CN116308227A - Innovative entrepreneur talent hatching data interaction method and system - Google Patents

Innovative entrepreneur talent hatching data interaction method and system Download PDF

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CN116308227A
CN116308227A CN202310593975.9A CN202310593975A CN116308227A CN 116308227 A CN116308227 A CN 116308227A CN 202310593975 A CN202310593975 A CN 202310593975A CN 116308227 A CN116308227 A CN 116308227A
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于喜巍
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Beijing Saixue Technology Co ltd
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Abstract

The invention discloses an innovation talent incubation data interaction method and system, belonging to the technical field of data processing, wherein the method comprises the following steps: receiving personal information of talents; under the condition that the personal information is real, recording the personal information in corresponding talent nodes, and decomposing the personal information into a plurality of personal data with different disclosure levels; evaluating talents; carrying out innovation and creation value evaluation on talents; receiving unit information of an enterprise; under the condition that the unit information is real, recording the unit information in a corresponding enterprise node, and decomposing the unit information into a plurality of enterprise data with different disclosure levels; issuing recruitment requirements of enterprises; screening candidate talents matched with recruitment requirements of enterprises according to the recruitment requirements of the enterprises and the resume of talents; selecting a preset number of target talents from the candidate talents according to the value evaluation results of the talents; and the data disclosure level between the enterprise node and the talent node which are matched with each other is improved to a higher level.

Description

Innovative entrepreneur talent hatching data interaction method and system
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an innovation talent hatching data interaction method and system.
Background
With the development of science and technology and the advancement of society, more and more people are put into the wave of innovation and creation. There are many innovative entrepreneur talent hatching projects at present, and two-way selection opportunities are provided for enterprises and talents. However, on one hand, the existing innovation talent hatching project is difficult to realize the identification of talents and the authenticity of enterprises, and recruitment cheating can not be avoided; on the other hand, the prior art cannot evaluate the innovation and creation capability of talents, and when enterprises need to recruit innovation and creation talents, the enterprises need to spend manpower to manually screen and discriminate the talents, so that the labor cost is high; moreover, the prior art often provides the enterprise with the largest public authority, and the enterprise can check personal information of all talents, so that information leakage is easy to occur.
Disclosure of Invention
The invention provides a method and a system for interaction of talents hatching data of innovative talents, which are used for solving the technical problems that in the prior art, authentication of talents and the authenticity of enterprises is difficult to realize, recruitment cheating can not be avoided, evaluation of the innovative talents can not be carried out, recruitment labor cost is high, enterprises can check personal information of all talents, and information leakage is easy to cause.
First aspect
The invention provides an innovation talent incubation data interaction method, which is applied to an innovation talent incubation data interaction system, wherein the innovation talent incubation data interaction system comprises talent nodes, enterprise nodes and verification nodes which are distributed and connected with each other, and the innovation talent incubation data interaction method comprises the following steps:
s101: receiving personal information of talents, and verifying whether the personal information is true or not through a verification node;
s102: under the condition that the personal information is real, recording the personal information in corresponding talent nodes, and decomposing the personal information into a plurality of personal data with different disclosure levels;
s103: evaluating talents;
s104: according to the evaluation result and the personal information, carrying out innovation and entrepreneur value evaluation on talents;
s105: receiving unit information of an enterprise, and verifying whether the unit information is real or not through a verification node;
s106: under the condition that the unit information is real, recording the unit information in a corresponding enterprise node, and decomposing the unit information into a plurality of enterprise data with different disclosure levels;
s107: issuing recruitment requirements of enterprises;
s108: screening candidate talents matched with recruitment requirements of enterprises according to the recruitment requirements of the enterprises and the resume of talents;
s109: selecting a preset number of target talents from the candidate talents according to the value evaluation results of the talents, and arranging the target talents in descending order according to the value evaluation results so as to complete matching of enterprises and the target talents;
s110: and the data disclosure level between the enterprise node and the talent node which are matched with each other is improved to a higher level.
Second aspect
The invention provides an innovation talent incubation data interaction system, which comprises talent nodes, enterprise nodes and verification nodes which are distributed and connected with each other, and further comprises:
the first receiving module is used for receiving personal information of talents and verifying whether the personal information is real or not through the verification node;
the first recording module is used for recording the personal information in the corresponding talent node under the condition that the personal information is real, and decomposing the personal information into a plurality of personal materials with different disclosure levels;
the evaluation module is used for evaluating talents;
the evaluation module is used for evaluating the innovation and startup value of talents according to the evaluation result and the personal information;
the second receiving module is used for receiving the unit information of the enterprise and verifying whether the unit information is real or not through the verification node;
the second recording module is used for recording the unit information in the corresponding enterprise nodes under the condition that the unit information is real, and decomposing the unit information into a plurality of enterprise materials with different disclosure levels;
the issuing module is used for issuing recruitment requirements of enterprises;
the screening module is used for screening candidate talents matched with the recruitment requirements of the enterprises according to the recruitment requirements of the enterprises and the talents' resume;
the selecting module is used for selecting a preset number of target talents from the candidate talents according to the value evaluation results of the talents, and arranging the target talents in descending order according to the value evaluation results so as to complete matching of enterprises and the target talents.
And the lifting module is used for lifting the data disclosure level between the enterprise node and the talent node which are matched with each other to a higher level.
