CN116433201A

CN116433201A - Talent resource information intelligent matching system and method based on big data

Info

Publication number: CN116433201A
Application number: CN202310421280.2A
Authority: CN
Inventors: 陈基伟; 曹志华
Original assignee: Shenzhen Qianhaicube Information Technology Co ltd
Current assignee: Shenzhen Qianhaicube Information Technology Co ltd
Priority date: 2023-04-19
Filing date: 2023-04-19
Publication date: 2023-07-14
Anticipated expiration: 2043-04-19
Also published as: CN116433201B

Abstract

The invention discloses a talent resource information intelligent matching system and method based on big data, and belongs to the technical field of talent resource information processing. The invention comprises the following steps: step one: predicting recruitment requirements of recruitment enterprises at each delayed time point; step two: screening talent information in a talent library based on the acquired related professional names and recruitment professional names; step three: secondarily screening talent information; step four: and according to the constructed talent map, intelligent matching is carried out on talents, and matched talent information is sent to an enterprise recruitment terminal. According to the constructed talent map, the talents with high adaptation degree to the recruitment enterprises are determined at the corresponding delay time points, and in the process, the talents with high adaptation degree to the recruitment enterprises can be continuously adjusted according to the delay time, so that prospective recruitment plans are formulated for the recruitment enterprises in real time.

Description

Talent resource information intelligent matching system and method based on big data

Technical Field

The invention relates to the technical field of talent resource information processing, in particular to a talent resource information intelligent matching system and method based on big data.

Background

With the rapid development of the mobile internet, internet recruitment websites are vigorous, each recruitment website can realize real-time update of talent information and recruitment information, and meanwhile, real-time communication between job seekers and enterprises can be realized, interaction of demands of the two parties is performed, and rapid matching and butt joint between the job seekers and the enterprises are realized.

At present, internet recruitment websites recommend high-quality job seekers for enterprises by matching recruitment information of the enterprises with job seekers' job seekers, but some enterprises can only issue recruitment plans when urgent talents are needed, and the recruitment process is long, so that the projects to be carried out by the recruited enterprises cannot be carried out on time, or the recruited talents cannot take on corresponding responsibilities, which is not beneficial to intelligent matching of talent resource information, and some enterprises often recruit some job seekers before the opening of the enterprise projects, but the talent images of the job seekers cannot be clearly seen in the recruitment process, and the job seekers cannot know specific responsibilities needed to be carried in the projects, so that the development of the enterprise projects is difficult.

Disclosure of Invention

The invention aims to provide a talent resource information intelligent matching system and method based on big data, so as to solve the problems in the background technology.

In order to solve the technical problems, the invention provides the following technical scheme: a talent resource information intelligent matching method based on big data, the method comprises:

step one: determining recruitment time periods of recruitment enterprises according to the development scale, development movement direction and incumbent personnel information of the enterprises, and predicting recruitment requirements of the recruitment enterprises at each delay time point based on the determined recruitment time periods, recruitment delay time of the recruitment enterprises and change conditions of personnel of the recruitment enterprises in the delay time, wherein the recruitment delay time refers to the time length from the predicted recruitment starting time point;

step two: acquiring recruitment professional names corresponding to the recruitment businesses according to the recruitment businesses of the recruitment businesses, determining professional ability required to be mastered by the professions based on the acquired recruitment professional names, acquiring related professional names according to a determination result, and screening talent information in a talent base based on the acquired related professional names and the recruitment professional names;

step three: acquiring professional ability and mastering degree of each talent according to talent information screened in the second step, determining time for each talent to finish a culture plan by combining learning ability corresponding to each talent, and secondarily screening talent information by combining pressure conditions born by each talent when learning professional knowledge;

Step four: based on the recruitment requirements of the recruitment enterprises corresponding to the recruitment time determined in the first step and the talent growth progress determined in the third step, a talent map is constructed, intelligent matching is conducted on talents according to the constructed talent map, and matched talent information is sent to the enterprise recruitment end.

Further, the first step includes:

s11: judging whether the enterprise can autonomously complete the project to be developed of the enterprise under the existing development scale and the existing personnel according to the development trend of the enterprise, and if the enterprise cannot autonomously complete the project to be developed of the enterprise, determining that the enterprise is a recruitment enterprise;

s12: determining recruitment positions of recruitment enterprises according to division conditions of existing personnel of the recruitment enterprises and demand conditions of to-be-developed projects of the enterprises, determining recruitment starting time points of the recruitment enterprises according to culture plans of the recruitment enterprises for the corresponding position talents and starting time of to-be-developed projects of the enterprises, and predicting recruitment time periods of the recruitment enterprises by combining latest time of the corresponding talents in the to-be-developed projects of the recruitment enterprises, wherein the talent culture plans refer to all culture flows required to be experienced by the recruiters under the condition of no related work experience;

S13: and (3) determining recruitment delay time of the recruitment enterprise according to the recruitment time period of the recruitment enterprise predicted in the step (S12), and predicting recruitment requirements of the recruitment enterprise at each delay time point by combining the change condition of personnel of the recruitment enterprise in the delay time.

