nationwide distribution condition, required enterprises of the occupation, top-level big coffee of the occupation and the like is established, so that a user can have all-round deep knowledge of a target occupation after J (MATCH) is selected.

b. Professional development path data _ JD: through the ways of big data capture, investigation and the like, the optimal development path of each profession is generated, the optimal development path comprises university selection, professional selection, certificates needing to be acquired, professional knowledge needing to be accumulated, required practice experience or overseas experience and the like, and the optimal development path helps a user to define the future profession development strategy after J (MATCH) is selected.

(2) Establishing a promotion strategy for each category of competency:

as shown in fig. 5, the processing module compares the standard occupation competence model library with the user competence model to generate a first quadrant window;

as shown in fig. 6, the user circle information further includes 360 evaluation information, and the processing module generates a second quadrant window according to the 360 evaluation information in the user circle information after obtaining the user competence model.

After selecting J (MATCH), the user compares A _ user _ n of the grabbing user with a standard value A _ standard _ n, and forms a user competence quadrant window, wherein the horizontal axis is A _ user _ n, the vertical axis is A _ standard _ n, the 1 st quadrant is an area with A _ standard _ n high and A _ user _ n high, and the competence area is named; the 2 nd quadrant is an A _ standard _ n high and A _ user _ n low area, and a development area is named; the 3 rd quadrant is an A _ standard _ n low and A _ user _ n low area, and a named neglected area; the 4 th quadrant is an A _ standard _ n low and A _ user _ n high area, and an overflow area is named; and preferentially recommending the promotion strategy and the refined knowledge of the user about the competency group aiming at the competency group of the 2 nd quadrant development area.

Simultaneously, capturing A _360_ n data from the 360i result to form a competence force 360 quadrant window of the user, wherein the horizontal axis is A _ user _ n, the vertical axis is A _360_ n, the 1 st quadrant is an area with the height of A _360_ n and the height of A _ user _ n, and a superiority consensus area is named; the 2 nd quadrant is an A _360_ n high and A _ user _ n low area, and a potential area is named; the 3 rd quadrant is an A _360_ n low and A _ user _ n low area, and a disadvantage consensus area is named; the 4 th quadrant is an A _360_ n low and A _ user _ n high region, named dead zone; and respectively recommending the promotion strategy and the refined knowledge of the user about the capability group aiming at the competency of the 2/3/4 quadrant potential area.

(1) After the expert or elite talent in the industry finishes evaluation, data of the expert or elite talent is captured, and a data _ JA database is calibrated by adopting a dynamic minimum discrete regression algorithm:

defining a standard value of original capacity of a certain professional as Jx _ An _ original;

defining the original ability value of a certain ability of a certain occupation currently used as Jx _ An _ now and the standard deviation as Jx _ sigma An _ now;

the average value of certain abilities of a sample size total (1-100) of 100 industrial experts and elite talents before a certain occupation is Jx _ An _100, and the standard deviation is Jx _ sigma An _ 100;

the average value of certain ability of a certain occupational n multiplied by 100 sample size population (1-k multiplied by 100) is Jx _ An _ k100, and the standard deviation is Jx _ sigma An _ k 100;

and (3) dynamic adjustment for the first time: if Jx _ An _100-Jx _ An _ original > 0.7, the ability value is added to the next gear on the basis of Jx _ An _ original, and if Jx _ An _100-Jx _ An _ original < -0.7, the ability value is subtracted from the previous gear on the basis of A1;

secondly, subsequent dynamic adjustment:

firstly, judging whether the data group n has smaller dispersion degree than the currently used data, wherein the logic is if Jx _ sigma An _ k100 < Jx _ sigma An _ now, if so, judging that the capability value is added to the next grade on the basis of A1 if Jx _ An _ k100-Jx _ An _ now is more than 0.7, and if Jx _ An _ k100-Jx _ An _ now is less than-0.7, the capability value is reduced to the previous grade on the basis of A1;

if Jx _ σ An _ k100 is less than or equal to Jx _ σ An _ now, no adjustment is made.

Further, the step S106 further includes, S1062, generating a personal developmental strategy;

in this step, according to the professional development data, the personal development data, the value view matching rate, the expected position and the position competence value job matching result, the analysis unit obtains a professional development path, a selection suggestion and a promotion suggestion of the user according to the professional matching result, and the processor generates a personal development strategy from the professional development path, the selection suggestion and the promotion suggestion.

A computing device comprising a memory, a processor and computer instructions stored on the memory and executable on the processor, the processor when executing the instructions implementing the steps of the method of any of claims 3 to 9.

The use process of the intelligent career planning and individual growth strategy making system is as follows:

when in use, the standard model database is stored in the standard model database; acquiring user information through an information acquisition module; storing the user information to a personal database; matching user value view information in a personal database with the standard model database to generate a value view matching rate of a user, and matching the user character information and the user talent information with the standard model database to generate a user competency model; comparing the user competence model with the standard occupation competence model library to generate a competence matching rate and a competence overflow rate of each occupation, performing occupation ranking according to the competence matching rate and the competence overflow rate, and obtaining a user matching occupation according to the occupation ranking, the user value observation matching rate, the user interest occupation information and the user circle information; generating a growth plan of the user according to the matched occupation of the user through a processing module; and receiving the career planning report and the personal growth strategy report through a user terminal.

In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include more than one of the feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. Furthermore, the terms "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. The utility model provides an intelligence career planning and individual growth strategy system, its characterized in that includes:

a first memory storing a standard model database;

2. The intelligent career planning and individual growth strategy formulation system of claim 1, wherein the standard model database comprises a career value view model library, a competency-personality model library, a career competency model library, a career information library, and a career-university profession-science library;

3. The intelligent career planning and personal growth strategy making system according to claim 1, characterized in that the intelligent career planning and personal growth strategy making system further comprises a model optimization module electrically connected to the information acquisition module and the processing module, wherein the information acquisition module is configured to input test information of a tester, and calibrate the standard career competency model base according to the test information through a dynamic minimum discrete regression algorithm.

4. The intelligent career planning and personal growth strategy making system of claim 1, wherein the processing module compares the user competence model with a standard career competence model library to generate a first quadrant window;

5. An intelligent career planning method is characterized by specifically comprising the following steps:

s101, storing a standard model database into a standard model database;

s102, acquiring user information through an information acquisition module;

s103, storing the user information into a personal database;

s106, generating a growth plan of the user according to the matched occupation of the user through a processing module; a

6. The intelligent career planning method of claim 5, further comprising S108, inputting test information of a tester through an information acquisition module;

7. The intelligent career planning method of claim 5, wherein the step S106 specifically comprises the step S1061 of generating the first quadrant window by comparing the user competence model with a standard career competence model library after the matched careers of the user are obtained.

8. The intelligent career planning method of claim 7, wherein the S106 specifically includes S1062, and after the user competency model is obtained, the second quadrant window is generated according to 360 evaluation information in the user circle information.

9. The intelligent career planning method of claim 8, wherein the S106 further comprises S1063, wherein after the matching careers are obtained, data in the career-university profession-subject library is extracted, and college professional information, college information and college subject information matched with the matching careers of the user are generated.

10. The intelligent career planning method of claim 9, wherein the S106 further comprises S1064, after the matching career is obtained, extracting the comprehensive information of the career in the career information base, and receiving the comprehensive information of the career through a user terminal.