Compared with the prior art, the invention has at least the following beneficial technical effects:
in the invention, the verification node is used for realizing the verification of the personal information of talents and the reality of the unit information of enterprises, so as to avoid recruitment cheating. The talent innovation and innovation capability can be evaluated, the talent resume and the recruitment requirement of the enterprise can be automatically matched, and the recruitment labor cost is reduced. Only in case of successful matching of talents and enterprises, the talents and enterprises can view higher-level information about each other, and information leakage is avoided.
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The above features, technical features, advantages and implementation of the present invention will be further described in the following description of preferred embodiments with reference to the accompanying drawings in a clear and easily understood manner.
FIG. 1 is a schematic flow chart of an innovative entrepreneur talent hatching data interaction method provided by the invention;
FIG. 2 is a schematic diagram of the structure of an innovative talent incubation data interaction system provided by the invention;
fig. 3 is a schematic structural diagram of another innovative talent incubation data interaction system provided by the invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
For simplicity of the drawing, only the parts relevant to the invention are schematically shown in each drawing, and they do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. Herein, "a" means not only "only this one" but also "more than one" case.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In this context, it should be noted that the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, unless explicitly stated or limited otherwise; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, in the description of the present invention, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
In one embodiment, referring to fig. 1 of the specification, the invention provides a flow diagram of an innovative talent incubation data interaction method. Referring to the attached figure 2 of the specification, the invention provides a structural schematic diagram of an innovative talent incubation data interaction system.
The innovation talent incubation data interaction method provided by the invention is applied to an innovation talent incubation data interaction system.
As shown in fig. 2, the innovation talent hatching data interaction system includes a talent node, an enterprise node, and a verification node that are distributed and connected to each other.
The talent node is used for storing personal information of talents, the enterprise node is used for storing unit information of enterprises, and the verification node is used for verifying the authenticity of the personal information of talents and the unit information of enterprises.
The innovation entrepreneur talent hatching data interaction method comprises the following steps:
s101: and receiving personal information of talents, and verifying whether the personal information is true or not through a verification node.
The personal information of talents may specifically be: personal information such as name, sex, identification number, age and academic school of talents.
Specifically, the verification node can be connected with the public security system to verify the authenticity of information such as name, gender, identity card number, age and the like, and can be connected with the credit system to verify the authenticity of the school.
S102: in the case that the personal information is authentic, the personal information is recorded in the corresponding talent node, and the personal information is decomposed into a plurality of personal materials of different disclosure levels.
Specifically, personal information can be decomposed into basic, medium, and high levels. The basic level information can be name, the middle level information can be gender, age and other information, and the high level information can be academic, identity card number and other information. It will be appreciated that all personal information of talents may be consulted when high-level rights are owned.
S103: talents were evaluated.
The specific evaluation content can comprise professional skill evaluation, character evaluation, innovation entrepreneur capability evaluation and the like. By evaluating talents, further understanding of talents can be achieved.
S104: and carrying out innovation and innovation value evaluation on talents according to the evaluation result and the personal information.
According to the invention, the talents can be automatically evaluated for innovation and creation values according to the evaluation result and the personal information, so that the innovation and creation values of the talents are mined, and references are provided for enterprises in recruitment.
In one possible implementation, S104 specifically includes sub-steps S1041 to S1047:
s1041: and constructing an evaluation index system of innovative entrepreneur talents.
Wherein, the first-level index in the evaluation index system comprises: professional basic capability index, practice capability index and comprehensive capability index.
The basic capability index comprises three secondary indexes, namely a professional capability index, a divergent thinking capability index and a logic analysis capability index.
The practice ability index comprises two secondary indexes, namely a manual ability index and a communication ability index.
The comprehensive capacity index comprises four secondary indexes, namely a compressive capacity index, a tissue capacity index, a coordination capacity index and an emergency treatment capacity index.
It is to be noted that, through the evaluation index system, accurate evaluation can be completely made on the innovation and creation capability of a talent.
S1042: constructing an initial matrix according to the evaluation result and the personal informationX：
Figure SMS_1
Wherein,,mthe number of the evaluation index is represented,nindicating the number of talents being evaluated.
It will be appreciated that the initial matrixXIn (a) and (b)
Figure SMS_2
Representing the firstnTalent of personal talentsmSpecific scoring of individual indicators.
S1043: from the initial matrixXDetermining the maximum value of the characteristic value of each evaluation index as a reference value, and forming a reference sequence
Figure SMS_3
：
Figure SMS_4
Wherein,,
Figure SMS_5
representing an initial matrixXIs the first of (2)mMaximum value in column.
S1044: calculating the number of talents to be evaluated
Figure SMS_6
And reference number column->
Figure SMS_7
Correlation coefficient betweenτ：
Figure SMS_8
Wherein,,ξrepresents the relevance factor, takes the value of 0.5,
Figure SMS_10
representing the number of talents being evaluated>
Figure SMS_12
And reference number column->
Figure SMS_15
Absolute difference on each evaluation index, +.>
Figure SMS_11
Representing the number of talents being evaluated>
Figure SMS_13
And reference number column->
Figure SMS_16
Minimum value of absolute difference on each evaluation index, +.>
Figure SMS_17
Representing the number of talents being evaluated>
Figure SMS_9
And reference number column->
Figure SMS_14
Maximum value of absolute difference on each evaluation index.