Further, the specific method for predicting the recruitment requirement of the recruitment enterprise at each delayed time point in S13 includes:

when the recruitment enterprise has personnel change in the delay time, the recruitment job determined in the S12 is adjusted according to the association condition of the change personnel and the project to be developed of the recruitment enterprise, and when the recruitment enterprise has no personnel change in the delay time, the adjusted recruitment job is still the recruitment job determined in the S12;

acquiring professional capacities of recruitment staff under the staff behaviors, and determining culture time corresponding to each professional capacity according to the acquired professional capacities;

determining the shortened cultivation time in the cultivation time corresponding to each professional ability according to the importance degree corresponding to each professional ability, determining the standard recruitment delay time allowed by the recruitment enterprise according to the shortened cultivation time value, and determining the recruitment requirement of the recruitment enterprise as the recruitment basic requirement within the standard recruitment delay time allowed by the recruitment enterprise, wherein the recruitment basic requirement refers to that recruiters accord with the recruitment profession but have no relevant work experience;

Calculating a difference value between the actual recruitment delay time and the standard recruitment delay time corresponding to the recruitment enterprise, if the difference value is a positive value, indicating that the recruitment requirement of the recruitment enterprise is changed, predicting the recruitment requirement of the recruitment enterprise at the moment, otherwise, indicating that the recruitment requirement of the recruitment enterprise is still a recruitment basic requirement;

and V, predicting recruitment requirements of recruitment enterprises in the IV, wherein a specific prediction formula Q is as follows:

wherein p=1, 2, …, q represents the number corresponding to the professional ability, the number sequence is the arrangement sequence of various types of responsibilities from small to large according to the importance degree, q represents the total number of types included by the professional ability, m represents the total number of types of responsibilities, j=0.1, 0.2, …, n represents the required degree corresponding to each professional ability, n=1, t represents the difference between the actual recruitment delay time and the standard recruitment delay time, and K _p ^j Representing a shortening of the incubation time corresponding to the professional ability numbered p when the required degree is j, Q representing a delay time difference, when Q > 0, representing that the predicted recruitment requirement does not conform to the actual recruitment requirement at the corresponding delay time point, when q=0, representing that the predicted recruitment requirement conforms to the actual recruitment requirement at the corresponding delay time point, and when Q < 0, representing that the predicted recruitment requirement can continue to delay Q time within the corresponding delay time;

And when Q is more than or equal to 0, acquiring the highest requirement degree corresponding to the professional ability with the number p, wherein the predicted recruitment requirement is the highest requirement degree corresponding to the professional ability with the number p.

Further, the third step includes:

s31: according to talent information screened in the second step, acquiring professional ability and mastering degree mastered by each talent, and determining time for each talent to finish a culture plan by combining learning ability of each talent on various professions, wherein a specific determination formula W is as follows:

wherein S is _p Represent the mastery degree of talents corresponding to the professional ability with the number p, K _p Representing the learning ability of talents on professional ability with the number p, wherein W represents the time of completing the culture plan after the recruitment time is delayed by u, u represents the actual recruitment delay time, if W is larger than R-u, the talents cannot complete the culture plan after the recruitment time is delayed by u, if W is smaller than or equal to R-u, the talents can complete the culture plan after the recruitment time is delayed by u, and R represents the recruitment duration corresponding to the recruitment enterprise determined in S12;

acquiring talents which cannot finish the culture plan after the recruitment time is delayed u, and removing the acquired talent information from the talent information screened in the second step;

S32: according to the formula y=max { [ (1-S) _p )/K _p ]/B _p Maximum compression resistance to talents when learning expert knowledgePredicting a value, wherein B _p Represents the standard time required for talents to fully grasp the professional ability numbered p,

[(1-S _p )/K _p ]/B _p indicating talents in (1-S) _p )/K _p The compressive capacity required to be born when the professional capacity with the number p is completely mastered in time, and Y represents the maximum compressive capacity value required to be born when the professional capacity required by recruitment enterprises is completely mastered in a culture plan;

if Y is greater than the maximum compression resistance of talents, the corresponding talent information needs to be continuously removed from the residual talent information removed in S31, otherwise, the corresponding talent information is reserved from the residual talent information removed in S31.