S1045: weight set of each evaluation index is calculated based on AHP analytic hierarchy processS * ：
Figure SMS_18
Wherein,,
Figure SMS_19
representing the weight of each evaluation index.
S1046: calculating a weighted relevance vectorR：
Figure SMS_20
S1047: by weighting association vectorsRAnd carrying out innovation and entrepreneur value evaluation on talents.
Specifically, in evaluating the professional basic capability index of a talent, only the weighted relevance vector needs to be examinedRThe system can objectively and accurately know whether the professional basic knowledge of the talents is solid or not, and intuitively know the level of the talents compared with other talents in the system.
S105: and receiving the unit information of the enterprise, and verifying whether the unit information is real or not through a verification node.
The unit information of the enterprise may specifically be: enterprise information such as enterprise name, corporate network, legal representatives, registry, and business.
In particular, the verification node may interface with an industrial and commercial system to verify the authenticity of information such as business names, networks, legal representatives, registries, businesses, and business scales.
In the invention, the verification node is used for realizing the verification of the personal information of talents and the reality of the unit information of enterprises, so as to avoid recruitment cheating.
S106: under the condition that the unit information is real, the unit information is recorded in the corresponding enterprise node, and the unit information is decomposed into a plurality of enterprise materials with different disclosure levels.
Specifically, the unit information can be equally decomposed into basic, intermediate, and advanced three stages. The basic level information can be enterprise names, the medium level information can be information of official networks, enterprise registries and the like, and the high level information can be information of legal representatives of enterprises, enterprise operation places, operation scales and the like. It will be appreciated that all of the corporate information may be consulted when having high-level rights.
S107: and issuing recruitment requirements of enterprises.
S108: and screening out candidate talents matched with the recruitment requirements of the enterprises according to the recruitment requirements of the enterprises and the resume of the talents.
According to the recruitment requirement of the enterprise and the talent resume, the talents can be automatically matched with the enterprise, and the labor cost of recruitment can be reduced.
In one possible implementation, S108 specifically includes substeps S1081 to S1082:
s1081: and respectively splitting the structured text and the unstructured text from the recruitment requirement of the enterprise and the talent resume.
Wherein the structured document comprises: sites, job positions, salaries, work properties, school, working years and professions.
S1082: calculating matching degree of structured text
Figure SMS_21
。
In one possible implementation, S1082 specifically includes:
calculating a degree of matching of a location
Figure SMS_22
Job matching degree->
Figure SMS_23
Payroll matching degree->
Figure SMS_24
Degree of matching of working properties
Figure SMS_25
Degree of academic match->
Figure SMS_26
Degree of working year matching->
Figure SMS_27
And professional matching degree->
Figure SMS_28
：
Figure SMS_29
。
According to the matching degree of the places
Figure SMS_30
Job matching degree->
Figure SMS_31
Payroll matching degree->
Figure SMS_32
Matching degree with working property
Figure SMS_33
Calculating talent preference matching degree +.>
Figure SMS_34
：
Figure SMS_35
Wherein,,
Figure SMS_36
representing the weights of the different structured texts.
According to the degree of matching of the academic
Figure SMS_37
Degree of working year matching->
Figure SMS_38
And professional matching degree->
Figure SMS_39
Calculating the matching degree of enterprise preference type ∈>
Figure SMS_40
：
Figure SMS_41
。
Calculating the matching degree of the structured text according to the talent preference type matching degree and the enterprise preference type matching degree
Figure SMS_43
：
Figure SMS_44
Wherein,,βrepresenting preference weight factors, 0.ltoreq.0.ltoreq.βNot more than 1, whenβ=0, the structured text matching degree is represented
Figure SMS_45
Completely favors talents whenβ=1, the degree of matching of the structured text is indicated +.>
Figure SMS_46
Is totally biased towards the enterprise.
S1083: calculating unstructured text matching degree
Figure SMS_47
。
In one possible implementation, S1083 specifically includes:
constructing an unstructured text matching model based on a cyclic neural network, wherein the unstructured text matching model comprises: an input layer, a state layer, an attention layer, a full connection layer, and a matching layer.
In the input layer, recruitment requirements of the enterprise and word vector sequences in talents' resume are input
Figure SMS_48
。
In the state layer, word vectors are computed attHidden state at time:
Figure SMS_49
wherein,,
Figure SMS_50
representing the previous state of the forward cycle, +.>
Figure SMS_51
Representing the previous state of the backward cycle, +.>
Figure SMS_52
Representation->
Figure SMS_53
Weight coefficient of>
Figure SMS_54
Representation->
Figure SMS_55
Weight coefficient of>
Figure SMS_56
Representation oftBias terms for time hidden states.
In the attention layer, each word vector is assigned a weightγAnd accumulating to obtain hidden state of current attention layer
Figure SMS_57
：
Figure SMS_58
。
Outputting the eigenvalue of the word vectorO：
Figure SMS_59
。
In the full connection layer, feature values of word vectors are aggregated.
In the matching layer, unstructured text matching degree is calculated through cosine similarity
Figure SMS_60
：
Figure SMS_61
Wherein,,Ja resume showing the talents of a person,Zrecruitment requirements of enterprises.
S1084: according to the matching degree of the structured text
Figure SMS_62
And unstructured text matching degree +.>
Figure SMS_63
Calculate the comprehensive matching degree +.>
Figure SMS_64
：
Figure SMS_65
Wherein,,
Figure SMS_66
representing structured text matching degree->
Figure SMS_67
Weight of->
Figure SMS_68
Representing unstructured text matching degree +.>
Figure SMS_69
Is a weight of (2).