Further, the fourth step includes:

s41: acquiring recruitment requirements of the recruitment enterprises determined in the step one at each delay time point, taking unit time as unit length, taking the recruitment requirements as deflection angles, taking a planning endpoint in the recruitment enterprise cultivation plan as a deflection angle change point to construct a recruiter talent map of the recruitment enterprises, wherein the default deflection angle is 0 degree in the standard delay time, and H _p ＝F _p * Pi, where F _p Represents the mastery degree required by recruiting enterprises on professional ability with the number p, H _p A deflection angle corresponding to a recruitment request of a recruiter for the professional ability with the number p is expressed, and a planning endpoint refers to a time point corresponding to the start of the cultivation plan (for example, a cultivation time range of the recruiter for the professional ability with the number 2 is a-b, a cultivation time range of the recruiter for the professional ability with the number 3 is b-c, and a time point a and a time point b are planning endpoints);

s42: acquiring time for reserving talents to complete a culture plan according to talent information reserved in S32, determining delay time of each talent according to the acquired information, and completing corresponding culture plan with talents (for example, recruiting enterprises with the culture time range of professional ability with the number of 2 being a-b, recruiting enterprises with the culture time range of professional ability with the number of 3 being b-c, and completing corresponding with talents at time point b and time point cTime point of the cultivation plan) and time point of talents starting the cultivation plan are deflection angle change points, a talent map is constructed with a unit time as a unit length by taking the degree of talents meeting recruitment requirements as deflection angles, the default deflection angle is 0 DEG in the standard delay time,

G _p The deflection angle corresponding to the degree that the talent grasps the professional ability with the number p and meets the recruitment requirement of the recruitment enterprise is represented;

s43: acquiring deflection angles corresponding to recruitment requirements on a recruiter talent map according to recruitment delay time, acquiring deflection angles corresponding to the degree that each talent meets the corresponding recruitment requirements on the talent map according to the recruitment delay time and the recruitment requirements corresponding to the recruitment delay time, determining talents according to the acquired deflection angles and average coincidence degrees of the deflection angles corresponding to the delay time points, and sending talent information matched with the determined talents to an enterprise recruitment end at the corresponding delay time points or before the corresponding delay time points,

when the average contact ratio is more than or equal to X, the corresponding person can meet the recruitment requirement of the recruitment enterprise, otherwise, the corresponding person cannot meet the recruitment requirement of the recruitment enterprise, and X is more than 0.7 and less than or equal to 1.

The system comprises an enterprise recruitment time period determining module, an enterprise recruitment requirement predicting module, a talent screening module, a talent determining module and a talent intelligent matching module;

the enterprise recruitment time period determining module is used for determining recruitment time periods of recruitment enterprises according to the development scale, development movement direction and incumbent personnel information of the enterprises, and transmitting the recruitment enterprises and the recruitment time periods corresponding to the recruitment enterprises to the enterprise recruitment requirement prediction module;

The enterprise recruitment requirement prediction module is used for receiving recruitment enterprises and recruitment time periods corresponding to the recruitment enterprises, wherein the recruitment enterprises and the recruitment time periods correspond to the recruitment enterprises are transmitted by the enterprise recruitment time period determination module, determining recruitment delay time of the recruitment enterprises according to receiving information, predicting recruitment requirements of the recruitment enterprises at each delay time point by combining the change condition of personnel of the recruitment enterprises in the delay time, and transmitting the predicted recruitment requirements to the talent intelligent matching module;

the talent screening module is used for acquiring recruitment professional names corresponding to recruitment businesses according to recruitment businesses of the recruitment businesses, determining professional ability required to be mastered by the professions based on the acquired recruitment professional names, acquiring related professional names according to determination results, screening talent information in a talent library based on the acquired related professional names and the recruitment professional names, and transmitting the screened talent information to the talent determining module;

the talent determining module receives talent information transmitted by the talent screening module, acquires professional ability and mastery degree mastered by each talent according to the received information, determines time for each talent to finish a culture plan by combining learning ability corresponding to each talent, performs secondary screening on talent information by combining pressure conditions born by each talent when learning professional knowledge, and transmits time for each talent after secondary screening to the talent intelligent matching module;

The talent intelligent matching module receives the time of the completion of the culture plan of the secondarily screened talents transmitted by the talent determining module and the recruitment requirements of the recruitment enterprises at each delay time point transmitted by the enterprise recruitment requirement predicting module, constructs a talent map according to the received information, carries out intelligent matching on the talents according to the constructed talent map, and transmits the matched talent information to the enterprise recruitment end.