Wherein, the person skilled in the art can determine the matching degree of the structured text according to the requirements of the actual scene
Figure SMS_70
And unstructured text matching degree +.>
Figure SMS_71
The relative importance degree between the two, adjusting the matching degree of the structured text +.>
Figure SMS_72
Weight of +.>
Figure SMS_73
And unstructured text matching degree +.>
Figure SMS_74
Weight of +.>
Figure SMS_75
Is a specific size of (c).
S1085: and screening out candidate talents matched with recruitment requirements of enterprises according to the comprehensive matching degree.
S109: according to the value evaluation result of talents, a preset number of target talents are selected from the candidate talents, and the target talents are arranged in descending order according to the value evaluation result, so that matching of enterprises and the target talents is completed.
The preset number may be 10, and the 10 target talents are arranged in descending order according to the value evaluation result, so as to be referred by the recruiter. For specific numerical values of the preset number, the person skilled in the art can set the numerical values according to actual conditions, and the invention is not limited.
S110: and the data disclosure level between the enterprise node and the talent node which are matched with each other is improved to a higher level.
It should be noted that, after the finally screened target talents are matched with the recruitment enterprises, in order to facilitate mutual understanding, the disclosure level of the data can be improved to a higher level, that is, the two parties can view more information. In the invention, only if the talents and enterprises are successfully matched, the talents and enterprises can view the information of higher level relative to each other, and information leakage is avoided.
Compared with the prior art, the invention has at least the following beneficial technical effects:
in the invention, the verification node is used for realizing the verification of the personal information of talents and the reality of the unit information of enterprises, so as to avoid recruitment cheating. The talent innovation and innovation capability can be evaluated, the talent resume and the recruitment requirement of the enterprise can be automatically matched, and the recruitment labor cost is reduced. Only in case of successful matching of talents and enterprises, the talents and enterprises can view higher-level information about each other, and information leakage is avoided.
Example 2
In one embodiment, referring to fig. 3 of the specification, the present invention provides a schematic structural diagram of another innovative talent incubation data interaction system.
The invention provides an innovative entrepreneur talent incubation data interaction system 30, which comprises: the distributed and interconnected talent nodes, enterprise nodes, and verification nodes, the innovation talent hatching data interaction system 30 further includes:
a first receiving module 301, configured to receive personal information of talents, and verify whether the personal information is real through a verification node;
a first recording module 302, configured to record personal information in a corresponding talent node and decompose the personal information into a plurality of personal materials with different disclosure levels, where the personal information is real;
an evaluation module 303, configured to evaluate talents;
the evaluation module 304 is used for evaluating the innovation and startup value of talents according to the evaluation result and the personal information;
a second receiving module 305, configured to receive the unit information of the enterprise, and verify whether the unit information is real through the verification node;
the second recording module 306 is configured to record the unit information in a corresponding enterprise node and decompose the unit information into a plurality of enterprise materials with different disclosure levels under the condition that the unit information is real;
a publishing module 307 for publishing recruitment requirements of the enterprise;
the screening module 308 is configured to screen candidate talents matched with the recruitment requirement of the enterprise according to the recruitment requirement of the enterprise and the resume of talents;
the selecting module 309 is configured to select a preset number of target talents from the candidate talents according to the value evaluation results of the talents, and arrange the target talents in descending order according to the value evaluation results, so as to complete matching between the enterprise and the target talents;
and a promotion module 310, configured to promote the disclosure level of the data between the enterprise node and the talent node that are matched to each other to a higher level.
In one possible implementation, the evaluation module 304 is specifically configured to:
constructing an evaluation index system of innovation startup talents, wherein the first-level index comprises: the system comprises a professional basic capability index, a practice capability index and a comprehensive capability index, wherein the basic capability index comprises three secondary indexes, namely a professional capability index, a divergent thinking capability index and a logic analysis capability index, the practice capability index comprises two secondary indexes, namely a manual capability index and a communication capability index, and the comprehensive capability index comprises four secondary indexes, namely a compression resistance capability index, a tissue capability index, a coordination capability index and an emergency processing capability index;
constructing an initial matrix according to the evaluation result and the personal informationX：
Figure SMS_76
Wherein,,mthe number of the evaluation index is represented,nrepresenting the number of talents being evaluated;
from the initial matrixXDetermining the maximum value of the characteristic value of each evaluation index as a reference value, and forming a reference sequence
Figure SMS_77
：
Figure SMS_78
Wherein,,
Figure SMS_79
representing an initial matrixXIs the first of (2)mMaximum value in column;
calculating the number of talents to be evaluated
Figure SMS_80
And reference number column->
Figure SMS_81
Correlation coefficient betweenτ：
Figure SMS_82
Wherein,,ξrepresents the relevance factor, takes the value of 0.5,
Figure SMS_85
representing the number of talents being evaluated>
Figure SMS_88
And reference number column->
Figure SMS_90
Absolute difference on each evaluation index, +.>
Figure SMS_84
Representing the number of talents being evaluated>
Figure SMS_86
And reference number column->
Figure SMS_89
Minimum value of absolute difference on each evaluation index, +.>
Figure SMS_91
Representing the number of talents being evaluated>
Figure SMS_83
And reference number column->
Figure SMS_87
A maximum value of absolute difference values at each evaluation index;
weight set of each evaluation index is calculated based on AHP analytic hierarchy processS * ：
Figure SMS_92
Wherein,,s j weights representing the respective evaluation indexes;
calculating a weighted relevance vectorR：
Figure SMS_93
By weighting association vectorsRAnd carrying out innovation and entrepreneur value evaluation on talents.
In one possible implementation, the screening module 308 is specifically configured to:
and respectively splitting a structured text and an unstructured text from recruitment requirements of enterprises and talents, wherein the structured text comprises: places, positions, salaries, working properties, school, working years and professions;
calculating matching degree of structured text
Figure SMS_94
；
Calculating unstructured text matching degree
Figure SMS_95
；
According to the matching degree of the structured text
Figure SMS_96
And unstructured text matching degree +.>
Figure SMS_97
Calculate the comprehensive matching degree +.>
Figure SMS_98
：
Figure SMS_99
Wherein,,
Figure SMS_100
representing structured text matching degree->
Figure SMS_101
Weight of->
Figure SMS_102
Representing unstructured text matching degree +.>
Figure SMS_103
Weights of (2);
and screening out candidate talents matched with recruitment requirements of enterprises according to the comprehensive matching degree.
In one possible implementation, the screening module 308 is specifically configured to:
calculating a degree of matching of a location
Figure SMS_104
Job matching degree->
Figure SMS_105
Payroll matching degree->
Figure SMS_106
Degree of matching of working properties
Figure SMS_107
Degree of academic match->
Figure SMS_108
Degree of working year matching->
Figure SMS_109
And special purposeIndustry match degree->
Figure SMS_110
：
Figure SMS_111
；
According to the matching degree of the places
Figure SMS_112
Job matching degree->
Figure SMS_113
Payroll matching degree->
Figure SMS_114
Matching degree with working property
Figure SMS_115
Calculating talent preference matching degree +.>
Figure SMS_116
：
Figure SMS_117
Wherein,,
Figure SMS_118
weights representing different structured texts;
according to the degree of matching of the academic
Figure SMS_119
Degree of working year matching->
Figure SMS_120
And professional matching degree->
Figure SMS_121
Calculating the matching degree of enterprise preference type ∈>
Figure SMS_122
：
Figure SMS_123
；
Calculating the matching degree of the structured text according to the talent preference type matching degree and the enterprise preference type matching degree
Figure SMS_125
：
Figure SMS_126
Wherein,,βrepresenting preference weight factors, 0.ltoreq.0.ltoreq.βNot more than 1, whenβ=0, the structured text matching degree is represented
Figure SMS_127
Completely favors talents whenβ=1, the degree of matching of the structured text is indicated +.>
Figure SMS_128
Is totally biased towards the enterprise.
In one possible implementation, the screening module 308 is specifically configured to:
constructing an unstructured text matching model based on a cyclic neural network, wherein the unstructured text matching model comprises: an input layer, a state layer, an attention layer, a full connection layer and a matching layer;
in the input layer, recruitment requirements of the enterprise and word vector sequences in talents' resume are input
Figure SMS_129
；
In the state layer, word vectors are computed attHidden state at time:
Figure SMS_130
wherein,,
Figure SMS_131
representing the previous state of the forward cycle, +.>
Figure SMS_132
Representing the previous state of the backward cycle, +.>
Figure SMS_133
Representation->
Figure SMS_134
Weight coefficient of>
Figure SMS_135
Representation->
Figure SMS_136
Weight coefficient of>
Figure SMS_137
Representation oftBias terms for time hidden states.
In the attention layer, each word vector is assigned a weightγAnd accumulating to obtain hidden state of current attention layer
Figure SMS_138
：
Figure SMS_139
；
Outputting the eigenvalue of the word vectorO：
Figure SMS_140
；
Converging the characteristic values of the word vectors in the full connection layer;
in the matching layer, unstructured text matching degree is calculated through cosine similarity
Figure SMS_141
：
Figure SMS_142
Wherein,,Ja resume showing the talents of a person,Zrecruitment requirements of enterprises.
The innovative talent incubation data interaction system 30 provided by the present invention can implement each process implemented in the above method embodiments, and in order to avoid repetition, a detailed description is omitted here.
The virtual system provided by the invention can be a system, and can also be a component, an integrated circuit or a chip in a terminal.
Compared with the prior art, the invention has at least the following beneficial technical effects:
in the invention, the verification node is used for realizing the verification of the personal information of talents and the reality of the unit information of enterprises, so as to avoid recruitment cheating. The talent innovation and innovation capability can be evaluated, the talent resume and the recruitment requirement of the enterprise can be automatically matched, and the recruitment labor cost is reduced. Only in case of successful matching of talents and enterprises, the talents and enterprises can view higher-level information about each other, and information leakage is avoided.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.