Further, the enterprise recruitment time period determining module comprises a recruitment enterprise determining unit and a recruitment time period determining unit;

the recruitment enterprise determining unit judges whether the enterprise can autonomously complete a project to be developed of the enterprise under the existing development scale and the existing personnel according to the development direction of the enterprise, determines the recruitment enterprise according to a judging result, and transmits the determined recruitment enterprise to the recruitment time period determining unit and the enterprise recruitment requirement predicting module;

the recruitment time period determining unit receives the recruitment enterprises transmitted by the recruitment enterprise determining unit, determines recruitment tasks of the recruitment enterprises according to the division conditions of existing personnel of the recruitment enterprises and the requirement conditions of to-be-developed projects of the enterprises for talents, determines recruitment starting time points of the recruitment enterprises according to the culture plans of the corresponding task talents and the starting time of to-be-developed projects of the enterprises, predicts the recruitment time periods of the recruitment enterprises in combination with the latest time of the corresponding talents in the to-be-developed projects of the recruitment enterprises, and transmits the predicted recruitment time periods to the enterprise recruitment requirement predicting module.

Further, the enterprise recruitment requirement prediction module comprises a recruitment position determination unit, a culture time determination unit and a recruitment requirement prediction unit;

the recruitment determining unit receives the recruitment transmitted by the recruitment enterprise determining unit, determines the recruitment of the recruitment enterprise according to the personnel change condition of the recruitment enterprise within the delay time, and transmits the determined recruitment to the cultivation time determining unit;

the recruitment request prediction unit is used for receiving the recruitment transmitted by the recruitment request determination unit, acquiring professional capacities of the recruitment under the action of the recruitment, determining the culture time corresponding to each professional capacity according to the acquired professional capacities, and transmitting the determined culture time to the recruitment request prediction unit;

the recruitment requirement prediction unit receives the incubation time transmitted by the incubation time determination unit and the recruitment time period transmitted by the recruitment time period determination unit, determines the incubation time which can be shortened in the incubation time corresponding to each professional capability according to the importance degree corresponding to each professional capability, determines the standard recruitment holding time allowed by the recruitment enterprise according to the shortened incubation time value, determines the basic recruitment requirement of the recruitment enterprise based on the determination result, calculates the actual recruitment holding time of the recruitment enterprise according to the received recruitment time period, calculates the difference value between the actual recruitment holding time corresponding to the recruitment enterprise and the standard recruitment holding time, predicts the recruitment requirement of the recruitment enterprise at each holding time point according to the calculation result, and transmits the predicted recruitment requirement to the talent intelligent matching module.

Further, the talent determination module comprises a completion time determination unit, a compression resistance prediction unit and a talent information screening unit;

the completion time determining unit receives talent information transmitted by the talent screening module, acquires professional ability and mastery degree mastered by each talent according to the received information, combines learning ability of each talent on each professional, determines time for each talent to complete a culture plan, and transmits the determined time for each talent to complete the culture plan to the talent information screening unit;

the compression resistance prediction unit receives talent information transmitted by the talent screening module, and based on the received information, the compression resistance prediction unit generates a mathematical model Y=max { [ (1-S) _p )/K _p ]/B _p Predicting the maximum compression resistance value born by each talent when learning expert knowledge, and transmitting the prediction result to a talent information screening unit;

the talent information screening unit acquires the time for completing the culture plan for each talent transmitted by the completion time determining unit, the talent information transmitted by the talent screening module and the maximum compression resistance value born by each talent transmitted by the compression resistance prediction unit when learning expert knowledge, performs secondary screening on the received talent information according to the acquired information, and transmits the time for completing the culture plan for the talents after the secondary screening to the talent intelligent matching module.