Claims (10)

1. The innovation talent incubation data interaction method is characterized by being applied to an innovation talent incubation data interaction system, wherein the innovation talent incubation data interaction system comprises talent nodes, enterprise nodes and verification nodes which are distributed and connected with each other, and the innovation talent incubation data interaction method comprises the following steps:
s101: receiving personal information of talents, and verifying whether the personal information is real or not through the verification node;
s102: recording the personal information in the corresponding talent node under the condition that the personal information is real, and decomposing the personal information into a plurality of personal materials with different disclosure levels;
s103: evaluating the talents;
s104: according to the evaluation result and the personal information, carrying out innovation and entrepreneur value evaluation on the talents;
s105: receiving unit information of an enterprise, and verifying whether the unit information is real or not through the verification node;
s106: under the condition that the unit information is real, recording the unit information in the corresponding enterprise node, and decomposing the unit information into a plurality of enterprise materials with different disclosure levels;
s107: issuing recruitment requirements of the enterprise;
s108: screening candidate talents matched with the recruitment requirements of the enterprises according to the recruitment requirements of the enterprises and the resume of the talents;
s109: selecting a preset number of target talents from the candidate talents according to the value evaluation results of the talents, and arranging the target talents in descending order according to the value evaluation results so as to complete the matching of the enterprise and the target talents;
s110: and the data disclosure level between the enterprise node and the talent node which are matched with each other is improved to a higher level.
2. The innovative entrepreneur talent hatching data interaction method according to claim 1, wherein S104 specifically comprises:
s1041: constructing an evaluation index system of innovation startup talents, wherein the first-level index comprises: the system comprises a professional basic capability index, a practice capability index and a comprehensive capability index, wherein the basic capability index comprises three secondary indexes, namely a professional capability index, a divergent thinking capability index and a logic analysis capability index, the practice capability index comprises two secondary indexes, namely a manual capability index and a communication capability index, and the comprehensive capability index comprises four secondary indexes, namely a compression resistance capability index, a tissue capability index, a coordination capability index and an emergency processing capability index;
s1042: constructing an initial matrix according to the evaluation result and the personal informationX：
Figure QLYQS_1
Wherein,,mthe number of the evaluation index is represented,nrepresenting the number of talents being evaluated;
s1043: from the initial matrixXDetermining the maximum value of the characteristic value of each evaluation index as a reference value, and forming a reference sequence
Figure QLYQS_2
：
Figure QLYQS_3
Wherein,,
Figure QLYQS_4
representing the initial matrixXIs the first of (2)mMaximum value in column;
s1044: calculating the number of talents to be evaluated
Figure QLYQS_5
And the reference number sequence->
Figure QLYQS_6
Correlation coefficient betweenτ：
Figure QLYQS_7
Wherein,,ξrepresents the relevance factor, takes the value of 0.5,
Figure QLYQS_9
representing the number of the evaluated talents +.>
Figure QLYQS_12
And the reference number series
Figure QLYQS_15
Absolute difference on each evaluation index, +.>
Figure QLYQS_8
Representing the number of the evaluated talents +.>
Figure QLYQS_11
And the reference number sequence->
Figure QLYQS_14
Minimum value of absolute difference on each evaluation index, +.>
Figure QLYQS_16
Representing the number of the evaluated talents +.>
Figure QLYQS_10
And the reference number sequence->
Figure QLYQS_13
A maximum value of absolute difference values at each evaluation index;
s1045: weight set of each evaluation index is calculated based on AHP analytic hierarchy processS * ：
Figure QLYQS_17
Wherein,,
Figure QLYQS_18
weights representing the respective evaluation indexes;
s1046: calculating a weighted relevance vectorR：
Figure QLYQS_19
S1047: by the weighted association vectorRAnd carrying out innovation and entrepreneur value evaluation on the talents.
3. The innovative entrepreneur talent hatching data interaction method according to claim 1, wherein S108 specifically comprises:
s1081: and respectively splitting a structured text and an unstructured text from recruitment requirements of the enterprise and the talent resume, wherein the structured text comprises: places, positions, salaries, working properties, school, working years and professions;
s1082: calculating matching degree of structured text
Figure QLYQS_20
；
S1083: calculating unstructured text matching degree
Figure QLYQS_21
；
S1084: according to the structured text matching degree
Figure QLYQS_22
And said unstructured text matching degree +.>
Figure QLYQS_23
Calculate the comprehensive matching degree +.>
Figure QLYQS_24
：/>
Figure QLYQS_25
Wherein,,
Figure QLYQS_26
representing the degree of matching of the structured text +.>
Figure QLYQS_27
Weight of->
Figure QLYQS_28
Representing the unstructured text matching degree
Figure QLYQS_29
Weights of (2);
s1085: and screening out the candidate talents matched with recruitment requirements of the enterprises according to the comprehensive matching degree.
4. The innovative entrepreneur talent hatching data interaction method according to claim 3, wherein said S1082 specifically comprises:
calculating a degree of matching of a location
Figure QLYQS_30
Job matching degree->
Figure QLYQS_31
Payroll matching degree->
Figure QLYQS_32
Degree of working property matching->
Figure QLYQS_33
Degree of academic match->
Figure QLYQS_34
Degree of working year matching->
Figure QLYQS_35
And professional matching degree->
Figure QLYQS_36
：
Figure QLYQS_37
；
According to the matching degree of the places
Figure QLYQS_38
The job matching degree ∈>
Figure QLYQS_39
Said salary match->
Figure QLYQS_40
And said work property matching degree->
Figure QLYQS_41
Calculating talent preference matching degree +.>
Figure QLYQS_42
：
Figure QLYQS_43
Wherein,,
Figure QLYQS_44
weights representing different structured texts;
according to the degree of matching of the academy
Figure QLYQS_45
The said operational years matching degree ∈>
Figure QLYQS_46
And said professional matching degree->
Figure QLYQS_47
Calculating the matching degree of enterprise preference type ∈>
Figure QLYQS_48
：
Figure QLYQS_49
；
Calculating the matching degree of the structured text according to the talent preference type matching degree and the enterprise preference type matching degree
Figure QLYQS_50
：
Figure QLYQS_51
Wherein,,βrepresenting preference weight factors, 0.ltoreq.0.ltoreq.βNot more than 1, whenβ=0, the structured text matching degree is represented
Figure QLYQS_52
Completely bias the talents, whenβ=1, representing the degree of matching of the structured text +.>
Figure QLYQS_53
Being totally biased towards the enterprise.