Further, the talent intelligent matching module comprises a recruitment talent map building unit, a talent map building unit and an intelligent matching unit;

the recruitment talent map construction unit receives recruitment requirements of the recruitment enterprises at each delayed time point, takes the recruitment requirements as deflection angles in unit time, takes a planning endpoint in a recruitment enterprise cultivation plan as a deflection angle change point, constructs recruitment talent maps of the recruitment enterprises, and transmits the constructed recruitment talent maps to the intelligent matching unit;

the talent map construction unit receives the time of the second-time screened talents transmitted by the talent information screening unit to finish the culture plan, determines the delay time of each talent according to the acquired information, constructs a talent map by taking the time point of the talents finishing the corresponding culture plan and the time point of the talents starting the culture plan as deflection angle change points and the degree of the talents conforming to recruitment requirements as deflection angles and takes unit time as unit length, and transmits the constructed talent map to the intelligent matching unit;

the intelligent matching unit degree receives the recruitment map transmitted by the recruitment map construction unit and the talent map transmitted by the talent map construction unit, acquires deflection angles corresponding to each recruitment requirement on the recruitment map according to recruitment delay time, acquires deflection angles corresponding to the degree that each talent meets the corresponding recruitment requirement on the talent map according to the recruitment delay time and the recruitment requirement corresponding to the recruitment delay time, determines talents according to the acquired deflection angles and average coincidence degree of the deflection angles corresponding to each delay time point, and transmits talent information matched with the determined talents to the enterprise recruitment end before the corresponding delay time point or the corresponding delay time point.

Compared with the prior art, the invention has the following beneficial effects:

1. according to the recruitment method and the recruitment system, recruitment time period of the recruitment enterprise and recruitment requirements of the recruitment enterprise at each delay time point are predicted through enterprise scale, development movement direction and incumbent personnel information of the recruitment enterprise, so that the recruitment enterprise can quickly find talents with higher matching degree when or before recruitment begins, and the intelligent matching effect of the system is further improved.

2. According to the invention, through the professional ability and the mastering degree mastered by each talent and the learning ability of each talent on various professions, the time for each talent to complete the culture plan is determined, and the talents are screened by combining the maximum compression resistance value born by each talent when learning the professional knowledge, so that the screened talents can meet the recruitment requirements of recruitment enterprises, and meanwhile, all professional ability can be mastered before the implementation of the development project of the recruitment enterprises, and the distribution precision of the system to the talents is further improved.

3. According to the recruitment request corresponding to the recruitment delay time and the recruitment delay time, the deflection angle corresponding to each recruitment request and the deflection angle corresponding to the degree that each recruiter meets the corresponding recruitment request are acquired on the constructed recruiter talent map and talent map, the talents with high adaptation degree to the recruitment enterprises are determined at the corresponding delay time points according to the acquired deflection angle and the average coincidence degree of the deflection angles corresponding to the delay time points, and in the process, continuous adjustment can be carried out on the talents with high adaptation degree to the recruitment enterprises according to the delay time, so that prospective recruitment plans are formulated for the recruitment enterprises in real time.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:

FIG. 1 is a schematic workflow diagram of a talent resource information intelligent matching system and method based on big data of the present invention;

fig. 2 is a schematic diagram of the working principle structure of the intelligent talent resource information matching system and method based on big data.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1 and 2, the present invention provides the following technical solutions: a talent resource information intelligent matching method based on big data comprises the following steps:

The first step comprises the following steps:

s13: determining recruitment delay time of the recruitment enterprise according to the recruitment time period of the recruitment enterprise predicted in the step S12, and predicting recruitment requirements of the recruitment enterprise at each delay time point by combining the change condition of personnel of the recruitment enterprise in the delay time, wherein the specific prediction method comprises the following steps:

wherein p=1, 2, …, q represents the number corresponding to the professional ability, the number sequence is the arrangement sequence of various types of responsibilities from small to large according to the importance degree, q represents the total number of types included by the professional ability, m represents the total number of types of responsibilities, j=0.1, 0.2, …, n represents the required degree corresponding to each professional ability, n=1, t represents the difference between the actual recruitment delay time and the standard recruitment delay time, and K _p ^j Representing the corresponding incubation time reduction time for professional ability numbered p at a desired level j, Q representing the hold-off time difference, when Q > 0, representing that the predicted recruitment requirement does not correspond to the actual recruitment requirement at the corresponding hold-off time point, when q=0, representingThe predicted recruitment requirement accords with the actual recruitment requirement at the corresponding delay time point, and when Q is less than 0, the predicted recruitment requirement can be continuously delayed for Q time within the corresponding delay time;

when Q is more than or equal to 0, acquiring the highest requirement degree corresponding to the professional ability with the number p, wherein the predicted recruitment requirement is the highest requirement degree corresponding to the professional ability with the number p;

the third step comprises:

wherein S is _p Represent the mastery degree of talents corresponding to the professional ability with the number p, K _p Representing the learning ability of talents to the professional ability with the number p, W represents the time of completion of the culture plan after the recruitment time is prolonged by u, u represents the actual recruitment time, if W > R-u, the talents cannot complete the culture plan after the recruitment time is prolonged by u, and if W is less than or equal to R-u, the talents can complete the culture plan after the recruitment time is prolonged by uThe cultivation plan can be completed, and R represents the recruitment duration corresponding to the recruitment enterprise determined in the step S12;