5. The innovative entrepreneur talent hatching data interaction method according to claim 3, wherein said S1083 specifically comprises:
building an unstructured text matching model based on a recurrent neural network, wherein the unstructured text matching model comprises: an input layer, a state layer, an attention layer, a full connection layer and a matching layer;
in the input layer, inputting recruitment requirements of the enterprise and word vector sequences in the resume of talents
Figure QLYQS_54
；
In the state layer, word vectors are calculated intHidden state at time:
Figure QLYQS_55
wherein,,
Figure QLYQS_56
representing the previous state of the forward cycle, +.>
Figure QLYQS_57
Representing the previous state of the backward cycle, +.>
Figure QLYQS_58
Representation->
Figure QLYQS_59
Weight coefficient of>
Figure QLYQS_60
Representation->
Figure QLYQS_61
Weight coefficient of>
Figure QLYQS_62
Representation oftBias items of the hidden state at the moment;
in the attention layer, a weight is assigned to each of the word vectorsγAnd accumulating to obtain the hidden state of the current attention layer
Figure QLYQS_63
：
Figure QLYQS_64
；
Outputting the eigenvalue of the word vectorO：
Figure QLYQS_65
；
Converging the characteristic values of the word vectors in the full connection layer;
in the matching layer, calculating the unstructured text matching degree through cosine similarity
Figure QLYQS_66
：
Figure QLYQS_67
Wherein,,Ja resume representing the talent of the person,Zrecruitment requirements of the enterprise.
6. The innovation talent incubation data interaction system is characterized by comprising talent nodes, enterprise nodes and verification nodes which are distributed and connected with each other, and further comprises:
the first receiving module is used for receiving personal information of talents and verifying whether the personal information is real or not through the verification node;
the first recording module is used for recording the personal information in the corresponding talent node and decomposing the personal information into a plurality of personal materials with different disclosure levels under the condition that the personal information is real;
the evaluation module is used for evaluating the talents;
the evaluation module is used for evaluating innovation and startup values of the talents according to the evaluation result and the personal information;
the second receiving module is used for receiving the unit information of the enterprise and verifying whether the unit information is real or not through the verification node;
the second recording module is used for recording the unit information in the corresponding enterprise nodes under the condition that the unit information is real, and decomposing the unit information into a plurality of enterprise materials with different disclosure levels;
the publishing module is used for publishing recruitment requirements of the enterprises;
the screening module is used for screening candidate talents matched with the recruitment requirements of the enterprises according to the recruitment requirements of the enterprises and the talents' resume;
the selecting module is used for selecting a preset number of target talents from the candidate talents according to the value evaluation results of the talents, and arranging the target talents in descending order according to the value evaluation results so as to complete the matching of the enterprises and the target talents;
and the lifting module is used for lifting the data disclosure level between the enterprise node and the talent node which are matched with each other to a higher level.
7. The innovation talent hatching data interaction system of claim 6, wherein the evaluation module is specifically configured to:
constructing an evaluation index system of innovation startup talents, wherein the first-level index comprises: the system comprises a professional basic capability index, a practice capability index and a comprehensive capability index, wherein the basic capability index comprises three secondary indexes, namely a professional capability index, a divergent thinking capability index and a logic analysis capability index, the practice capability index comprises two secondary indexes, namely a manual capability index and a communication capability index, and the comprehensive capability index comprises four secondary indexes, namely a compression resistance capability index, a tissue capability index, a coordination capability index and an emergency processing capability index;
constructing an initial matrix according to the evaluation result and the personal informationX：
Figure QLYQS_68
Wherein,,mthe number of the evaluation index is represented,nrepresenting the number of talents being evaluated;
from the initial matrixXDetermining the maximum value of the characteristic value of each evaluation index as a reference value, and forming a reference sequence
Figure QLYQS_69
：
Figure QLYQS_70
Wherein,,
Figure QLYQS_71
representing the initial matrixXIs the first of (2)mMaximum value in column;
calculating the number of talents to be evaluatedX i And the reference number seriesX 0 Correlation coefficient betweenτ：
Figure QLYQS_72
Wherein,,ξrepresents the relevance factor, takes the value of 0.5,
Figure QLYQS_73
representing the talents of the person to be evaluatedX i And the reference number seriesX 0 Absolute difference on each evaluation index, +.>
Figure QLYQS_74
Representing the talents of the person to be evaluatedX i And the reference number seriesX 0 Minimum value of absolute difference on each evaluation index, +.>
Figure QLYQS_75
Representing the talents of the person to be evaluatedX i And the reference number seriesX 0 A maximum value of absolute difference values at each evaluation index;
weight set of each evaluation index is calculated based on AHP analytic hierarchy processS * ：
Figure QLYQS_76
Wherein,,
Figure QLYQS_77
weights representing the respective evaluation indexes;
calculating a weighted relevance vectorR：
Figure QLYQS_78
By the weighted association vectorRAnd carrying out innovation and entrepreneur value evaluation on the talents.