s32: according to the formula y=max { [ (1-S) _p )/K _p ]/B _p Predicting the maximum compression resistance value born by each talent when learning expert knowledge, wherein B _p Represents the standard time required for talents to fully grasp the professional ability numbered p, [ (1-S) _p )/K _p ]/B _p Indicating talents in (1-S) _p )/K _p The compressive capacity required to be born when the professional capacity with the number p is completely mastered in time, and Y represents the maximum compressive capacity value required to be born when the professional capacity required by recruitment enterprises is completely mastered in a culture plan;

if Y is greater than the maximum compression resistance of talents, the corresponding talent information needs to be continuously removed from the residual talent information removed in S31, otherwise, the corresponding talent information is reserved from the residual talent information removed in S31;

step four: based on the recruitment requirements of the recruitment enterprises corresponding to the recruitment time determined in the first step and the talent growth progress determined in the third step, constructing a talent map, intelligently matching talents according to the constructed talent map, and sending the matched talent information to the enterprise recruitment end;

The fourth step comprises:

s41: acquiring recruitment requirements of the recruitment enterprises determined in the step one at each delay time point, taking unit time as unit length, taking the recruitment requirements as deflection angles, taking a planning endpoint in the recruitment enterprise cultivation plan as a deflection angle change point to construct a recruiter talent map of the recruitment enterprises, wherein the default deflection angle is 0 degree in the standard delay time, and H _p ＝F _p * Pi, where F _p Represents the mastery degree required by recruiting enterprises on professional ability with the number p, H _p Recruitment requirement correspondence representing recruitment enterprises' professional ability numbered pThe plan endpoint refers to the point in time corresponding to the beginning of the cultivation plan (e.g., time point a, time point b are the plan endpoints when the cultivation time range of recruiter to professional ability numbered 2 is a-b, and the cultivation time range of recruiter to professional ability numbered 3 is b-c);

s42: acquiring time for reserving talents to finish a culture plan according to talent information reserved in S32, determining delay time of each talent according to the acquired information, constructing a talent map with time points for the talents to finish the corresponding culture plan (for example, a recruiter has a culture time range of 2 professional abilities and a recruiter has a culture time range of 3 professional abilities and b-c, the time points b and c are time points for the talents to finish the corresponding culture plan) and time points for the talents to start the culture plan as deflection angle change points, setting the degree of compliance of the talents with the recruitment requirement as deflection angles, setting a talent map with unit time as a unit length, setting a default deflection angle as 0 DEG within the standard delay time,

when the average coincidence is more than or equal to X, then the tableIndicating that the corresponding person can meet the recruitment requirement of the recruitment enterprise, otherwise, indicating that the corresponding person cannot meet the recruitment requirement of the recruitment enterprise, wherein X is more than 0.7 and less than or equal to 1.

The talent resource information intelligent matching system based on big data comprises an enterprise recruitment time period determining module, an enterprise recruitment requirement predicting module, a talent screening module, a talent determining module and a talent intelligent matching module;

The enterprise recruitment time period determining module comprises a recruitment enterprise determining unit and a recruitment time period determining unit;

the recruitment enterprise determining unit judges whether the enterprise can autonomously complete the project to be developed of the enterprise under the existing development scale and the existing personnel according to the development direction of the enterprise, determines the recruitment enterprise according to the judgment result, and transmits the determined recruitment enterprise to the recruitment time period determining unit and the enterprise recruitment requirement predicting module;

the recruitment time period determining unit receives the recruitment enterprises transmitted by the recruitment enterprise determining unit, determines recruitment tasks of the recruitment enterprises according to the division conditions of existing personnel of the recruitment enterprises and the requirement conditions of the enterprises to-be-developed projects for talents, determines recruitment starting time points of the recruitment enterprises according to the culture plans of the corresponding talents and the starting time of the enterprises to-be-developed projects by the recruitment enterprises, predicts recruitment time periods of the recruitment enterprises according to the latest recruitment time of the corresponding talents in the to-be-developed projects of the recruitment enterprises, and transmits the predicted recruitment time periods to the enterprise recruitment requirement prediction module;

the enterprise recruitment requirement prediction module is used for receiving recruitment enterprises and recruitment time periods corresponding to the recruitment enterprises, which are transmitted by the enterprise recruitment time period determination module, determining recruitment delay time of the recruitment enterprises according to the receiving information, predicting recruitment requirements of the recruitment enterprises at each delay time point by combining the change condition of personnel of the recruitment enterprises in the delay time, and transmitting the predicted recruitment requirements to the talent intelligent matching module;

The enterprise recruitment requirement prediction module comprises a recruitment position determination unit, a culture time determination unit and a recruitment requirement prediction unit;

the recruitment request prediction unit is used for receiving the recruitment transmitted by the recruitment request determination unit, acquiring professional capabilities of the recruitment under the action of the recruitment, determining culture time corresponding to each professional capability according to the acquired professional capabilities, and transmitting the determined culture time to the recruitment request prediction unit;

the recruitment requirement prediction unit receives the incubation time transmitted by the incubation time determination unit and the recruitment time period transmitted by the recruitment time period determination unit, determines the incubation time which can be shortened in the incubation time corresponding to each professional ability according to the importance degree corresponding to each professional ability, determines the standard recruitment holding time allowed by the recruitment enterprise according to the shortened incubation time value, determines the basic recruitment requirement of the recruitment enterprise based on the determination result, calculates the actual recruitment holding time of the recruitment enterprise according to the received recruitment time period, calculates the difference value between the actual recruitment holding time corresponding to the recruitment enterprise and the standard recruitment holding time, predicts the recruitment requirement of the recruitment enterprise at each holding time point according to the calculation result, and transmits the predicted recruitment requirement to the talent intelligent matching module;

The talent screening module is used for acquiring recruitment professional names corresponding to recruitment businesses according to recruitment businesses of the recruitment businesses, determining professional ability required to be mastered by the professions based on the acquired recruitment professional names, acquiring related professional names according to determination results, screening talent information in a talent library based on the acquired related professional names and the acquired recruitment professional names, and transmitting the screened talent information to the talent determining module;

the talent determining module is used for receiving talent information transmitted by the talent screening module, acquiring professional ability and mastery degree mastered by each talent according to the received information, determining time for each talent to complete the culture plan by combining learning ability corresponding to each talent, secondarily screening talent information by combining pressure conditions born by each talent when learning professional knowledge, and transmitting time for the talents after secondary screening to the talent intelligent matching module;

the talent determination module comprises a completion time determination unit, a compression resistance prediction unit and a talent information screening unit;

The compression resistance prediction unit receives talent information transmitted by the talent screening module, and based on the received information, the compression resistance prediction unit performs a process according to a mathematical model Y=max { [ (1-S) _p )/K _p ]/B _p Predicting the maximum compression resistance value born by each talent when learning expert knowledge, and transmitting the prediction result to a talent information screening unit;

the talent information screening unit acquires the time for each talent transmitted by the completion time determining unit to complete the culture plan, the talent information transmitted by the talent screening module and the maximum compression resistance value born by each talent transmitted by the compression resistance prediction unit when learning expert knowledge, performs secondary screening on the received talent information according to the acquired information, and transmits the time for the talents after secondary screening to complete the culture plan to the talent intelligent matching module;

the talent intelligent matching module receives the time of the second screened talents transmitted by the talent determining module for completing the culture plan and the recruitment requirements of the recruitment enterprises at each delay time point transmitted by the enterprise recruitment requirement predicting module, constructs a talent map according to the received information, carries out intelligent matching on talents according to the constructed talent map, and transmits the matched talent information to the enterprise recruitment end;

The talent intelligent matching module comprises a recruitment talent map building unit, a talent map building unit and an intelligent matching unit;

the recruitment talent map construction unit receives recruitment requirements of the recruitment enterprises at each delayed time point, takes the recruitment requirements as deflection angles in unit time, constructs recruitment talent maps of the recruitment enterprises by taking planning endpoints in a recruitment enterprise cultivation plan as deflection angle change points, and transmits the constructed recruitment talent maps to the intelligent matching unit;

the talent map construction unit receives the time of the second-time screened talents transmitted by the talent information screening unit to finish the culture plan, determines the delay time of each talent according to the acquired information, constructs a talent map by taking the time point of the talents finishing the corresponding culture plan and the time point of the talents starting the culture plan as deflection angle change points and the degree of the talents meeting recruitment requirements as deflection angles and taking unit time as unit length, and transmits the constructed talent map to the intelligent matching unit;

the intelligent matching unit degree receives the recruitment map transmitted by the recruitment map construction unit and the talent map transmitted by the talent map construction unit, acquires deflection angles corresponding to each recruitment requirement on the recruitment map according to recruitment delay time, acquires deflection angles corresponding to the degree that each talent meets the corresponding recruitment requirement on the talent map according to recruitment delay time and the recruitment requirement corresponding to the recruitment delay time, determines talents according to the acquired deflection angles and average coincidence degrees of the deflection angles corresponding to each delay time point, and transmits talent information matched with the determined talents to the enterprise recruitment end before the corresponding delay time point or the corresponding delay time point.