8. The innovation talent hatching data interaction system of claim 6, wherein the screening module is specifically configured to:
and respectively splitting a structured text and an unstructured text from recruitment requirements of the enterprise and the talent resume, wherein the structured text comprises: places, positions, salaries, working properties, school, working years and professions;
calculating matching degree of structured text
Figure QLYQS_79
；
Calculating unstructured text matching degree
Figure QLYQS_80
；
According to the structured text matching degree
Figure QLYQS_81
And said unstructured text matching degree +.>
Figure QLYQS_82
Calculating the comprehensive matching degree
Figure QLYQS_83
：
Figure QLYQS_84
Wherein,,
Figure QLYQS_85
representing the degree of matching of the structured text +.>
Figure QLYQS_86
Weight of->
Figure QLYQS_87
Representing the unstructured text matching degree
Figure QLYQS_88
Weights of (2);
and screening out the candidate talents matched with recruitment requirements of the enterprises according to the comprehensive matching degree.
9. The innovation talent hatching data interaction system of claim 8, wherein the screening module is specifically configured to:
calculating a degree of matching of a location
Figure QLYQS_89
Job matching degree->
Figure QLYQS_90
Payroll matching degree->
Figure QLYQS_91
Degree of working property matching->
Figure QLYQS_92
Degree of academic match->
Figure QLYQS_93
Degree of working year matching->
Figure QLYQS_94
And professional matching degree->
Figure QLYQS_95
：
Figure QLYQS_96
；
According to the matching degree of the places
Figure QLYQS_97
The job matching degree ∈>
Figure QLYQS_98
Said salary match->
Figure QLYQS_99
And said work property matching degree->
Figure QLYQS_100
Calculating talent preference matching degree +.>
Figure QLYQS_101
：
Figure QLYQS_102
Wherein,,
Figure QLYQS_103
weights representing different structured texts;
according to the degree of matching of the academy
Figure QLYQS_104
The said operational years matching degree ∈>
Figure QLYQS_105
And said professional matching degree->
Figure QLYQS_106
Calculating the matching degree of enterprise preference type ∈>
Figure QLYQS_107
：
Figure QLYQS_108
；
Calculating the matching degree of the structured text according to the talent preference type matching degree and the enterprise preference type matching degree
Figure QLYQS_109
：
Figure QLYQS_110
Wherein,,βrepresenting preference weight factors, 0.ltoreq.0.ltoreq.βNot more than 1, whenβ=0, the structured text matching degree is represented
Figure QLYQS_111
Completely bias the talents, whenβ=1, representing the degree of matching of the structured text +.>
Figure QLYQS_112
Being totally biased towards the enterprise.
10. The innovation talent hatching data interaction system of claim 8, wherein the screening module is specifically configured to:
building an unstructured text matching model based on a recurrent neural network, wherein the unstructured text matching model comprises: an input layer, a state layer, an attention layer, a full connection layer and a matching layer;
in the input layer, inputting recruitment requirements of the enterprise and word vector sequences in the resume of talents
Figure QLYQS_113
；
In the state layer, word vectors are calculated intHidden state at time:
Figure QLYQS_114
wherein,,
Figure QLYQS_115
representing the previous state of the forward cycle, +.>
Figure QLYQS_116
Representing the previous state of the backward cycle, +.>
Figure QLYQS_117
Representation->
Figure QLYQS_118
Weight coefficient of>
Figure QLYQS_119
Representation->
Figure QLYQS_120
Weight coefficient of>
Figure QLYQS_121
Representation oftBias items of the hidden state at the moment;
in the attention layer, a weight is assigned to each of the word vectorsγAnd accumulating to obtain the hidden state of the current attention layer
Figure QLYQS_122
：
Figure QLYQS_123
；
Outputting the eigenvalue of the word vectorO：
Figure QLYQS_124
；
Converging the characteristic values of the word vectors in the full connection layer;
in the matching layer, calculating the unstructured text matching degree through cosine similarity
Figure QLYQS_125
：
Figure QLYQS_126
Wherein,,Ja resume representing the talent of the person,Zrecruitment requirements of the enterprise.
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CN117217719A (en) * 2023-11-07 2023-12-12 湖南海润天恒科技集团有限公司 Talent information recruitment data intelligent management method and system based on big data
CN117312397A (en) * 2023-10-18 2023-12-29 广东倍智人才科技股份有限公司 Talent supply chain management method and system based on big data

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CN112686560A (en) * 2021-01-06 2021-04-20 罗兰 One-stop innovative entrepreneurship incubation platform
CN114881441A (en) * 2022-04-26 2022-08-09 许昌学院 One-stop college innovation entrepreneur talent incubation platform

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CN112686560A (en) * 2021-01-06 2021-04-20 罗兰 One-stop innovative entrepreneurship incubation platform
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116720792A (en) * 2023-08-10 2023-09-08 北京正开科技有限公司 Incubator public management service system based on data processing
CN117312397A (en) * 2023-10-18 2023-12-29 广东倍智人才科技股份有限公司 Talent supply chain management method and system based on big data
CN117312397B (en) * 2023-10-18 2024-03-22 广东倍智人才科技股份有限公司 Talent supply chain management method and system based on big data
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