Embodiment 1:

setting the total number of specialized ability types corresponding to recruitment requirements of recruitment enterprises to be 3, enabling talents to grasp the specialized ability corresponding to the specialized ability with the serial number of 1, 2 and 3 to be 0.3, 0.5 and 0.8 respectively, enabling talents to grasp the learning ability of the specialized ability with the serial number of 1 to be 0.02 every day, enabling talents to grasp the learning ability of the specialized ability with the serial number of 2 to be 0.01 every day, enabling talents to grasp the learning ability of the specialized ability with the serial number of 3 to be 0.04 every day, and enabling corresponding talents to finish culture planning time to be:

i.e., the time for the corresponding talents to complete the culture plan is 90 days.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A talent resource information intelligent matching method based on big data is characterized in that: the method comprises the following steps:

2. The intelligent talent resource information matching method based on big data as claimed in claim 1, wherein the method comprises the following steps: the first step comprises the following steps:

3. The intelligent talent resource information matching method based on big data as claimed in claim 2, wherein the method comprises the following steps: the specific method for predicting the recruitment requirement of the recruitment enterprise at each delayed time point in the S13 is as follows:

4. The intelligent talent resource information matching method based on big data as claimed in claim 3, wherein the intelligent talent resource information matching method based on big data is characterized in that: the third step comprises the following steps:

5. The intelligent talent resource information matching method based on big data as claimed in claim 4, wherein the intelligent talent resource information matching method based on big data is characterized in that: the fourth step comprises the following steps:

s41: acquiring recruitment requirements of the recruitment enterprises determined in the step one at each delay time point, taking unit time as unit length, taking the recruitment requirements as deflection angles, taking a planning endpoint in the recruitment enterprise cultivation plan as a deflection angle change point to construct a recruiter talent map of the recruitment enterprises, wherein the default deflection angle is 0 degree in the standard delay time, and H _p ＝F _p * Pi, where F _p Represents the mastery degree required by recruiting enterprises on professional ability with the number p, H _p Representing a deflection angle corresponding to recruitment requirements of recruitment enterprises for professional capacities with the number p, wherein a planning endpoint refers to a time point corresponding to the start of a cultivation plan;

s42: acquiring time for reserving talents to finish a culture plan according to talent information reserved in S32, determining delay time of each talent according to the acquired information, constructing a talent map by taking time points of the talents to finish the corresponding culture plan and time points of the talents to start the culture plan as deflection angle change points and taking the degree of the talents meeting recruitment requirements as deflection angles and taking unit time as unit length, setting a default deflection angle to be 0 DEG in standard delay time,

s43: acquiring deflection angles corresponding to each recruitment requirement on a recruitment talent map according to the recruitment delay time, acquiring deflection angles corresponding to the degree that each talent meets the corresponding recruitment requirement on the talent map according to the recruitment delay time and the recruitment requirement corresponding to the recruitment delay time, determining talents according to the acquired deflection angles and average coincidence degrees of the deflection angles corresponding to each delay time point, and determining talent information matched with the determined talents on the corresponding drags The delayed time point or the corresponding delayed time point is sent to the recruitment end of the enterprise, wherein,

6. A big data based talent resource information intelligent matching system applied to the big data based talent resource information intelligent matching method of any one of claims 1-5, characterized in that: the system comprises an enterprise recruitment time period determining module, an enterprise recruitment requirement predicting module, a talent screening module, a talent determining module and a talent intelligent matching module;

7. The intelligent talent resource information matching system based on big data as claimed in claim 6, wherein: the enterprise recruitment time period determining module comprises a recruitment enterprise determining unit and a recruitment time period determining unit;

8. The intelligent talent resource information matching system based on big data as claimed in claim 7, wherein: the enterprise recruitment requirement prediction module comprises a recruitment position determination unit, a culture time determination unit and a recruitment requirement prediction unit;

9. The intelligent talent resource information matching system based on big data as claimed in claim 8, wherein: the talent determination module comprises a completion time determination unit, a compression resistance prediction unit and a talent information screening unit;

10. The intelligent talent resource information matching system based on big data as claimed in claim 9, wherein: the talent intelligent matching module comprises a recruitment talent map building unit, a talent map building unit and an intelligent matching unit;